{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "wE2M30LfCCdQ" }, "source": [ "## Image Captioning\n", "- Generating Captions for Images" ] }, { "cell_type": "markdown", "metadata": { "id": "Suh1-mOPCCdS" }, "source": [ "### Steps\n", "- Data collection\n", "- Understanding the data\n", "- Data Cleaning\n", "- Loading the training set\n", "- Data Preprocessing — Images\n", "- Data Preprocessing — Captions\n", "- Data Preparation using Generator Function\n", "- Word Embeddings\n", "- Model Architecture\n", "- Inference" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "1sfyBpcjCNEK", "outputId": "eb016033-f34c-407a-e598-3687aabde9f2" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Warning: Looks like you're using an outdated `kagglehub` version (installed: 0.3.7), please consider upgrading to the latest version (0.3.8).\n", "Downloading from https://www.kaggle.com/api/v1/datasets/download/sayanf/flickr8k?dataset_version_number=5...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1.04G/1.04G [00:14<00:00, 75.5MB/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Extracting files...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Path to dataset files: /root/.cache/kagglehub/datasets/sayanf/flickr8k/versions/5\n" ] } ], "source": [ "import kagglehub\n", "# Download latest version\n", "path = kagglehub.dataset_download(\"sayanf/flickr8k\")\n", "\n", "print(\"Path to dataset files:\", path)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "73wlL9i6CV1c", "outputId": "b49b3e8b-b964-4055-a450-26dcba3f0ec1" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mv: cannot move '/root/.cache/kagglehub/datasets/sayanf/flickr8k/versions/5/' to '/content/5': Directory not empty\n" ] } ], "source": [ "!mv /root/.cache/kagglehub/datasets/sayanf/flickr8k/versions/5/ /content/" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Oqv2Y1CNCCdU" }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import keras\n", "import re\n", "import nltk\n", "from nltk.corpus import stopwords\n", "import string\n", "import json\n", "from time import time\n", "import pickle\n", "from keras.applications.vgg16 import VGG16\n", "from keras.applications.resnet50 import ResNet50, preprocess_input, decode_predictions\n", "from keras.preprocessing import image\n", "from keras.models import Model, load_model\n", "from keras.preprocessing.sequence import pad_sequences\n", "from keras.utils import to_categorical\n", "from keras.layers import Input, Dense, Dropout, Embedding, LSTM,add" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "fZoWNd5QCCdW" }, "outputs": [], "source": [ "# Read Text Captions\n", "\n", "def readTextFile(path):\n", " with open(path) as f:\n", " captions = f.read()\n", " return captions\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "NW6zsQJGCgtv", "outputId": "bfbf2240-dcfa-429a-d888-f81c1b1d1a65" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5\t\t glove.6B.100d.txt glove.6B.300d.txt glove.6B.zip\tsaved\n", "descriptions_1.txt glove.6B.200d.txt glove.6B.50d.txt sample_data\n" ] } ], "source": [ "!ls" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "6n0_JVDECCdX" }, "outputs": [], "source": [ "captions = readTextFile(\"/content/5/Flickr8k_text/Flickr8k.token.txt\")\n", "captions = captions.split('\\n')[:-1]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "DA91P3JHCCdY", "outputId": "f4d6d0c6-a8b2-4fdb-8702-96d74fc09a4d" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "40460\n" ] } ], "source": [ "print(len(captions))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "gWfxXQF8CCdZ", "outputId": "4029803f-d23a-46ee-fa66-5ae70df3b9ce" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1000268201_693b08cb0e\n", "A child in a pink dress is climbing up a set of stairs in an entry way .\n" ] } ], "source": [ "first,second = captions[0].split('\\t')\n", "print(first.split(\".\")[0])\n", "print(second)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Kirj8IBVCCda" }, "outputs": [], "source": [ "# Dictionary to Map each Image with the list of captions it has" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "tgdWi3QwCCdb" }, "outputs": [], "source": [ "descriptions = {}\n", "\n", "for x in captions:\n", " first,second = x.split('\\t')\n", " img_name = first.split(\".\")[0]\n", "\n", " #if the image id is already present or not\n", " if descriptions.get(img_name) is None:\n", " descriptions[img_name] = []\n", "\n", " descriptions[img_name].append(second)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "NjDOmp1bCCdb", "outputId": "f6033be4-d655-458a-d63c-9a7b7581b698" }, "outputs": [ { "data": { "text/plain": [ "['A child in a pink dress is climbing up a set of stairs in an entry way .',\n", " 'A girl going into a wooden building .',\n", " 'A little girl climbing into a wooden playhouse .',\n", " 'A little girl climbing the stairs to her playhouse .',\n", " 'A little girl in a pink dress going into a wooden cabin .']" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "descriptions[\"1000268201_693b08cb0e\"]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "vnVC9uf-9O1-" }, "outputs": [], "source": [ "import cv2" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 406 }, "id": "NKbRjvx6CCdc", "outputId": "2e6291ad-b411-4cf2-dd1e-6488c537b815" }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "IMG_PATH = \"/content/5/Flickr8k_Dataset/\" # Update with your actual image path\n", "\n", "img = cv2.imread(IMG_PATH + \"1000268201_693b08cb0e.jpg\")\n", "\n", "# Check if the image was loaded successfully\n", "if img is None:\n", " print(f\"Error: Could not load image from {IMG_PATH + '1000268201_693b08cb0e.jpg'}\")\n", "else:\n", " img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n", " plt.imshow(img)\n", " plt.axis(\"off\")\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "gJET2KJVCCdc" }, "source": [ "### Data Cleaning\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "hQH2HzTxCCdc" }, "outputs": [], "source": [ "\n", "def clean_text(sentence):\n", " sentence = sentence.lower()\n", " sentence = re.sub(\"[^a-z]+\",\" \",sentence)\n", " sentence = sentence.split()\n", "\n", " sentence = [s for s in sentence if len(s)>1]\n", " sentence = \" \".join(sentence)\n", " return sentence\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 35 }, "id": "N6WFzYYlCCdd", "outputId": "92ce453d-e1e6-4ce9-ffae-d7d71d9a7388" }, "outputs": [ { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'cat is sitting over the house'" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clean_text(\"A cat is sitting over the house # 64\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "bvtrPKVqCCdd" }, "outputs": [], "source": [ "# Clean all Captions\n", "for key,caption_list in descriptions.items():\n", " for i in range(len(caption_list)):\n", " caption_list[i] = clean_text(caption_list[i])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "yCiTzevTCCdd", "outputId": "a89e5692-43b5-4de9-9a5c-876910245f28" }, "outputs": [ { "data": { "text/plain": [ "['child in pink dress is climbing up set of stairs in an entry way',\n", " 'girl going into wooden building',\n", " 'little girl climbing into wooden playhouse',\n", " 'little girl climbing the stairs to her playhouse',\n", " 'little girl in pink dress going into wooden cabin']" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "descriptions[\"1000268201_693b08cb0e\"]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "A_RheWhrCCdd" }, "outputs": [], "source": [ "# Write the data to text file\n", "with open(\"descriptions_1.txt\",\"w\") as f:\n", " f.write(str(descriptions))" ] }, { "cell_type": "markdown", "metadata": { "id": "TOvK-6-5CCde" }, "source": [ "### Vocabulary" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "6rqZRPsjCCde" }, "outputs": [], "source": [ "descriptions = None\n", "with open(\"descriptions_1.txt\",'r') as f:\n", " descriptions= f.read()\n", "\n", "json_acceptable_string = descriptions.replace(\"'\",\"\\\"\")\n", "descriptions = json.loads(json_acceptable_string)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "fjGLQ1KuCCde", "outputId": "0502fc27-f393-4c0f-c7f6-bb0dc845b207" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print(type(descriptions))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "8FxZW0rfCCde", "outputId": "0ac69843-15bd-46eb-a9cf-84251fd34a16" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Vocab Size : 8424\n" ] } ], "source": [ "# Vocab\n", "\n", "vocab = set()\n", "for key in descriptions.keys():\n", " [vocab.update(sentence.split()) for sentence in descriptions[key]]\n", "\n", "print(\"Vocab Size : %d\"% len(vocab))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "wrqgjkExCCdf", "outputId": "a3035a17-0c6d-40cc-9023-986e3bf6183c" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total Words 373837\n" ] } ], "source": [ "# Total No of words across all the sentences\n", "total_words = []\n", "\n", "for key in descriptions.keys():\n", " [total_words.append(i) for des in descriptions[key] for i in des.split()]\n", "\n", "print(\"Total Words %d\"%len(total_words))\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "bFR9wx7UCCdf" }, "outputs": [], "source": [ "# Filter Words from the Vocab according to certain threshold frequncy" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "n1D75sWOCCdf", "outputId": "c681958b-9f0f-45ca-8800-f9ffa8752111" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "8424\n" ] } ], "source": [ "import collections\n", "\n", "counter = collections.Counter(total_words)\n", "freq_cnt = dict(counter)\n", "print(len(freq_cnt.keys()))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "_NIxtUsVCCdg" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "qBcCB0HcCCdg" }, "outputs": [], "source": [ "# Sort this dictionary according to the freq count\n", "sorted_freq_cnt = sorted(freq_cnt.items(),reverse=True,key=lambda x:x[1])\n", "\n", "# Filter\n", "threshold = 10\n", "sorted_freq_cnt = [x for x in sorted_freq_cnt if x[1]>threshold]\n", "total_words = [x[0] for x in sorted_freq_cnt]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "jb7a3gqBCCdh", "outputId": "6e609dab-d982-4fd1-e497-20629fddc915" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1845\n" ] } ], "source": [ "print(len(total_words))" ] }, { "cell_type": "markdown", "metadata": { "id": "twB03ssVCCdh" }, "source": [ "### Prepare Train/Test Data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "mZhrVpkdCCdh" }, "outputs": [], "source": [ "train_file_data = readTextFile(\"/content/5/Flickr8k_text/Flickr_8k.trainImages.txt\")\n", "test_file_data = readTextFile(\"/content/5/Flickr8k_text/Flickr_8k.testImages.txt\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "041u_PbYCCdh" }, "outputs": [], "source": [ "train = [row.split(\".\")[0] for row in train_file_data.split(\"\\n\")[:-1]]\n", "test = [row.split(\".\")[0] for row in test_file_data.split(\"\\n\")[:-1]]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "owiOf102CCdh", "outputId": "86c55ed1-be21-41b5-de47-7a50ae08330f" }, "outputs": [ { "data": { "text/plain": [ "['2513260012_03d33305cf',\n", " '2903617548_d3e38d7f88',\n", " '3338291921_fe7ae0c8f8',\n", " '488416045_1c6d903fe0',\n", " '2644326817_8f45080b87']" ] }, "execution_count": 95, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train[:5]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "vDSPh7WWCCdi" }, "outputs": [], "source": [ "# Prepare Description for the Training Data\n", "# Tweak - Add and token to our training data\n", "train_descriptions = {}\n", "\n", "for img_id in train:\n", " train_descriptions[img_id] = []\n", " for cap in descriptions[img_id]:\n", " cap_to_append = \"startseq \" + cap + \" endseq\"\n", " train_descriptions[img_id].append(cap_to_append)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "MgPe-fhRCCdi", "outputId": "eaed8e1d-7ee0-4161-9ed3-1a85c483aadc" }, "outputs": [ { "data": { "text/plain": [ "['startseq child in pink dress is climbing up set of stairs in an entry way endseq',\n", " 'startseq girl going into wooden building endseq',\n", " 'startseq little girl climbing into wooden playhouse endseq',\n", " 'startseq little girl climbing the stairs to her playhouse endseq',\n", " 'startseq little girl in pink dress going into wooden cabin endseq']" ] }, "execution_count": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_descriptions[\"1000268201_693b08cb0e\"]" ] }, { "cell_type": "markdown", "metadata": { "id": "idkM6xb5CCdj" }, "source": [ "### Transfer Learning\n", "- Images --> Features\n", "- Text ---> Features" ] }, { "cell_type": "markdown", "metadata": { "id": "AZSSfMxCCCdk" }, "source": [ "### Step - 1 Image Feature Extraction" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "xOwUcyb1CCdk", "outputId": "10bfd1b1-9131-48aa-80c5-3124b7449de1" }, "outputs": [ { "data": { "text/html": [ "
Model: \"resnet50\"\n",
              "
\n" ], "text/plain": [ "\u001b[1mModel: \"resnet50\"\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
              "┃ Layer (type)               Output Shape                   Param #  Connected to           ┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
              "│ input_layer_3             │ (None, 224, 224, 3)    │              0 │ -                      │\n",
              "│ (InputLayer)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv1_pad (ZeroPadding2D) │ (None, 230, 230, 3)    │              0 │ input_layer_3[0][0]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv1_conv (Conv2D)       │ (None, 112, 112, 64)   │          9,472 │ conv1_pad[0][0]        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv1_bn                  │ (None, 112, 112, 64)   │            256 │ conv1_conv[0][0]       │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv1_relu (Activation)   │ (None, 112, 112, 64)   │              0 │ conv1_bn[0][0]         │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ pool1_pad (ZeroPadding2D) │ (None, 114, 114, 64)   │              0 │ conv1_relu[0][0]       │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ pool1_pool (MaxPooling2D) │ (None, 56, 56, 64)     │              0 │ pool1_pad[0][0]        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2_block1_1_conv       │ (None, 56, 56, 64)     │          4,160 │ pool1_pool[0][0]       │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2_block1_1_bn         │ (None, 56, 56, 64)     │            256 │ conv2_block1_1_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2_block1_1_relu       │ (None, 56, 56, 64)     │              0 │ conv2_block1_1_bn[0][ │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2_block1_2_conv       │ (None, 56, 56, 64)     │         36,928 │ conv2_block1_1_relu[0… │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2_block1_2_bn         │ (None, 56, 56, 64)     │            256 │ conv2_block1_2_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2_block1_2_relu       │ (None, 56, 56, 64)     │              0 │ conv2_block1_2_bn[0][ │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2_block1_0_conv       │ (None, 56, 56, 256)    │         16,640 │ pool1_pool[0][0]       │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2_block1_3_conv       │ (None, 56, 56, 256)    │         16,640 │ conv2_block1_2_relu[0… │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2_block1_0_bn         │ (None, 56, 56, 256)    │          1,024 │ conv2_block1_0_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2_block1_3_bn         │ (None, 56, 56, 256)    │          1,024 │ conv2_block1_3_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2_block1_add (Add)    │ (None, 56, 56, 256)    │              0 │ conv2_block1_0_bn[0][ │\n",
              "│                           │                        │                │ conv2_block1_3_bn[0][ │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2_block1_out          │ (None, 56, 56, 256)    │              0 │ conv2_block1_add[0][0] │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2_block2_1_conv       │ (None, 56, 56, 64)     │         16,448 │ conv2_block1_out[0][0] │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2_block2_1_bn         │ (None, 56, 56, 64)     │            256 │ conv2_block2_1_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2_block2_1_relu       │ (None, 56, 56, 64)     │              0 │ conv2_block2_1_bn[0][ │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2_block2_2_conv       │ (None, 56, 56, 64)     │         36,928 │ conv2_block2_1_relu[0… │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2_block2_2_bn         │ (None, 56, 56, 64)     │            256 │ conv2_block2_2_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2_block2_2_relu       │ (None, 56, 56, 64)     │              0 │ conv2_block2_2_bn[0][ │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2_block2_3_conv       │ (None, 56, 56, 256)    │         16,640 │ conv2_block2_2_relu[0… │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2_block2_3_bn         │ (None, 56, 56, 256)    │          1,024 │ conv2_block2_3_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2_block2_add (Add)    │ (None, 56, 56, 256)    │              0 │ conv2_block1_out[0][0… │\n",
              "│                           │                        │                │ conv2_block2_3_bn[0][ │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2_block2_out          │ (None, 56, 56, 256)    │              0 │ conv2_block2_add[0][0] │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2_block3_1_conv       │ (None, 56, 56, 64)     │         16,448 │ conv2_block2_out[0][0] │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2_block3_1_bn         │ (None, 56, 56, 64)     │            256 │ conv2_block3_1_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2_block3_1_relu       │ (None, 56, 56, 64)     │              0 │ conv2_block3_1_bn[0][ │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2_block3_2_conv       │ (None, 56, 56, 64)     │         36,928 │ conv2_block3_1_relu[0… │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2_block3_2_bn         │ (None, 56, 56, 64)     │            256 │ conv2_block3_2_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2_block3_2_relu       │ (None, 56, 56, 64)     │              0 │ conv2_block3_2_bn[0][ │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2_block3_3_conv       │ (None, 56, 56, 256)    │         16,640 │ conv2_block3_2_relu[0… │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2_block3_3_bn         │ (None, 56, 56, 256)    │          1,024 │ conv2_block3_3_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2_block3_add (Add)    │ (None, 56, 56, 256)    │              0 │ conv2_block2_out[0][0… │\n",
              "│                           │                        │                │ conv2_block3_3_bn[0][ │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv2_block3_out          │ (None, 56, 56, 256)    │              0 │ conv2_block3_add[0][0] │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block1_1_conv       │ (None, 28, 28, 128)    │         32,896 │ conv2_block3_out[0][0] │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block1_1_bn         │ (None, 28, 28, 128)    │            512 │ conv3_block1_1_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block1_1_relu       │ (None, 28, 28, 128)    │              0 │ conv3_block1_1_bn[0][ │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block1_2_conv       │ (None, 28, 28, 128)    │        147,584 │ conv3_block1_1_relu[0… │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block1_2_bn         │ (None, 28, 28, 128)    │            512 │ conv3_block1_2_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block1_2_relu       │ (None, 28, 28, 128)    │              0 │ conv3_block1_2_bn[0][ │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block1_0_conv       │ (None, 28, 28, 512)    │        131,584 │ conv2_block3_out[0][0] │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block1_3_conv       │ (None, 28, 28, 512)    │         66,048 │ conv3_block1_2_relu[0… │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block1_0_bn         │ (None, 28, 28, 512)    │          2,048 │ conv3_block1_0_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block1_3_bn         │ (None, 28, 28, 512)    │          2,048 │ conv3_block1_3_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block1_add (Add)    │ (None, 28, 28, 512)    │              0 │ conv3_block1_0_bn[0][ │\n",
              "│                           │                        │                │ conv3_block1_3_bn[0][ │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block1_out          │ (None, 28, 28, 512)    │              0 │ conv3_block1_add[0][0] │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block2_1_conv       │ (None, 28, 28, 128)    │         65,664 │ conv3_block1_out[0][0] │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block2_1_bn         │ (None, 28, 28, 128)    │            512 │ conv3_block2_1_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block2_1_relu       │ (None, 28, 28, 128)    │              0 │ conv3_block2_1_bn[0][ │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block2_2_conv       │ (None, 28, 28, 128)    │        147,584 │ conv3_block2_1_relu[0… │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block2_2_bn         │ (None, 28, 28, 128)    │            512 │ conv3_block2_2_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block2_2_relu       │ (None, 28, 28, 128)    │              0 │ conv3_block2_2_bn[0][ │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block2_3_conv       │ (None, 28, 28, 512)    │         66,048 │ conv3_block2_2_relu[0… │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block2_3_bn         │ (None, 28, 28, 512)    │          2,048 │ conv3_block2_3_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block2_add (Add)    │ (None, 28, 28, 512)    │              0 │ conv3_block1_out[0][0… │\n",
              "│                           │                        │                │ conv3_block2_3_bn[0][ │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block2_out          │ (None, 28, 28, 512)    │              0 │ conv3_block2_add[0][0] │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block3_1_conv       │ (None, 28, 28, 128)    │         65,664 │ conv3_block2_out[0][0] │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block3_1_bn         │ (None, 28, 28, 128)    │            512 │ conv3_block3_1_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block3_1_relu       │ (None, 28, 28, 128)    │              0 │ conv3_block3_1_bn[0][ │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block3_2_conv       │ (None, 28, 28, 128)    │        147,584 │ conv3_block3_1_relu[0… │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block3_2_bn         │ (None, 28, 28, 128)    │            512 │ conv3_block3_2_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block3_2_relu       │ (None, 28, 28, 128)    │              0 │ conv3_block3_2_bn[0][ │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block3_3_conv       │ (None, 28, 28, 512)    │         66,048 │ conv3_block3_2_relu[0… │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block3_3_bn         │ (None, 28, 28, 512)    │          2,048 │ conv3_block3_3_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block3_add (Add)    │ (None, 28, 28, 512)    │              0 │ conv3_block2_out[0][0… │\n",
              "│                           │                        │                │ conv3_block3_3_bn[0][ │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block3_out          │ (None, 28, 28, 512)    │              0 │ conv3_block3_add[0][0] │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block4_1_conv       │ (None, 28, 28, 128)    │         65,664 │ conv3_block3_out[0][0] │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block4_1_bn         │ (None, 28, 28, 128)    │            512 │ conv3_block4_1_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block4_1_relu       │ (None, 28, 28, 128)    │              0 │ conv3_block4_1_bn[0][ │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block4_2_conv       │ (None, 28, 28, 128)    │        147,584 │ conv3_block4_1_relu[0… │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block4_2_bn         │ (None, 28, 28, 128)    │            512 │ conv3_block4_2_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block4_2_relu       │ (None, 28, 28, 128)    │              0 │ conv3_block4_2_bn[0][ │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block4_3_conv       │ (None, 28, 28, 512)    │         66,048 │ conv3_block4_2_relu[0… │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block4_3_bn         │ (None, 28, 28, 512)    │          2,048 │ conv3_block4_3_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block4_add (Add)    │ (None, 28, 28, 512)    │              0 │ conv3_block3_out[0][0… │\n",
              "│                           │                        │                │ conv3_block4_3_bn[0][ │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3_block4_out          │ (None, 28, 28, 512)    │              0 │ conv3_block4_add[0][0] │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block1_1_conv       │ (None, 14, 14, 256)    │        131,328 │ conv3_block4_out[0][0] │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block1_1_bn         │ (None, 14, 14, 256)    │          1,024 │ conv4_block1_1_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block1_1_relu       │ (None, 14, 14, 256)    │              0 │ conv4_block1_1_bn[0][ │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block1_2_conv       │ (None, 14, 14, 256)    │        590,080 │ conv4_block1_1_relu[0… │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block1_2_bn         │ (None, 14, 14, 256)    │          1,024 │ conv4_block1_2_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block1_2_relu       │ (None, 14, 14, 256)    │              0 │ conv4_block1_2_bn[0][ │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block1_0_conv       │ (None, 14, 14, 1024)   │        525,312 │ conv3_block4_out[0][0] │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block1_3_conv       │ (None, 14, 14, 1024)   │        263,168 │ conv4_block1_2_relu[0… │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block1_0_bn         │ (None, 14, 14, 1024)   │          4,096 │ conv4_block1_0_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block1_3_bn         │ (None, 14, 14, 1024)   │          4,096 │ conv4_block1_3_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block1_add (Add)    │ (None, 14, 14, 1024)   │              0 │ conv4_block1_0_bn[0][ │\n",
              "│                           │                        │                │ conv4_block1_3_bn[0][ │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block1_out          │ (None, 14, 14, 1024)   │              0 │ conv4_block1_add[0][0] │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block2_1_conv       │ (None, 14, 14, 256)    │        262,400 │ conv4_block1_out[0][0] │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block2_1_bn         │ (None, 14, 14, 256)    │          1,024 │ conv4_block2_1_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block2_1_relu       │ (None, 14, 14, 256)    │              0 │ conv4_block2_1_bn[0][ │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block2_2_conv       │ (None, 14, 14, 256)    │        590,080 │ conv4_block2_1_relu[0… │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block2_2_bn         │ (None, 14, 14, 256)    │          1,024 │ conv4_block2_2_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block2_2_relu       │ (None, 14, 14, 256)    │              0 │ conv4_block2_2_bn[0][ │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block2_3_conv       │ (None, 14, 14, 1024)   │        263,168 │ conv4_block2_2_relu[0… │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block2_3_bn         │ (None, 14, 14, 1024)   │          4,096 │ conv4_block2_3_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block2_add (Add)    │ (None, 14, 14, 1024)   │              0 │ conv4_block1_out[0][0… │\n",
              "│                           │                        │                │ conv4_block2_3_bn[0][ │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block2_out          │ (None, 14, 14, 1024)   │              0 │ conv4_block2_add[0][0] │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block3_1_conv       │ (None, 14, 14, 256)    │        262,400 │ conv4_block2_out[0][0] │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block3_1_bn         │ (None, 14, 14, 256)    │          1,024 │ conv4_block3_1_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block3_1_relu       │ (None, 14, 14, 256)    │              0 │ conv4_block3_1_bn[0][ │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block3_2_conv       │ (None, 14, 14, 256)    │        590,080 │ conv4_block3_1_relu[0… │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block3_2_bn         │ (None, 14, 14, 256)    │          1,024 │ conv4_block3_2_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block3_2_relu       │ (None, 14, 14, 256)    │              0 │ conv4_block3_2_bn[0][ │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block3_3_conv       │ (None, 14, 14, 1024)   │        263,168 │ conv4_block3_2_relu[0… │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block3_3_bn         │ (None, 14, 14, 1024)   │          4,096 │ conv4_block3_3_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block3_add (Add)    │ (None, 14, 14, 1024)   │              0 │ conv4_block2_out[0][0… │\n",
              "│                           │                        │                │ conv4_block3_3_bn[0][ │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block3_out          │ (None, 14, 14, 1024)   │              0 │ conv4_block3_add[0][0] │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block4_1_conv       │ (None, 14, 14, 256)    │        262,400 │ conv4_block3_out[0][0] │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block4_1_bn         │ (None, 14, 14, 256)    │          1,024 │ conv4_block4_1_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block4_1_relu       │ (None, 14, 14, 256)    │              0 │ conv4_block4_1_bn[0][ │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block4_2_conv       │ (None, 14, 14, 256)    │        590,080 │ conv4_block4_1_relu[0… │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block4_2_bn         │ (None, 14, 14, 256)    │          1,024 │ conv4_block4_2_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block4_2_relu       │ (None, 14, 14, 256)    │              0 │ conv4_block4_2_bn[0][ │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block4_3_conv       │ (None, 14, 14, 1024)   │        263,168 │ conv4_block4_2_relu[0… │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block4_3_bn         │ (None, 14, 14, 1024)   │          4,096 │ conv4_block4_3_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block4_add (Add)    │ (None, 14, 14, 1024)   │              0 │ conv4_block3_out[0][0… │\n",
              "│                           │                        │                │ conv4_block4_3_bn[0][ │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block4_out          │ (None, 14, 14, 1024)   │              0 │ conv4_block4_add[0][0] │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block5_1_conv       │ (None, 14, 14, 256)    │        262,400 │ conv4_block4_out[0][0] │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block5_1_bn         │ (None, 14, 14, 256)    │          1,024 │ conv4_block5_1_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block5_1_relu       │ (None, 14, 14, 256)    │              0 │ conv4_block5_1_bn[0][ │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block5_2_conv       │ (None, 14, 14, 256)    │        590,080 │ conv4_block5_1_relu[0… │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block5_2_bn         │ (None, 14, 14, 256)    │          1,024 │ conv4_block5_2_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block5_2_relu       │ (None, 14, 14, 256)    │              0 │ conv4_block5_2_bn[0][ │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block5_3_conv       │ (None, 14, 14, 1024)   │        263,168 │ conv4_block5_2_relu[0… │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block5_3_bn         │ (None, 14, 14, 1024)   │          4,096 │ conv4_block5_3_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block5_add (Add)    │ (None, 14, 14, 1024)   │              0 │ conv4_block4_out[0][0… │\n",
              "│                           │                        │                │ conv4_block5_3_bn[0][ │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block5_out          │ (None, 14, 14, 1024)   │              0 │ conv4_block5_add[0][0] │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block6_1_conv       │ (None, 14, 14, 256)    │        262,400 │ conv4_block5_out[0][0] │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block6_1_bn         │ (None, 14, 14, 256)    │          1,024 │ conv4_block6_1_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block6_1_relu       │ (None, 14, 14, 256)    │              0 │ conv4_block6_1_bn[0][ │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block6_2_conv       │ (None, 14, 14, 256)    │        590,080 │ conv4_block6_1_relu[0… │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block6_2_bn         │ (None, 14, 14, 256)    │          1,024 │ conv4_block6_2_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block6_2_relu       │ (None, 14, 14, 256)    │              0 │ conv4_block6_2_bn[0][ │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block6_3_conv       │ (None, 14, 14, 1024)   │        263,168 │ conv4_block6_2_relu[0… │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block6_3_bn         │ (None, 14, 14, 1024)   │          4,096 │ conv4_block6_3_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block6_add (Add)    │ (None, 14, 14, 1024)   │              0 │ conv4_block5_out[0][0… │\n",
              "│                           │                        │                │ conv4_block6_3_bn[0][ │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv4_block6_out          │ (None, 14, 14, 1024)   │              0 │ conv4_block6_add[0][0] │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv5_block1_1_conv       │ (None, 7, 7, 512)      │        524,800 │ conv4_block6_out[0][0] │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv5_block1_1_bn         │ (None, 7, 7, 512)      │          2,048 │ conv5_block1_1_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv5_block1_1_relu       │ (None, 7, 7, 512)      │              0 │ conv5_block1_1_bn[0][ │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv5_block1_2_conv       │ (None, 7, 7, 512)      │      2,359,808 │ conv5_block1_1_relu[0… │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv5_block1_2_bn         │ (None, 7, 7, 512)      │          2,048 │ conv5_block1_2_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv5_block1_2_relu       │ (None, 7, 7, 512)      │              0 │ conv5_block1_2_bn[0][ │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv5_block1_0_conv       │ (None, 7, 7, 2048)     │      2,099,200 │ conv4_block6_out[0][0] │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv5_block1_3_conv       │ (None, 7, 7, 2048)     │      1,050,624 │ conv5_block1_2_relu[0… │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv5_block1_0_bn         │ (None, 7, 7, 2048)     │          8,192 │ conv5_block1_0_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv5_block1_3_bn         │ (None, 7, 7, 2048)     │          8,192 │ conv5_block1_3_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv5_block1_add (Add)    │ (None, 7, 7, 2048)     │              0 │ conv5_block1_0_bn[0][ │\n",
              "│                           │                        │                │ conv5_block1_3_bn[0][ │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv5_block1_out          │ (None, 7, 7, 2048)     │              0 │ conv5_block1_add[0][0] │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv5_block2_1_conv       │ (None, 7, 7, 512)      │      1,049,088 │ conv5_block1_out[0][0] │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv5_block2_1_bn         │ (None, 7, 7, 512)      │          2,048 │ conv5_block2_1_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv5_block2_1_relu       │ (None, 7, 7, 512)      │              0 │ conv5_block2_1_bn[0][ │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv5_block2_2_conv       │ (None, 7, 7, 512)      │      2,359,808 │ conv5_block2_1_relu[0… │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv5_block2_2_bn         │ (None, 7, 7, 512)      │          2,048 │ conv5_block2_2_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv5_block2_2_relu       │ (None, 7, 7, 512)      │              0 │ conv5_block2_2_bn[0][ │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv5_block2_3_conv       │ (None, 7, 7, 2048)     │      1,050,624 │ conv5_block2_2_relu[0… │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv5_block2_3_bn         │ (None, 7, 7, 2048)     │          8,192 │ conv5_block2_3_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv5_block2_add (Add)    │ (None, 7, 7, 2048)     │              0 │ conv5_block1_out[0][0… │\n",
              "│                           │                        │                │ conv5_block2_3_bn[0][ │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv5_block2_out          │ (None, 7, 7, 2048)     │              0 │ conv5_block2_add[0][0] │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv5_block3_1_conv       │ (None, 7, 7, 512)      │      1,049,088 │ conv5_block2_out[0][0] │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv5_block3_1_bn         │ (None, 7, 7, 512)      │          2,048 │ conv5_block3_1_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv5_block3_1_relu       │ (None, 7, 7, 512)      │              0 │ conv5_block3_1_bn[0][ │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv5_block3_2_conv       │ (None, 7, 7, 512)      │      2,359,808 │ conv5_block3_1_relu[0… │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv5_block3_2_bn         │ (None, 7, 7, 512)      │          2,048 │ conv5_block3_2_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv5_block3_2_relu       │ (None, 7, 7, 512)      │              0 │ conv5_block3_2_bn[0][ │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv5_block3_3_conv       │ (None, 7, 7, 2048)     │      1,050,624 │ conv5_block3_2_relu[0… │\n",
              "│ (Conv2D)                  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv5_block3_3_bn         │ (None, 7, 7, 2048)     │          8,192 │ conv5_block3_3_conv[0… │\n",
              "│ (BatchNormalization)      │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv5_block3_add (Add)    │ (None, 7, 7, 2048)     │              0 │ conv5_block2_out[0][0… │\n",
              "│                           │                        │                │ conv5_block3_3_bn[0][ │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv5_block3_out          │ (None, 7, 7, 2048)     │              0 │ conv5_block3_add[0][0] │\n",
              "│ (Activation)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ avg_pool                  │ (None, 2048)           │              0 │ conv5_block3_out[0][0] │\n",
              "│ (GlobalAveragePooling2D)  │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ predictions (Dense)       │ (None, 1000)           │      2,049,000 │ avg_pool[0][0]         │\n",
              "└───────────────────────────┴────────────────────────┴────────────────┴────────────────────────┘\n",
              "
\n" ], "text/plain": [ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓\n", "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to \u001b[0m\u001b[1m \u001b[0m┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩\n", "│ input_layer_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m3\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", "│ (\u001b[38;5;33mInputLayer\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv1_pad (\u001b[38;5;33mZeroPadding2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m230\u001b[0m, \u001b[38;5;34m230\u001b[0m, \u001b[38;5;34m3\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ input_layer_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv1_conv (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m9,472\u001b[0m │ conv1_pad[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │ conv1_conv[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv1_relu (\u001b[38;5;33mActivation\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv1_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ pool1_pad (\u001b[38;5;33mZeroPadding2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m114\u001b[0m, \u001b[38;5;34m114\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv1_relu[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ pool1_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ pool1_pad[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv2_block1_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m4,160\u001b[0m │ pool1_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv2_block1_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │ conv2_block1_1_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv2_block1_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv2_block1_1_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv2_block1_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m36,928\u001b[0m │ conv2_block1_1_relu[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv2_block1_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │ conv2_block1_2_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv2_block1_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv2_block1_2_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv2_block1_0_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m16,640\u001b[0m │ pool1_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv2_block1_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m16,640\u001b[0m │ conv2_block1_2_relu[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv2_block1_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │ conv2_block1_0_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv2_block1_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │ conv2_block1_3_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv2_block1_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv2_block1_0_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ │ │ │ conv2_block1_3_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv2_block1_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv2_block1_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv2_block2_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m16,448\u001b[0m │ conv2_block1_out[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv2_block2_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │ conv2_block2_1_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv2_block2_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv2_block2_1_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv2_block2_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m36,928\u001b[0m │ conv2_block2_1_relu[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv2_block2_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │ conv2_block2_2_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv2_block2_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv2_block2_2_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv2_block2_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m16,640\u001b[0m │ conv2_block2_2_relu[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv2_block2_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │ conv2_block2_3_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv2_block2_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv2_block1_out[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n", "│ │ │ │ conv2_block2_3_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv2_block2_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv2_block2_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv2_block3_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m16,448\u001b[0m │ conv2_block2_out[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv2_block3_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │ conv2_block3_1_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv2_block3_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv2_block3_1_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv2_block3_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m36,928\u001b[0m │ conv2_block3_1_relu[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv2_block3_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │ conv2_block3_2_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv2_block3_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv2_block3_2_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv2_block3_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m16,640\u001b[0m │ conv2_block3_2_relu[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv2_block3_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │ conv2_block3_3_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv2_block3_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv2_block2_out[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n", "│ │ │ │ conv2_block3_3_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv2_block3_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv2_block3_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block1_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m32,896\u001b[0m │ conv2_block3_out[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block1_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │ conv3_block1_1_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block1_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv3_block1_1_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block1_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m147,584\u001b[0m │ conv3_block1_1_relu[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block1_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │ conv3_block1_2_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block1_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv3_block1_2_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block1_0_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m131,584\u001b[0m │ conv2_block3_out[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block1_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m66,048\u001b[0m │ conv3_block1_2_relu[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block1_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ conv3_block1_0_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block1_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ conv3_block1_3_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block1_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv3_block1_0_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ │ │ │ conv3_block1_3_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block1_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv3_block1_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block2_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m65,664\u001b[0m │ conv3_block1_out[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block2_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │ conv3_block2_1_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block2_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv3_block2_1_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block2_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m147,584\u001b[0m │ conv3_block2_1_relu[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block2_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │ conv3_block2_2_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block2_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv3_block2_2_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block2_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m66,048\u001b[0m │ conv3_block2_2_relu[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block2_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ conv3_block2_3_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block2_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv3_block1_out[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n", "│ │ │ │ conv3_block2_3_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block2_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv3_block2_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block3_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m65,664\u001b[0m │ conv3_block2_out[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block3_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │ conv3_block3_1_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block3_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv3_block3_1_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block3_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m147,584\u001b[0m │ conv3_block3_1_relu[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block3_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │ conv3_block3_2_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block3_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv3_block3_2_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block3_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m66,048\u001b[0m │ conv3_block3_2_relu[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block3_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ conv3_block3_3_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block3_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv3_block2_out[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n", "│ │ │ │ conv3_block3_3_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block3_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv3_block3_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block4_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m65,664\u001b[0m │ conv3_block3_out[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block4_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │ conv3_block4_1_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block4_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv3_block4_1_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block4_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m147,584\u001b[0m │ conv3_block4_1_relu[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block4_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │ conv3_block4_2_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block4_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv3_block4_2_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block4_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m66,048\u001b[0m │ conv3_block4_2_relu[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block4_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ conv3_block4_3_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block4_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv3_block3_out[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n", "│ │ │ │ conv3_block4_3_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv3_block4_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv3_block4_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block1_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m131,328\u001b[0m │ conv3_block4_out[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block1_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block1_1_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block1_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv4_block1_1_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block1_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m590,080\u001b[0m │ conv4_block1_1_relu[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block1_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block1_2_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block1_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv4_block1_2_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block1_0_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m525,312\u001b[0m │ conv3_block4_out[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block1_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m263,168\u001b[0m │ conv4_block1_2_relu[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block1_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m4,096\u001b[0m │ conv4_block1_0_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block1_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m4,096\u001b[0m │ conv4_block1_3_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block1_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv4_block1_0_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ │ │ │ conv4_block1_3_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block1_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv4_block1_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block2_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m262,400\u001b[0m │ conv4_block1_out[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block2_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block2_1_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block2_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv4_block2_1_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block2_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m590,080\u001b[0m │ conv4_block2_1_relu[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block2_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block2_2_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block2_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv4_block2_2_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block2_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m263,168\u001b[0m │ conv4_block2_2_relu[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block2_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m4,096\u001b[0m │ conv4_block2_3_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block2_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv4_block1_out[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n", "│ │ │ │ conv4_block2_3_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block2_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv4_block2_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block3_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m262,400\u001b[0m │ conv4_block2_out[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block3_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block3_1_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block3_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv4_block3_1_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block3_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m590,080\u001b[0m │ conv4_block3_1_relu[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block3_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block3_2_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block3_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv4_block3_2_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block3_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m263,168\u001b[0m │ conv4_block3_2_relu[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block3_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m4,096\u001b[0m │ conv4_block3_3_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block3_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv4_block2_out[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n", "│ │ │ │ conv4_block3_3_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block3_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv4_block3_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block4_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m262,400\u001b[0m │ conv4_block3_out[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block4_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block4_1_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block4_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv4_block4_1_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block4_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m590,080\u001b[0m │ conv4_block4_1_relu[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block4_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block4_2_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block4_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv4_block4_2_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block4_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m263,168\u001b[0m │ conv4_block4_2_relu[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block4_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m4,096\u001b[0m │ conv4_block4_3_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block4_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv4_block3_out[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n", "│ │ │ │ conv4_block4_3_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block4_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv4_block4_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block5_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m262,400\u001b[0m │ conv4_block4_out[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block5_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block5_1_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block5_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv4_block5_1_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block5_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m590,080\u001b[0m │ conv4_block5_1_relu[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block5_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block5_2_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block5_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv4_block5_2_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block5_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m263,168\u001b[0m │ conv4_block5_2_relu[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block5_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m4,096\u001b[0m │ conv4_block5_3_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block5_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv4_block4_out[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n", "│ │ │ │ conv4_block5_3_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block5_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv4_block5_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block6_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m262,400\u001b[0m │ conv4_block5_out[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block6_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block6_1_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block6_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv4_block6_1_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block6_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m590,080\u001b[0m │ conv4_block6_1_relu[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block6_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │ conv4_block6_2_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block6_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv4_block6_2_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block6_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m263,168\u001b[0m │ conv4_block6_2_relu[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block6_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m4,096\u001b[0m │ conv4_block6_3_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block6_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv4_block5_out[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n", "│ │ │ │ conv4_block6_3_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv4_block6_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv4_block6_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv5_block1_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m524,800\u001b[0m │ conv4_block6_out[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv5_block1_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ conv5_block1_1_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv5_block1_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block1_1_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv5_block1_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,359,808\u001b[0m │ conv5_block1_1_relu[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv5_block1_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ conv5_block1_2_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv5_block1_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block1_2_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv5_block1_0_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m2048\u001b[0m) │ \u001b[38;5;34m2,099,200\u001b[0m │ conv4_block6_out[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv5_block1_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m2048\u001b[0m) │ \u001b[38;5;34m1,050,624\u001b[0m │ conv5_block1_2_relu[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv5_block1_0_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m2048\u001b[0m) │ \u001b[38;5;34m8,192\u001b[0m │ conv5_block1_0_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv5_block1_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m2048\u001b[0m) │ \u001b[38;5;34m8,192\u001b[0m │ conv5_block1_3_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv5_block1_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m2048\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block1_0_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ │ │ │ conv5_block1_3_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv5_block1_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m2048\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block1_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv5_block2_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m1,049,088\u001b[0m │ conv5_block1_out[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv5_block2_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ conv5_block2_1_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv5_block2_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block2_1_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv5_block2_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,359,808\u001b[0m │ conv5_block2_1_relu[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv5_block2_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ conv5_block2_2_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv5_block2_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block2_2_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv5_block2_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m2048\u001b[0m) │ \u001b[38;5;34m1,050,624\u001b[0m │ conv5_block2_2_relu[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv5_block2_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m2048\u001b[0m) │ \u001b[38;5;34m8,192\u001b[0m │ conv5_block2_3_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv5_block2_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m2048\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block1_out[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n", "│ │ │ │ conv5_block2_3_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv5_block2_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m2048\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block2_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv5_block3_1_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m1,049,088\u001b[0m │ conv5_block2_out[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv5_block3_1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ conv5_block3_1_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv5_block3_1_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block3_1_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv5_block3_2_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,359,808\u001b[0m │ conv5_block3_1_relu[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv5_block3_2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │ conv5_block3_2_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv5_block3_2_relu │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block3_2_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv5_block3_3_conv │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m2048\u001b[0m) │ \u001b[38;5;34m1,050,624\u001b[0m │ conv5_block3_2_relu[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mConv2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv5_block3_3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m2048\u001b[0m) │ \u001b[38;5;34m8,192\u001b[0m │ conv5_block3_3_conv[\u001b[38;5;34m0\u001b[0m… │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv5_block3_add (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m2048\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block2_out[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n", "│ │ │ │ conv5_block3_3_bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ conv5_block3_out │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m2048\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block3_add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ avg_pool │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv5_block3_out[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "│ (\u001b[38;5;33mGlobalAveragePooling2D\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ predictions (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1000\u001b[0m) │ \u001b[38;5;34m2,049,000\u001b[0m │ avg_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "└───────────────────────────┴────────────────────────┴────────────────┴────────────────────────┘\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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\n" ], "text/plain": [ "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m25,583,592\u001b[0m (97.59 MB)\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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\n" ], "text/plain": [ "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m53,120\u001b[0m (207.50 KB)\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "model = ResNet50(weights=\"imagenet\",input_shape=(224,224,3))\n", "model.summary()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "D-Sd-glECCdk" }, "outputs": [], "source": [ "model_new = Model(model.input,model.layers[-2].output)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "o1dcFrFtCCdl" }, "outputs": [], "source": [ "def preprocess_img(img):\n", " img = image.load_img(img,target_size=(224,224))\n", " img = image.img_to_array(img)\n", " img = np.expand_dims(img,axis=0)\n", " # Normalisation\n", " img = preprocess_input(img)\n", " return img" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "aksz6DN8CCdl" }, "outputs": [], "source": [ "#img = preprocess_img(IMG_PATH+\"1000268201_693b08cb0e.jpg\")\n", "#plt.imshow(img[0])\n", "#plt.axis(\"off\")\n", "#plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "BKTkUz9kCCdn" }, "outputs": [], "source": [ "def encode_image(img):\n", " img = preprocess_img(img)\n", " feature_vector = model_new.predict(img,verbose=0)\n", "\n", " feature_vector = feature_vector.reshape((-1,))\n", " #print(feature_vector.shape)\n", " return feature_vector" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "vE3ukd7HCCdn", "outputId": "2f5857ca-8afc-4d28-8c1a-44791b55e39e" }, "outputs": [ { "data": { "text/plain": [ "array([0.06536549, 0.1678271 , 0.32518435, ..., 0.05111533, 0.32817906,\n", " 1.0043344 ], dtype=float32)" ] }, "execution_count": 103, "metadata": {}, "output_type": "execute_result" } ], "source": [ "encode_image(IMG_PATH+\"1000268201_693b08cb0e.jpg\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "xIiAd0yLCCdo", "outputId": "672f7c2f-3ff0-43c1-f323-9dabf6df4149" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Encoding in Progress Time step 0 \n", "Encoding in Progress Time step 100 \n", "Encoding in Progress Time step 200 \n", "Encoding in Progress Time step 300 \n", "Encoding in Progress Time step 400 \n", "Encoding in Progress Time step 500 \n", "Encoding in Progress Time step 600 \n", "Encoding in Progress Time step 700 \n", "Encoding in Progress Time step 800 \n", "Encoding in Progress Time step 900 \n", "Encoding in Progress Time step 1000 \n", "Encoding in Progress Time step 1100 \n", "Encoding in Progress Time step 1200 \n", "Encoding in Progress Time step 1300 \n", "Encoding in Progress Time step 1400 \n", "Encoding in Progress Time step 1500 \n", "Encoding in Progress Time step 1600 \n", "Encoding in Progress Time step 1700 \n", "Encoding in Progress Time step 1800 \n", "Encoding in Progress Time step 1900 \n", "Encoding in Progress Time step 2000 \n", "Encoding in Progress Time step 2100 \n", "Encoding in Progress Time step 2200 \n", "Encoding in Progress Time step 2300 \n", "Encoding in Progress Time step 2400 \n", "Encoding in Progress Time step 2500 \n", "Encoding in Progress Time step 2600 \n", "Encoding in Progress Time step 2700 \n", "Encoding in Progress Time step 2800 \n", "Encoding in Progress Time step 2900 \n", "Encoding in Progress Time step 3000 \n", "Encoding in Progress Time step 3100 \n", "Encoding in Progress Time step 3200 \n", "Encoding in Progress Time step 3300 \n", "Encoding in Progress Time step 3400 \n", "Encoding in Progress Time step 3500 \n", "Encoding in Progress Time step 3600 \n", "Encoding in Progress Time step 3700 \n", "Encoding in Progress Time step 3800 \n", "Encoding in Progress Time step 3900 \n", "Encoding in Progress Time step 4000 \n", "Encoding in Progress Time step 4100 \n", "Encoding in Progress Time step 4200 \n", "Encoding in Progress Time step 4300 \n", "Encoding in Progress Time step 4400 \n", "Encoding in Progress Time step 4500 \n", "Encoding in Progress Time step 4600 \n", "Encoding in Progress Time step 4700 \n", "Encoding in Progress Time step 4800 \n", "Encoding in Progress Time step 4900 \n", "Encoding in Progress Time step 5000 \n", "Encoding in Progress Time step 5100 \n", "Encoding in Progress Time step 5200 \n", "Encoding in Progress Time step 5300 \n", "Encoding in Progress Time step 5400 \n", "Encoding in Progress Time step 5500 \n", "Encoding in Progress Time step 5600 \n", "Encoding in Progress Time step 5700 \n", "Encoding in Progress Time step 5800 \n", "Encoding in Progress Time step 5900 \n", "Total Time Taken : 2443.200225830078\n" ] } ], "source": [ "start = time()\n", "encoding_train = {}\n", "#image_id -->feature_vector extracted from Resnet Image\n", "\n", "for ix,img_id in enumerate(train):\n", " img_path = IMG_PATH+\"/\"+img_id+\".jpg\"\n", " encoding_train[img_id] = encode_image(img_path)\n", "\n", " if ix%100==0:\n", " print(\"Encoding in Progress Time step %d \"%ix)\n", "\n", "end_t = time()\n", "print(\"Total Time Taken :\",end_t-start)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "01eNgIj_CCdo", "outputId": "be089555-f598-419a-cfed-fdbf86c7788b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mkdir: cannot create directory ‘saved’: File exists\n" ] } ], "source": [ "!mkdir saved" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "6rW2XmRoCCdo" }, "outputs": [], "source": [ "# Store everything to the disk\n", "with open(\"saved/encoded_train_features.pkl\",\"wb\") as f:\n", " pickle.dump(encoding_train,f)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "0gvgPgf7CCdo", "outputId": "4357b00f-06a7-4081-f711-ca0cf5d17888" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test Encoding in Progress Time step 0 \n", "Test Encoding in Progress Time step 100 \n", "Test Encoding in Progress Time step 200 \n", "Test Encoding in Progress Time step 300 \n", "Test Encoding in Progress Time step 400 \n", "Test Encoding in Progress Time step 500 \n", "Test Encoding in Progress Time step 600 \n", "Test Encoding in Progress Time step 700 \n", "Test Encoding in Progress Time step 800 \n", "Test Encoding in Progress Time step 900 \n", "Total Time Taken(test) : 400.96601963043213\n" ] } ], "source": [ "start = time()\n", "encoding_test = {}\n", "#image_id -->feature_vector extracted from Resnet Image\n", "\n", "for ix,img_id in enumerate(test):\n", " img_path = IMG_PATH+\"/\"+img_id+\".jpg\"\n", " encoding_test[img_id] = encode_image(img_path)\n", "\n", " if ix%100==0:\n", " print(\"Test Encoding in Progress Time step %d \"%ix)\n", "\n", "end_t = time()\n", "print(\"Total Time Taken(test) :\",end_t-start)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "vHpMtr01CCdp" }, "outputs": [], "source": [ "with open(\"saved/encoded_test_features.pkl\",\"wb\") as f:\n", " pickle.dump(encoding_test,f)" ] }, { "cell_type": "markdown", "metadata": { "id": "55RlJgoXCCdp" }, "source": [ "### Data pre-processing for Captions" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "o-0MqO8yCCdp", "outputId": "f8275e43-9337-4188-e6f8-a95a24bd3320" }, "outputs": [ { "data": { "text/plain": [ "1845" ] }, "execution_count": 109, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Vocab\n", "len(total_words)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "WXezB9suCCdp" }, "outputs": [], "source": [ "word_to_idx = {}\n", "idx_to_word = {}\n", "\n", "for i,word in enumerate(total_words):\n", " word_to_idx[word] = i+1\n", " idx_to_word[i+1] = word" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "hJXkiL0qCCdp", "outputId": "37e9f506-b21e-4f36-aaa1-c1468f313293" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1845\n" ] } ], "source": [ "#word_to_idx[\"dog\"]\n", "#idx_to_word[1]\n", "print(len(idx_to_word))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "m8CizGYbCCdq", "outputId": "80c81276-aef8-4360-d758-90d3b635fe73" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Vocab Size 1848\n" ] } ], "source": [ "# Two special words\n", "idx_to_word[1846] = 'startseq'\n", "word_to_idx['startseq'] = 1846\n", "\n", "idx_to_word[1847] = 'endseq'\n", "word_to_idx['endseq'] = 1847\n", "\n", "vocab_size = len(word_to_idx) + 1\n", "print(\"Vocab Size\",vocab_size)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "XFrcPYy2CCdq", "outputId": "0e8f5beb-4a8a-4ff5-f2eb-7e78103b2845" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "35\n" ] } ], "source": [ "max_len = 0\n", "for key in train_descriptions.keys():\n", " for cap in train_descriptions[key]:\n", " max_len = max(max_len,len(cap.split()))\n", "\n", "print(max_len)" ] }, { "cell_type": "markdown", "metadata": { "id": "xS0D5dh-CCdq" }, "source": [ "### Data Loader (Generator)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "QFFGyPEj-x6F" }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "id": "MtXxritgCCdr" }, "source": [ "## Word Embeddings" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "E3Wr4oZ4EJvo", "outputId": "53694463-cfca-42c6-d604-8adb3b7adfcb" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2025-02-20 20:57:33-- http://nlp.stanford.edu/data/glove.6B.zip\n", "Resolving nlp.stanford.edu (nlp.stanford.edu)... 171.64.67.140\n", "Connecting to nlp.stanford.edu (nlp.stanford.edu)|171.64.67.140|:80... connected.\n", "HTTP request sent, awaiting response... 302 Found\n", "Location: https://nlp.stanford.edu/data/glove.6B.zip [following]\n", "--2025-02-20 20:57:33-- https://nlp.stanford.edu/data/glove.6B.zip\n", "Connecting to nlp.stanford.edu (nlp.stanford.edu)|171.64.67.140|:443... connected.\n", "HTTP request sent, awaiting response... 301 Moved Permanently\n", "Location: https://downloads.cs.stanford.edu/nlp/data/glove.6B.zip [following]\n", "--2025-02-20 20:57:33-- https://downloads.cs.stanford.edu/nlp/data/glove.6B.zip\n", "Resolving downloads.cs.stanford.edu (downloads.cs.stanford.edu)... 171.64.64.22\n", "Connecting to downloads.cs.stanford.edu (downloads.cs.stanford.edu)|171.64.64.22|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 862182613 (822M) [application/zip]\n", "Saving to: ‘glove.6B.zip.1’\n", "\n", "glove.6B.zip.1 100%[===================>] 822.24M 4.97MB/s in 2m 39s \n", "\n", "2025-02-20 21:00:12 (5.17 MB/s) - ‘glove.6B.zip.1’ saved [862182613/862182613]\n", "\n", "Archive: glove.6B.zip\n", "replace glove.6B.50d.txt? [y]es, [n]o, [A]ll, [N]one, [r]ename: nA\n", "replace glove.6B.100d.txt? [y]es, [n]o, [A]ll, [N]one, [r]ename: n\n", "replace glove.6B.200d.txt? [y]es, [n]o, [A]ll, [N]one, [r]ename: n\n", "replace glove.6B.300d.txt? [y]es, [n]o, [A]ll, [N]one, [r]ename: n\n" ] } ], "source": [ "\n", "!wget http://nlp.stanford.edu/data/glove.6B.zip\n", "!unzip glove.6B.zip\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "eYParhjuCCdr" }, "outputs": [], "source": [ "f = open(\"/content/glove.6B.50d.txt\",encoding='utf8')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Nea4c9r8CCdr" }, "outputs": [], "source": [ "embedding_index = {}\n", "\n", "for line in f:\n", " values = line.split()\n", "\n", " word = values[0]\n", " word_embedding = np.array(values[1:],dtype='float')\n", " embedding_index[word] = word_embedding\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "_B_CtQxECCdr" }, "outputs": [], "source": [ "f.close()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "W1l70uO6CCds", "outputId": "5b4ff182-369d-47cb-b661-2c36c6f99813" }, "outputs": [ { "data": { "text/plain": [ "array([ 0.52042 , -0.8314 , 0.49961 , 1.2893 , 0.1151 , 0.057521,\n", " -1.3753 , -0.97313 , 0.18346 , 0.47672 , -0.15112 , 0.35532 ,\n", " 0.25912 , -0.77857 , 0.52181 , 0.47695 , -1.4251 , 0.858 ,\n", " 0.59821 , -1.0903 , 0.33574 , -0.60891 , 0.41742 , 0.21569 ,\n", " -0.07417 , -0.5822 , -0.4502 , 0.17253 , 0.16448 , -0.38413 ,\n", " 2.3283 , -0.66682 , -0.58181 , 0.74389 , 0.095015, -0.47865 ,\n", " -0.84591 , 0.38704 , 0.23693 , -1.5523 , 0.64802 , -0.16521 ,\n", " -1.4719 , -0.16224 , 0.79857 , 0.97391 , 0.40027 , -0.21912 ,\n", " -0.30938 , 0.26581 ])" ] }, "execution_count": 119, "metadata": {}, "output_type": "execute_result" } ], "source": [ "embedding_index['apple']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "KwzNIiElCCds" }, "outputs": [], "source": [ "def get_embedding_matrix():\n", " emb_dim = 50\n", " matrix = np.zeros((vocab_size,emb_dim))\n", " for word,idx in word_to_idx.items():\n", " embedding_vector = embedding_index.get(word)\n", "\n", " if embedding_vector is not None:\n", " matrix[idx] = embedding_vector\n", "\n", " return matrix\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "I7ePEY6kCCds", "outputId": "34d532e0-badd-41c9-9bfb-8f9c5d328a9b" }, "outputs": [ { "data": { "text/plain": [ "(1848, 50)" ] }, "execution_count": 121, "metadata": {}, "output_type": "execute_result" } ], "source": [ "embedding_matrix = get_embedding_matrix()\n", "embedding_matrix.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "JmaeA9ohCCds" }, "outputs": [], "source": [ "#embedding_matrix[1847]" ] }, { "cell_type": "markdown", "metadata": { "id": "n0iOEldZCCdt" }, "source": [ "#### Model Architecture" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "9fOlAPORCCdt" }, "outputs": [], "source": [ "input_img_features = Input(shape=(2048,))\n", "inp_img1 = Dropout(0.3)(input_img_features)\n", "inp_img2 = Dense(256,activation='relu')(inp_img1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Mm1IJMboCCdt" }, "outputs": [], "source": [ "# Captions as Input\n", "input_captions = Input(shape=(max_len,))\n", "inp_cap1 = Embedding(input_dim=vocab_size,output_dim=50,mask_zero=True)(input_captions)\n", "inp_cap2 = Dropout(0.3)(inp_cap1)\n", "inp_cap3 = LSTM(256)(inp_cap2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "3tWErT5NCCdt" }, "outputs": [], "source": [ "decoder1 = add([inp_img2,inp_cap3])\n", "decoder2 = Dense(256,activation='relu')(decoder1)\n", "outputs = Dense(vocab_size,activation='softmax')(decoder2)\n", "\n", "# Combined Model\n", "model = Model(inputs=[input_img_features,input_captions],outputs=outputs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 578 }, "id": "owRarvzmCCdu", "outputId": "ab1b7082-7570-4f8a-9f46-149c3c34ea05" }, "outputs": [ { "data": { "text/html": [ "
Model: \"functional_3\"\n",
              "
\n" ], "text/plain": [ "\u001b[1mModel: \"functional_3\"\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
              "┃ Layer (type)               Output Shape                   Param #  Connected to           ┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
              "│ input_layer_5             │ (None, 35)             │              0 │ -                      │\n",
              "│ (InputLayer)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ input_layer_4             │ (None, 2048)           │              0 │ -                      │\n",
              "│ (InputLayer)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ embedding_1 (Embedding)   │ (None, 35, 50)         │         92,400 │ input_layer_5[0][0]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_2 (Dropout)       │ (None, 2048)           │              0 │ input_layer_4[0][0]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_3 (Dropout)       │ (None, 35, 50)         │              0 │ embedding_1[0][0]      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ not_equal_1 (NotEqual)    │ (None, 35)             │              0 │ input_layer_5[0][0]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dense_3 (Dense)           │ (None, 256)            │        524,544 │ dropout_2[0][0]        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ lstm_1 (LSTM)             │ (None, 256)            │        314,368 │ dropout_3[0][0],       │\n",
              "│                           │                        │                │ not_equal_1[0][0]      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ add_1 (Add)               │ (None, 256)            │              0 │ dense_3[0][0],         │\n",
              "│                           │                        │                │ lstm_1[0][0]           │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dense_4 (Dense)           │ (None, 256)            │         65,792 │ add_1[0][0]            │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dense_5 (Dense)           │ (None, 1848)           │        474,936 │ dense_4[0][0]          │\n",
              "└───────────────────────────┴────────────────────────┴────────────────┴────────────────────────┘\n",
              "
\n" ], "text/plain": [ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓\n", "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to \u001b[0m\u001b[1m \u001b[0m┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩\n", "│ input_layer_5 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m35\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", "│ (\u001b[38;5;33mInputLayer\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ input_layer_4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", "│ (\u001b[38;5;33mInputLayer\u001b[0m) │ │ │ │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ embedding_1 (\u001b[38;5;33mEmbedding\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m35\u001b[0m, \u001b[38;5;34m50\u001b[0m) │ \u001b[38;5;34m92,400\u001b[0m │ input_layer_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ input_layer_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m35\u001b[0m, \u001b[38;5;34m50\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ embedding_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ not_equal_1 (\u001b[38;5;33mNotEqual\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m35\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ input_layer_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ dense_3 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m524,544\u001b[0m │ dropout_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m314,368\u001b[0m │ dropout_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ │ │ not_equal_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ add_1 (\u001b[38;5;33mAdd\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ dense_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ │ │ lstm_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ dense_4 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m65,792\u001b[0m │ add_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n", "│ dense_5 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1848\u001b[0m) │ \u001b[38;5;34m474,936\u001b[0m │ dense_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "└───────────────────────────┴────────────────────────┴────────────────┴────────────────────────┘\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
 Total params: 1,472,040 (5.62 MB)\n",
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 Trainable params: 1,472,040 (5.62 MB)\n",
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 Non-trainable params: 0 (0.00 B)\n",
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\n" ], "text/plain": [ "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "model.summary()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "AXL79-w-CCdu" }, "outputs": [], "source": [ "model.compile(loss='categorical_crossentropy',optimizer=\"adam\")" ] }, { "cell_type": "markdown", "metadata": { "id": "Rjriv1uECCdu" }, "source": [ "### Training of Model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "qndZ34XUQ8Mn", "outputId": "968ccc48-d082-426f-fa85-b2c41dc936d5" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/20\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 324ms/step - loss: 7.1339\n", "Epoch 2/20\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 317ms/step - loss: 6.2380\n", "Epoch 3/20\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 547ms/step - loss: 5.7255\n", "Epoch 4/20\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 393ms/step - loss: 5.8111\n", "Epoch 5/20\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 301ms/step - loss: 5.5374\n", "Epoch 6/20\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 301ms/step - loss: 5.7522\n", "Epoch 7/20\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 305ms/step - loss: 5.6486\n", "Epoch 8/20\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 422ms/step - loss: 5.6147\n", "Epoch 9/20\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 488ms/step - loss: 5.2110\n", "Epoch 10/20\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 310ms/step - loss: 5.3810\n", "Epoch 11/20\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 306ms/step - loss: 5.5084\n", "Epoch 12/20\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 311ms/step - loss: 5.3409\n", "Epoch 13/20\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 325ms/step - loss: 5.4901\n", "Epoch 14/20\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 564ms/step - loss: 5.3307\n", "Epoch 15/20\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 322ms/step - loss: 5.2439\n", "Epoch 16/20\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 310ms/step - loss: 5.2932\n", "Epoch 17/20\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 299ms/step - loss: 5.1518\n", "Epoch 18/20\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 322ms/step - loss: 5.1742\n", "Epoch 19/20\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 492ms/step - loss: 5.1293\n", "Epoch 20/20\n", "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 530ms/step - loss: 5.0389\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 141, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import tensorflow as tf # Import tensorflow\n", "\n", "epochs = 20\n", "number_pics_per_batch = 3\n", "steps = 8\n", "\n", "def data_generator(descriptions, photos, wordtoix, max_length, num_photos_per_batch):\n", " X1, X2, y = list(), list(), list()\n", " n=0\n", " # loop for ever over images\n", " while 1:\n", " for key, desc_list in descriptions.items():\n", " n+=1\n", " photo = photos[key]\n", " for desc in desc_list:\n", " seq = [wordtoix[word] for word in desc.split(' ') if word in wordtoix]\n", " for i in range(1, len(seq)):\n", " in_seq, out_seq = seq[:i], seq[i]\n", " in_seq = pad_sequences([in_seq], maxlen=max_length)[0]\n", " out_seq = to_categorical([out_seq], num_classes=vocab_size)[0]\n", " X1.append(photo)\n", " X2.append(in_seq)\n", " y.append(out_seq)\n", " if n==num_photos_per_batch:\n", " # Yielding a tuple instead of a list to match output_signature\n", " yield ((np.array(X1), np.array(X2)), np.array(y))\n", " X1, X2, y = list(), list(), list()\n", " n=0\n", "\n", "# Wrap the generator with tf.data.Dataset.from_generator and specify output signature:\n", "dataset = tf.data.Dataset.from_generator(\n", " lambda: data_generator(train_descriptions, encoding_train, word_to_idx, max_len, number_pics_per_batch),\n", " output_signature=(\n", " (tf.TensorSpec(shape=(None, 2048), dtype=tf.float32), tf.TensorSpec(shape=(None, max_len), dtype=tf.int32)),\n", " tf.TensorSpec(shape=(None, vocab_size), dtype=tf.float32)\n", " )\n", ")\n", "\n", "model.fit(dataset, epochs=epochs, steps_per_epoch=steps, verbose=1) # Fit the model using the dataset" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "jf2F-Q0HCCdu" }, "outputs": [], "source": [ "epochs = 20\n", "batch_size = 3\n", "steps = len(train_descriptions)//20\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 141 }, "id": "x3oTsp74J7qn", "outputId": "1ec63010-774a-45c3-e52d-b87116234a70" }, "outputs": [ { "ename": "AttributeError", "evalue": "'Functional' object has no attribute 'diagram'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdiagram\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m: 'Functional' object has no attribute 'diagram'" ] } ], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 35 }, "id": "IIUMpUSmR-NM", "outputId": "692da29c-672b-48c8-fa0a-70352d96d2ea" }, "outputs": [ { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'dog in the the the the the'" ] }, "execution_count": 149, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# prompt: predict using model , for an image\n", "\n", "def generate_caption(photo):\n", " in_text = \"startseq\"\n", " for i in range(max_len):\n", " sequence = [word_to_idx[w] for w in in_text.split() if w in word_to_idx]\n", " sequence = pad_sequences([sequence], maxlen=max_len)\n", " yhat = model.predict([photo,sequence], verbose=0)\n", " yhat = np.argmax(yhat)\n", " word = idx_to_word[yhat]\n", " in_text += ' ' + word\n", " if word == 'endseq':\n", " break\n", " final_caption = in_text.split()[1:-1]\n", " final_caption = ' '.join(final_caption)\n", " return final_caption\n", "\n", "# Example usage:\n", "photo_path = \"/content/5/Flickr8k_Dataset/1001773457_577c3a7d70.jpg\" # Replace with your image path\n", "\n", "photo = encode_image(photo_path)\n", "photo = photo.reshape((1,2048))\n", "\n", "caption = generate_caption(photo)\n", "caption\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 245 }, "id": "ZNBe21L8Ja75", "outputId": "5ee1fc27-8fd1-43b4-a3be-ea96d54daed5" }, "outputs": [ { "ename": "AttributeError", "evalue": "'Functional' object has no attribute 'fit_generator'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m\u001b[0m in \u001b[0;36mtrain\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mgenerator\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_descriptions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mencoding_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mword_to_idx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmax_len\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'./model_weights/model_'\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'.h5'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mAttributeError\u001b[0m: 'Functional' object has no attribute 'fit_generator'" ] } ], "source": [ "train()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "V7YGCeYXCCdv" }, "outputs": [], "source": [ "model = load_model('./model_weights/model_9.h5')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Pw88OaefUQkj" }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "id": "207d1b23" }, "source": [ "# Image Captioning with InceptionV3 and LSTM\n", "\n", "This notebook demonstrates how to build an image captioning model using a combination of InceptionV3 for image feature extraction and an LSTM network for caption generation.\n", "\n", "## 1. Data Loading and Preprocessing" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-WbvGZn_SBuM" }, "outputs": [], "source": [ "# IMPORTANT: RUN THIS CELL IN ORDER TO IMPORT YOUR KAGGLE DATA SOURCES,\n", "# THEN FEEL FREE TO DELETE THIS CELL.\n", "# NOTE: THIS NOTEBOOK ENVIRONMENT DIFFERS FROM KAGGLE'S PYTHON\n", "# ENVIRONMENT SO THERE MAY BE MISSING LIBRARIES USED BY YOUR\n", "# NOTEBOOK.\n", "import kagglehub\n", "adityajn105_flickr8k_path = kagglehub.dataset_download('adityajn105/flickr8k')\n", "\n", "print('Data source import complete.')\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-07-01T16:59:45.065022Z", "iopub.status.busy": "2025-07-01T16:59:45.064789Z", "iopub.status.idle": "2025-07-01T17:00:02.24498Z", "shell.execute_reply": "2025-07-01T17:00:02.244129Z", "shell.execute_reply.started": "2025-07-01T16:59:45.065001Z" }, "id": "4d74wBtTuqmv", "outputId": "4ee7c002-aa7e-4da5-a662-a50e210e34d2", "trusted": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2025-07-01 16:59:50.087359: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "E0000 00:00:1751389190.297618 35 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", "E0000 00:00:1751389190.359663 35 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n" ] } ], "source": [ "import os\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as p\n", "import seaborn as sns\n", "import tensorflow as tf\n", "from tensorflow.keras.applications.inception_v3 import InceptionV3\n", "from tensorflow.keras.preprocessing.image import img_to_array, load_img\n", "from tensorflow.keras.preprocessing.text import Tokenizer\n", "from tensorflow.keras.preprocessing.sequence import pad_sequences\n", "from tensorflow.keras.utils import to_categorical, plot_model\n", "from tensorflow.keras.models import Model, load_model\n", "from tensorflow.keras.layers import Input, Dense, LSTM, Embedding, add\n", "from tensorflow.keras.layers import Flatten, Dropout, BatchNormalization\n", "from tensorflow.keras.optimizers import Adam\n", "from tensorflow.keras.callbacks import EarlyStopping, LearningRateScheduler\n", "from sklearn.model_selection import train_test_split\n", "from PIL import Image\n", "from tqdm import tqdm_notebook\n", "from collections import Counter\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "execution": { "iopub.execute_input": "2025-07-01T17:00:02.246505Z", "iopub.status.busy": "2025-07-01T17:00:02.245913Z", "iopub.status.idle": "2025-07-01T17:00:02.425115Z", "shell.execute_reply": "2025-07-01T17:00:02.424306Z", "shell.execute_reply.started": "2025-07-01T17:00:02.24646Z" }, "id": "0qnZbvVTzbJG", "outputId": "732b38d4-bbe9-4276-c55b-88a98dc9e3a0", "trusted": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Path to dataset files: /kaggle/input/\n" ] } ], "source": [ "import kagglehub\n", "\n", "# Download latest version\n", "path = kagglehub.dataset_download(\"adityajn105/flickr8k\")\n", "\n", "print(\"Path to dataset files:\", path)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "execution": { "iopub.execute_input": "2025-07-01T17:00:02.426991Z", "iopub.status.busy": "2025-07-01T17:00:02.426775Z", "iopub.status.idle": "2025-07-01T17:00:02.513962Z", "shell.execute_reply": "2025-07-01T17:00:02.513221Z", "shell.execute_reply.started": "2025-07-01T17:00:02.426973Z" }, "id": "mKUQrHd2upa9", "outputId": "9ebc6e37-719a-4d4d-bf80-2956d46da9fd", "trusted": true }, "outputs": [ { "data": { "text/plain": [ "['1000268201_693b08cb0e.jpg,a child in a pink dress is climbing up a set of stairs in an entry way .\\n',\n", " '1000268201_693b08cb0e.jpg,a little girl climbing the stairs to her playhouse .\\n',\n", " '1001773457_577c3a7d70.jpg,a black dog and a tri-colored dog playing with each other on the road .\\n',\n", " '1001773457_577c3a7d70.jpg,two dogs on pavement moving toward each other .\\n',\n", " '1002674143_1b742ab4b8.jpg,a small girl in the grass plays with fingerpaints in front of a white canvas with a rainbow on it .\\n']" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "images_directory = f'{path}/Images/'\n", "captions_path = f'{path}/captions.txt'\n", "def load_captions(file_path):\n", " with open(file_path, 'r') as f:\n", " captions = f.readlines()\n", " captions = [caption.lower() for caption in captions[1:]]\n", " return captions\n", "\n", "def tokenize_captions(captions):\n", " tokenizer = Tokenizer()\n", " tokenizer.fit_on_texts(captions)\n", " return tokenizer\n", "\n", "captions = load_captions(captions_path)\n", "captions[:15:3]\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-07-01T17:00:02.515223Z", "iopub.status.busy": "2025-07-01T17:00:02.514685Z", "iopub.status.idle": "2025-07-01T17:00:02.536448Z", "shell.execute_reply": "2025-07-01T17:00:02.535721Z", "shell.execute_reply.started": "2025-07-01T17:00:02.515197Z" }, "id": "fh16tvC4zq-M", "trusted": true }, "outputs": [], "source": [ "import regex as re" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "execution": { "iopub.execute_input": "2025-07-01T17:00:02.537568Z", "iopub.status.busy": "2025-07-01T17:00:02.537266Z", "iopub.status.idle": "2025-07-01T17:00:03.439059Z", "shell.execute_reply": "2025-07-01T17:00:03.438481Z", "shell.execute_reply.started": "2025-07-01T17:00:02.537542Z" }, "id": "t1BLpBvgy1Gg", "outputId": "ddf4d7a2-7c87-40e9-8cba-039dcabc3cdb", "trusted": true }, "outputs": [ { "data": { "text/plain": [ "['a child in a pink dress is climbing up a set of stairs in an entry way',\n", " 'a little girl climbing into a wooden playhouse',\n", " 'a little girl in a pink dress going into a wooden cabin',\n", " 'a black dog and a tricolored dog playing with each other on the road',\n", " 'two dogs of different breeds looking at each other on the road',\n", " 'a little girl covered in paint sits in front of a painted rainbow with her hands in a bowl',\n", " 'a small girl in the grass plays with fingerpaints in front of a white canvas with a rainbow on it',\n", " 'young girl with pigtails painting outside in the grass']" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def clean_text(text):\n", " text = re.sub(r'[^\\w\\s]', '', text)\n", " text = re.sub(r'\\d+', '', text)\n", " text = re.sub(r'\\s+', ' ', text).strip()\n", " return text\n", "\n", "cleaned_captions = [clean_text(caption.split(',')[1]) for caption in captions]\n", "cleaned_captions[:15:2]\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "execution": { "iopub.execute_input": "2025-07-01T17:00:03.439992Z", "iopub.status.busy": "2025-07-01T17:00:03.43974Z", "iopub.status.idle": "2025-07-01T17:00:03.473185Z", "shell.execute_reply": "2025-07-01T17:00:03.472606Z", "shell.execute_reply.started": "2025-07-01T17:00:03.439973Z" }, "id": "F_GS7noay2YB", "outputId": "919daf85-211d-4d04-cfda-64cafcb2b76c", "trusted": true }, "outputs": [ { "data": { "text/plain": [ "(['1000268201_693b08cb0e.jpg\\tstart a child in a pink dress is climbing up a set of stairs in an entry way end\\n',\n", " '1000268201_693b08cb0e.jpg\\tstart a little girl climbing the stairs to her playhouse end\\n',\n", " '1001773457_577c3a7d70.jpg\\tstart a black dog and a tricolored dog playing with each other on the road end\\n',\n", " '1001773457_577c3a7d70.jpg\\tstart two dogs on pavement moving toward each other end\\n',\n", " '1002674143_1b742ab4b8.jpg\\tstart a small girl in the grass plays with fingerpaints in front of a white canvas with a rainbow on it end\\n',\n", " '1003163366_44323f5815.jpg\\tstart a man lays on a bench while his dog sits by him end\\n',\n", " '1003163366_44323f5815.jpg\\tstart a shirtless man lies on a park bench with his dog end\\n'],\n", " 40455)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "captions_IDs = []\n", "for i in range(len(cleaned_captions)):\n", " item = captions[i].split(',')[0]+'\\t'+'start '+cleaned_captions[i]+' end\\n'\n", " captions_IDs.append(item)\n", "\n", "captions_IDs[:20:3], len(captions_IDs)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 566 }, "execution": { "iopub.execute_input": "2025-07-01T17:00:03.474285Z", "iopub.status.busy": "2025-07-01T17:00:03.474023Z", "iopub.status.idle": "2025-07-01T17:00:04.627375Z", "shell.execute_reply": "2025-07-01T17:00:04.626249Z", "shell.execute_reply.started": "2025-07-01T17:00:03.474267Z" }, "id": "6BeY8tZKy3hT", "outputId": "44624a95-9db0-49b0-aada-a184c7eefd41", "trusted": true }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def visualaization(data, num_of_images):\n", " captions_dictionary = {}\n", " for item in data[100:100+(num_of_images)*5]:\n", " image_id, caption = item.split('\\t')\n", " if image_id not in captions_dictionary:\n", " captions_dictionary[image_id] = []\n", " captions_dictionary[image_id].append(caption)\n", " else:\n", " list_captions = [x for x in captions_dictionary.items()]\n", "\n", " count = 1\n", " fig = plt.figure(figsize=(10,20))\n", " for filename in list(captions_dictionary.keys()):\n", " captions = captions_dictionary[filename]\n", " image_load = load_img(images_directory+filename, target_size=(199,199,3))\n", "\n", " ax = fig.add_subplot(num_of_images,2,count,xticks=[],yticks=[])\n", " ax.imshow(image_load)\n", " count += 1\n", "\n", " ax = fig.add_subplot(num_of_images,2,count)\n", " plt.axis('off')\n", " ax.plot()\n", " ax.set_xlim(0,1)\n", " ax.set_ylim(0,len(captions))\n", " for i, caption in enumerate(captions):\n", " ax.text(0,i,caption,fontsize=20)\n", " count += 1\n", " plt.show()\n", "\n", "visualaization(captions_IDs, 5)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 331 }, "execution": { "iopub.execute_input": "2025-07-01T17:00:04.628686Z", "iopub.status.busy": "2025-07-01T17:00:04.628407Z", "iopub.status.idle": "2025-07-01T17:00:05.664921Z", "shell.execute_reply": "2025-07-01T17:00:05.66419Z", "shell.execute_reply.started": "2025-07-01T17:00:04.628667Z" }, "id": "70HaMX82y45l", "outputId": "552c2944-f8a5-46b9-9b29-dffea34c2ef4", "trusted": true }, "outputs": [ { "data": { "image/png": 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x1NbWbvLcefPmNXkw81vf+lb88Ic//Mw1qX///vHNb34zRo4cGd/85jcbtO7pp58e55577md+GGPw4MFx5plnxrBhw+K8885LvNZbb70lnAoAAAAAZFXT/hQvAAAAAACQVqZfOE/aoa25HH/88fHcc8/FmWeemXEw9b9ts802ccUVVySe//rrrzdqv43p3bt33HLLLZv8ovyIESPipptuyqjr4UMPPdTI6tqOd999N954443E84cMGRK33HLLJrut7bPPPvGb3/wmo1ruuOOOjOYn1adPn7j11ls3+gX/sWPHxrnnnpt4zfnz528y2LhmzZpEax133HGJ9/20Ll26xP777x/XXnttvPzyy3HNNddEr169GrRWa3XvvfcmDgz36dMnbr/99nqDqZ+2ww47xHXXXZe4jj//+c+J52YiNzc3brzxxs+FFz+tR48e8X//938ZrfvOO+9sck6m3ZI/DmJtTHFxcdx0000xaNCgjNZuj1rysW1tzjzzzPjud79b7/twfn5+XHnllWlDgPVpqfuhqa/rvXr1ikMOOSRuvPHGePnll+Pyyy//XCf31iTT9+1f/epXm+w63rdv3/jDH/6QUfh4ypQpG31OLF68OKPOnSUlJXHrrbdu8jk5bty4jN5LmktOTk6cddZZ8eSTT8Y555wT48ePjz59+kSHDh2ipKQkhg0bFgcffHBcccUV8eKLL8bee++ddp3rrrsu7r///jj66KMzCqamc9hhh8XJJ5+ceH5T/hni40B8fdek3XbbLb7whS9kvO5BBx0U55133meCqZ928MEHx3bbbZd4vUw6SgMAAAAAJCGcCgAAAAAALaCysjKj+V26dGmiShqmT58+GQUzN2XvvfeOwsLCRHPff//9rO37384555zo0aNHork77LBDHH744YnXfvPNNxMHUNq6xx57LKP5l156aXTr1i3R3L322iu++tWvJl575syZMXv27IzqSeLHP/5xoo7GmTxHIiI+/PDDjY4XFxcnWifTa0w6RUVFceihhyYKZrYljz/+eOK5P/7xjxNfEyIi9thjj9htt90SzX3jjTcy6jKa1DHHHJOohm222SZxl+aITT83169fn1HwZ+TIkXHCCSckmltSUhIXXXRR4rXbq5Z6bFubrbfeOs4444xNzissLMyoO/H8+fMbU1aDNed1vWvXrnHsscdGSUlJo9dqCrNnz46ZM2cmnv+Vr3wlvvzlLyea26VLl/jJT36SUT0be7/417/+FalUKvFaZ5xxRuIOxXvvvXfsu+++idduDj//+c/jzDPPjIKCgk3OLSgoqPe9M9tdmidMmJB47vTp07O698c6dOgQP/7xjzc5b+edd8543R/96EebnLepcPantdR1DgAAAABov4RTAQAAAACgDaivW057kZOTEz179kw0d+HChU1SQ5cuXeKwww7L6Jzjjz8+8dy6urqMOmy1Zf/85z8Tz91mm2022gUwndNPPz2j+f/4xz8ymr8pw4cPj4MPPjjR3G7dukX//v0Tr11WVrbR8aRByeuuu26Ta22Oli5dmjjg3rt374xCLx9LGiiqq6uLF198MeP1NyY3NzdRaO9jo0ePTjx33bp1Gx1/6623orq6OvF6xx13XEY/cvDlL385USC8vWrJx7a1+d73vhd5eXmJ5raF+yHpdf3qq6+OqqqqJq6mZWX6fv2d73wno/m77757bLvttonnb+zzTCZh/IKCgjj66KMTz4+I+MY3vpHR/Kb0ta99LY488siWLiOtTLq7N9WfIfbcc89E3b2HDRuW0bp77713os+QmfwYQVu73gMAAAAArV9+SxcAAAAAAACbo44dO2Y0v6103Jw9e3a8+eabMXPmzFiwYEEsXrw4Vq9eHWvXro2qqqqoqanJqMtUOuXl5VFeXh5FRUVZqvo/9t5770TdoD5t2223jQEDBsSiRYsSzX///fcz6m7UFq1bty7mzJmTeP5BBx2U8R5bbbVVbL311om7q02bNi3jPTYmk86tERF9+/aNjz76KNHctWvXbnQ8aajm1VdfjX333TcOPPDA2GmnnWKrrbaKQYMGZf1109a8+eabiefusssuiQNwn5YkoPKxadOmxRFHHJHxHvXZZZddMgpwZtLBblOBlkw70h1wwAEZzc/NzY399tsv/vKXv2R0XnvRko9ta1JUVJS4U2ZE27gfkl7XH3nkkXjttdfiwAMPjPHjx8eIESNi4MCBGX+mbM0yuUYPGzYsRo0alfEeX/nKV+Kdd95JNHfOnDmxbt266Ny58+fGMrnm7bLLLtGlS5fE8yMidtttt+jcuXOLvz4LCwsTde9sjOrq6nj77bfj3XffjVmzZsWiRYtiyZIlsWbNmli/fn1UV1dn9OMH9Vm2bFkWqv28pNekPn36ZLTuPvvsk2jeFltskXjNln4+AQAAAADtj3AqAAAAAAC0gEwDYq05nDp79uy4++6748knn4ylS5c2y54VFRVZD9ltt912DTpvm222SRxO/eCDDxq0R1syc+bMjALI48ePb9A+48aNSxxOnTFjRoP2qM/OO++c0fx0oZb6lJeXb3R88ODBMWjQoFiwYMEm11q9enXcddddcdddd31yrE+fPjFkyJDYcsstY+jQobH11lvH6NGjo3fv3olrbMuSPmciIh599NF49NFHm7CayCjIncROO+2U0fySkpLEczf13EzynPxYnz59Mup297Ftttkm43Pai5Z8bFuTsWPHRmFhYeL5mVx/169f35CSGm3HHXeMTp06RUVFxSbnLlmyJG6//fa4/fbbI+I/oe0tttgiBg8e/Ml1feTIkTF69Ojo1q1b0xbeBDK5Rjfm80NSqVQqZs6cGV/4whc+N5ZJF84xY8YknvuxvLy8GDlyZEYdWpvCwQcfHF27dm2StV966aV44IEH4oUXXmiW61CS11hDJA2YZ3o/Jn3edOrUKfGaLXWdAwAAAADaL+FUAAAAAABoAZl2zmmu0GcmlixZEldddVU89dRTje6GmqnKysqsrzlixIgGnTd8+PB45plnEs0tLS1t0B5tSdKg7se23nrrBu2z1VZbJZ67ePHiSKVSkZOT06C9/lumndoyCQ0keS1985vfjCuuuCKjGj62dOnSWLp0aUyePPkzxwcOHBi77LJL7LXXXrHXXntlFP5qS5J2sG0umb5eNiXT52YmIf9NPTcz6Ug3ZMiQxHM/bejQoQ06rz1oyce2NWnK629LKSgoiK9//etx6623ZnxuXV1dLFq0KBYtWhT//ve/Pzmek5MTQ4cOjV133TX23nvv2H333SM/v3V/PSSVSmV0jW6Ozw8R/3nf+O9wallZWUZhysZc81o6nLrXXntlfc0333wzLr/88nj33XezvvbGNMWfHz5+rSWRyWerTNZtSJd3AAAAAIBsyW3pAgAAAAAAYHPUv3//jOa/+eabTVNIA02aNCkOO+ywePLJJ1sk1FJXV5f1NRvaYSyT89auXdugPdqSlStXJp5bUFAQXbp0adA+PXv2TDy3qqoq1q1b16B9/lunTp0y6kgYEdGhQ4es7P2xY489tsGhnPosXLgw7r///jj77LNj9913j5/97GetMhTfWJk8P5vD6tWrs7peph1wsxlWyySolUk3y0/L9LXXnrTkY9uatNf74bvf/W7GP1yyMalUKubOnRt//etf4zvf+U7sscce8etf/7pVfw5Zt25dVFdXJ57fkO7LEf+5jmQSElyxYsXnjmXa5bMtX/PGjh2b1fVuv/32+MY3vtHswdSIpgniFxUVRceOHRPNzeR6lMm6AAAAAAAtSTgVAAAAAABaQKYdlN56662oqalpmmIyNG3atPjud7+b9VBVSysuLm7y8zZs2NCgPdqSioqKxHMbep9HZB7YyKSubO4bEZGbm91/kuvQoUP88Y9/jIEDB2Z13Y+VlZXFnXfeGRMmTIiHHnqoSfZoKU3RNa0xsn1NyPT5mc3nZibvUQ3tzNsWumA2lZZ8bFuTTO+HttJRsFu3bnHrrbc2+IcyNmXlypVx0003xYQJE+LFF19skj0aK9P36eb6DJHuOl1bW5vRfg0NGbZ0F/P8/PyMf1BnY+6888646qqrMr7/WrNMulTn5OQ0yboAAAAAAC2pff6LFAAAAAAAtHKZdiEqLy+P1157rYmqSW7Dhg1x3nnnZS3o15o09IvymZzXXsNCn5ZJV6rG3B8tFTgqKCjI+JymqLVv375x7733xkEHHZT1tT9WXl4eF110UTz88MNNtsfmLtsBnUyfn9l8bmbSIbih7yGZditsT1rysW1NMr0f2tL77lZbbRX33Xdf7Lbbbk22x8qVK+OMM86IV155pcn2aKhMu1pmEvT7b5k8L9LVlWlH9Ib+EEFLX/Oy2bl1+vTp8Ytf/CJr67UWDflc2JLrAgAAAABkW9v5lxgAAAAAAGhHRo4cmXEHuDvuuKOJqknuL3/5SyxatKily2gS69evb/LzGto5qy3JpMtXY0IXZWVlGc1vj/d9jx494te//nU8+OCDceSRRzaqi1x9UqlUXHLJJbFw4cKsr90S2uPzoLXo3Llz4rmZvn4/tmbNmgadB23FoEGD4vbbb4877rgjJkyY0CTXrKqqqjjvvPMa/DpsKpne1ub6DJHuc02moc1169ZlNP9ja9eubdB52ZLNzxW/+tWvorq6OmvrAQAAAADQOuS3dAEAAAAAALA5ys/Pjz322COefvrpxOf84x//iA8//DAGDx7chJVt3D333JPR/N69e8dRRx0Vu+66awwdOjS6du0ahYWFabtdffnLX27R4OuKFSua/Lzu3bs3aI+2pFu3bonnVlRUxIYNGzIKtH5s9erViefm5eVltftXa7PNNtvEVVddFZdddlm89tprMWnSpHjzzTdjxowZGd1P9amuro7f/e53cfXVVze+2Ba2ObwGW0qfPn0Sz/3ggw8atMfs2bMbdB60NbvsskvssssuUV5eHq+88kq89tprMW3atJgxY0aDf0zj01atWhW33XZbnHXWWVmoNjs6d+4ceXl5iTtKN/T9raqqKqPuzV27dv3cscLCwujatWviwPzmfs1bvHhx/Otf/8ronFGjRsXhhx8e48ePj0GDBkVJSUnabqILFy6MfffdN1ulAgAAAACQIeFUAAAAAABoIQcccEBG4dS6urq49NJLY+LEiZGbm9uElaU3f/78mDdvXuL5Bx10UFx11VWJO8Ru2LChgZVlx8yZM2PvvffO+LxZs2Ylntu3b9+M129rtthii4zmz5o1K7bbbruM98n0fs/Pb///LFZQUBC777577L777p8cW7FiRXz44YexaNGiWLRoUXz44YcxZ86ceP/996Oqqirx2k8//XT87Gc/a/P344ABAxLPPfPMM1tVcKu1GzJkSOK5y5Yti2XLlkXv3r0z2uOdd97JsCpo24qKimLffff9JHyXSqVi6dKlMX/+/Fi0aFEsXLgw5s+fHzNnzoxZs2ZFTU1N4rUfe+yxVnWNy8/Pjz59+sTixYsTzc/kc8CnzZ49O1KpVOL5/fv3T3t8yJAhMW3atERrvPfee4n3+1hlZWWDb2Nr89JLL0VdXV3i+RdeeGF861vfSjS3srKyoWUBAAAAAJAFbftfjwEAAAAAoA3bb7/9olu3bhl1fpo8eXLcfPPN8b3vfa9JaiorK6u3w2QmX6wfMGBA/PKXv0zb4SidqqqqWLVqVeL1m8Kbb76Z8Tm1tbUZhaVGjBiR8R5tTaa38e23325QODWT+3348OEZr99e9OzZM3r27Bnjx4//zPGKiop47rnn4pprroklS5Zscp3169fHW2+99bl12pqtttoq8VxByMxss802Gc1/5pln4hvf+Ebi+VVVVfHss89mWha0Kzk5OdG3b9/o27dv7LTTTp8ZW7duXTz22GNx7bXXxtq1aze51gcffBClpaWt6oczhg8fnjic+vbbbzdoj0yv7fV9rhk9enTicOqkSZNi3bp10blz58T7Pvfccy3+wy3Z8v777yee+5WvfCVxMDUiorS0tCElAQAAAACQJc3/s+oAAAAAAEBERHTq1CmOOeaYjM/77W9/G3/5y1+yWktdXV3cc889ccIJJ9Q7J0mA7WP77LNP4mBqRMSrr76aUUelpvDiiy9GWVlZRudMnjw5o1Dttttum2he0u6UmXRIay69e/fOqDtlJt2DP1ZaWppRmHj77bfPeI/2rlOnTvHVr341br755sTnzJ8/vwkrah477LBD4rn//ve/Wzw035aMGTOm3h83SOeuu+7K6Lp/3333JQrcNVQmXYFra2ubrA5oqM6dO8dxxx0XV199deJzklzXM3ltNPZzSSbX6LfffjtxkPXTnnzyycRzBwwYEL169Uo7tssuuyRep7q6Ou65557E81OpVEycODHx/NYuk8dpwoQJGa3973//O9NyAAAAAADIIuFUAAAAAABoQaecckpGXZQi/hMkvfzyy+P//u//orq6ulH7p1KpeOGFF+Lwww+Pn/zkJxsN/pSXlydet1OnThnVcfvtt2c0vylUVVXFnXfemdE5f/rTnxLPLSgoSNx1sri4ONG8VatWtcqA6pe+9KXEcydNmhQzZszIaP0///nPkUqlmqSezc2oUaPqDd78t/YQ1Ozbt29svfXWieZWVVXFLbfc0iR1vPLKK/HWW281ydotJT8/P/bcc8/E82fMmBF33HFHorkfffRR/OY3v2loaYkkve5GRCxbtqwJK4HGyeQ9L8l1PZPXxvLlyxPPTSeT2lOpVMaf22bNmhWvvvpqVur50pe+lNEPsdx4442xaNGiRHP/+te/ZvQjHK1dRUVF4rmZ/Bli3bp1cd999zWkJAAAAAAAskQ4FQAAAAAAWlCPHj3ijDPOaNC5t9xyS3zlK1+Jxx9/PKOgXsR/wgh//vOf45BDDonTTz89UTiwqKgo8frvvPNO4rl/+9vf4uWXX048vyn94Q9/iDlz5iSa+/jjj8eLL76YeO3dd989CgsLE81N2n2wqqoqXnvttcQ1NJeDDjoo8dy6urq44oorEods58yZk1GYuV+/fhl1YmsLfvSjH2Xtcd+wYUPibpSNDcO3FgcffHDiubfffnv885//zMq+1dXV8dBDD8Vhhx0W3/zmN2P27NlZWbc1OfzwwzOa/4tf/CLuv//+jc758MMP45RTTonVq1c3vLAEMvmhiJdeeqkJK2Fzs2LFivif//mfeO+997Ky3tKlSxPPTfLem8lrY9q0abFu3brE8//bDjvsEFtssUXi+XfccUfMnDkz0dza2tq44oorMup8vLEunl26dIl999038VplZWVxyimnbLJb7X333RdXXnll4nXbgqb4M0QqlYqf/vSn7eKHMwAAAAAA2jLhVAAAAAAAaGEnnXRSjBs3rkHnfvjhh3HeeefFXnvtFZdeemk8+eST8f7770dpaWlUVVVFVVVVrF69OmbMmBHPPPNM/PrXv46vf/3rsfvuu8cVV1wRs2bNSrxX0u6KEf/pCvj8889vct5f/vKXuOKKKxKv29TKy8vj1FNPjenTp2903rPPPhsXXXRRRmsfccQRief27ds38dxLL700Xn755YzCFk1t1113jWHDhiWe/9prr8VFF10UVVVVG503f/78OPXUUzc579O+8Y1vRE5OTuL5bcGjjz4aJ5xwQhx00EHxhz/8YZPP14256aabEt+fffr0afA+rckxxxwTHTt2TDS3trY2vv/978edd94ZdXV1Ge9VXV0dL730UvzkJz+JPffcMy688MJGPV6t3Z577hkjRoxIPL+uri4uvvji+Na3vhWPP/54LFmy5JP3ralTp8bVV18dhx56aMybN6/piv7/ZXLdfeONN+Kqq67KKAQI9amuro677747jjjiiDjqqKPijjvuaPBzPpVKxW9/+9vE83v37r3JOb169Yq8vLxE65WXl8fZZ5+dODD633JycuIb3/hG4vnV1dVx2mmnxQcffLDJeZdccklMmjQp8dpDhw6NL37xixudc8oppyReL+I/n9u/+tWvxlVXXRVTpkyJVatWRVVVVSxZsiSeeOKJ+Na3vhWXXHJJq/pMlw2Z/Bli4sSJm+wwW1lZGRdffHE8+uijjS0NAAAAAIBGym/pAgAAAAAAYHOXn58f1157bRxxxBGxZs2aBq1RWloa9957b9x7771Zru7/ySRwFBFx9tlnx6mnnhrHHnts9O/f/5PjlZWV8eqrr8af/vSnjEICzWXx4sXxta99LY466qg4+OCDY8SIEdG5c+dYtWpVvPPOO/Hggw/GM888k9GagwYNiv322y/x/DFjxsSUKVMSzV24cGF861vfio4dO0a/fv2isLAwbRjzV7/6VUaB0cbIycmJM844Iy644ILE5zzyyCPx7rvvxve///3YZ599ori4+JOxhQsXxsMPPxy33HJLVFRUJF6ze/fu8fWvfz2j2tuSuXPnxrXXXhvXXntt9OnTJ770pS/F6NGjY/jw4TFixIh6w3br16+PN954I/7yl7/EP/7xj8T7ZRLea8169uwZ3/jGN+K2225LNL+6ujp+9rOfxd/+9rc48sgjY88994xhw4ZFfv5n/6k1lUrFypUr47333ot333033n333Zg0aVKDr+ttUU5OTpx77rlx5plnZnTeyy+/3OIdtLfZZpuM5t9+++1x++23R69evaJbt26fez5EROyyyy5x8cUXZ6tENgPvvPNOvPPOO3HllVfGoEGD4otf/GKMGjUqRowYEcOHD4+ePXumPW/NmjXx6quvxu23357480PEf7qLb0rHjh1j+PDhiQOn//73v+OQQw6Jzp07R69evdL+GEBxcXH89a9/TXv+17/+9bjtttti5cqVifYrLS2Nww8/PL71rW/F4YcfHoMHD/5krKKiIv75z3/GDTfckHFg9nvf+94mf9xi++23jy9/+cuJfpDlY5WVlZ9cPzYXw4cPTzx39erVccwxx8Q555wTBx98cJSUlHwytmrVqnjuuefiD3/4Q3z44YdNUSoAAAAAABkSTgUAAAAAgFZgwIABceONN8app54aGzZsaOly0ho1alT06dMncae46urq+P3vfx+///3vo1evXtG9e/coLy+PZcuWZdT5siVUV1fHXXfdFXfddVdW1jv77LMTdx2LiBg7dmzGe1RWVm70i/qVlZUZr9kYhxxySNx9993x2muvJT5n7ty5cf7550d+fn707NkzSkpKYuXKlbFq1aoG1fCDH/wgunTp0qBz25qlS5fGAw888Jlj+fn5UVJSEsXFxVFcXBy1tbWxdu3aWL58eaRSqYzWLykpifHjx2ez5BZ15plnxuOPPx6lpaWJz5kzZ05cc801cc0110RBQcEnz9Hq6upYvXp1rFu3rt11u2uI/fffP/bee++Mgs+tQbdu3WLw4MEZB56WL18ey5cvTzs2YMCAbJTGZmrBggVx9913f+ZYhw4dPrmuFxUVRU1NTaxatapB75NDhgyJQYMGJZq7/fbbZxzuXLduXaxbty7tWOfOnes9r3PnznH++efHJZdcknivDRs2xI033hg33nhjdOvWLXr06BEVFRWxfPnyqK6uzqjuiIjx48fHoYcemmjuJZdcEq+++mqUl5dnvM/mYs8994yf//zniecvX748fvKTn8T//M//RP/+/aOoqCjWrl0by5Yta1AXcwAAAAAAmk5uSxcAAAAAAAD8x4477hi//e1v03aYai0OOeSQBp23fPnymDVrVixatChtMLVDhw7RqVOnxpbXKu26666JAw4f23fffT/TObSt+uUvfxldu3bN+LyampooLS2NOXPmNDiYuu+++8YxxxzToHPbi5qamli9enUsWrQoZs6cGXPmzIlly5ZlHEyNiNhvv/1a9bUpUyUlJXHdddel7XaZRFVVVSxevDhmzZoV8+bNi9WrVwumfspVV13VJJ12DzzwwKyv+WmZXquhuVVXV8eqVati4cKFMXPmzJg7d26D3ye/8pWvJJ7b3K+Nr33tazFhwoQGnbt69eqYO3d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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def captions_length(data):\n", " plt.figure(figsize=(15, 7), dpi=300)\n", " sns.set_style('darkgrid')\n", " sns.histplot(x=[len(x.split(' ')) for x in data], kde=True, binwidth=1)\n", " plt.title('Captions length histogram', fontsize=15, fontweight='bold')\n", " plt.xticks(fontweight='bold')\n", " plt.yticks(fontweight='bold')\n", " plt.xlabel('Length', fontweight='bold')\n", " plt.ylabel('Freaquency', fontweight='bold')\n", " plt.show()\n", "\n", "captions_length(cleaned_captions)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "execution": { "iopub.execute_input": "2025-07-01T17:00:05.668948Z", "iopub.status.busy": "2025-07-01T17:00:05.668306Z", "iopub.status.idle": "2025-07-01T17:00:06.101597Z", "shell.execute_reply": "2025-07-01T17:00:06.100936Z", "shell.execute_reply.started": "2025-07-01T17:00:05.668919Z" }, "id": "0497qJHIy6Dy", "outputId": "bcd9b1b7-a489-48d5-961c-3d0d4164e290", "trusted": true }, "outputs": [ { "data": { "text/plain": [ "8586" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tokenizer = tokenize_captions(cleaned_captions)\n", "vocab_size = len(tokenizer.word_index) + 1\n", "vocab_size\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-07-01T18:01:12.201584Z", "iopub.status.busy": "2025-07-01T18:01:12.201001Z", "iopub.status.idle": "2025-07-01T18:01:12.219679Z", "shell.execute_reply": "2025-07-01T18:01:12.219117Z", "shell.execute_reply.started": "2025-07-01T18:01:12.201562Z" }, "id": "PHLxDb9hSBuT", "outputId": "df0054fb-0bed-494c-aa28-6531d032cd62", "trusted": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#save the tokenizer for later inference\n", "import pickle\n", "\n", "with open('/kaggle/working/tokenizer.pkl', 'wb') as f:\n", " pickle.dump(tokenizer, f)\n", "\n", "#load\n", "with open('/kaggle/working/tokenizer.pkl', 'rb') as f:\n", " tok_test = pickle.load(f)\n", "\n", "tok_test" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-07-01T18:05:19.804354Z", "iopub.status.busy": "2025-07-01T18:05:19.803764Z", "iopub.status.idle": "2025-07-01T18:05:19.847094Z", "shell.execute_reply": "2025-07-01T18:05:19.846553Z", "shell.execute_reply.started": "2025-07-01T18:05:19.804328Z" }, "id": "n-zMCNAuSBuU", "outputId": "e4bb1019-e050-46f0-d068-7221bfda9e2d", "trusted": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#also save to json for portability\n", "# Save tokenizer to JSON\n", "tokenizer_json = tokenizer.to_json()\n", "with open('/kaggle/working/tokenizer.json', 'w', encoding='utf-8') as f:\n", " f.write(tokenizer_json)\n", "from tensorflow.keras.preprocessing.text import tokenizer_from_json\n", "import json\n", "\n", "# Load tokenizer from JSON\n", "with open('/kaggle/working/tokenizer.json', 'r', encoding='utf-8') as f:\n", " tokenizer_json = f.read()\n", "\n", "toke = tokenizer_from_json(tokenizer_json)\n", "toke" ] }, { "cell_type": "markdown", "metadata": { "id": "853b42ed" }, "source": [ "## 2. Image Feature Extraction" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "execution": { "iopub.execute_input": "2025-07-01T17:00:06.102544Z", "iopub.status.busy": "2025-07-01T17:00:06.102282Z", "iopub.status.idle": "2025-07-01T17:00:08.915198Z", "shell.execute_reply": "2025-07-01T17:00:08.914596Z", "shell.execute_reply.started": "2025-07-01T17:00:06.102526Z" }, "id": "1E4jwqCVy7Ci", "outputId": "4b79c31b-2f80-485d-a8e7-008f00d8a3d5", "trusted": true }, "outputs": [ { "data": { "text/plain": [ "('1000268201_693b08cb0e.jpg\\tstart a child in a pink dress is climbing up a set of stairs in an entry way end\\n',\n", " '1001773457_577c3a7d70.jpg\\tstart a black dog and a spotted dog are fighting end\\n',\n", " '1042590306_95dea0916c.jpg\\tstart a man and woman pose for the camera while another man looks on end\\n',\n", " 6877.0,\n", " 1092.0,\n", " 122.0)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "all_image_ids = os.listdir(images_directory)\n", "\n", "train_image_ids, val_image_ids = train_test_split(all_image_ids, test_size=0.15, random_state=42)\n", "val_image_ids, test_image_ids = train_test_split(val_image_ids, test_size=0.1, random_state=42)\n", "\n", "train_captions, val_captions, test_captions = [], [], []\n", "for caption in captions_IDs:\n", " image_id, _ = caption.split('\\t')\n", "\n", " if image_id in train_image_ids:\n", " train_captions.append(caption)\n", "\n", " elif image_id in val_image_ids:\n", " val_captions.append(caption)\n", "\n", " elif image_id in test_image_ids:\n", " test_captions.append(caption)\n", "\n", " else:\n", " print('Unknown image ID !')\n", "\n", "train_captions[0], val_captions[0], test_captions[0], len(train_captions)/5, len(val_captions)/5, len(test_captions)/5\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-07-01T21:40:08.602754Z", "iopub.status.busy": "2025-07-01T21:40:08.601978Z", "iopub.status.idle": "2025-07-01T21:40:10.222761Z", "shell.execute_reply": "2025-07-01T21:40:10.221971Z", "shell.execute_reply.started": "2025-07-01T21:40:08.602728Z" }, "id": "Qbrv3Fm-y8cR", "trusted": true }, "outputs": [], "source": [ "def preprocess_image(image_path):\n", " img = load_img(image_path, target_size=(299, 299))\n", " img = img_to_array(img)\n", " img = np.expand_dims(img, axis=0)\n", " img = tf.keras.applications.inception_v3.preprocess_input(img)\n", " return img\n", "\n", "def extract_image_features(model, image_path):\n", " img = preprocess_image(image_path)\n", " features = model.predict(img, verbose=0)\n", " return features\n", "\n", "inception_v3_model = InceptionV3(weights = 'imagenet', input_shape=(299, 299, 3))\n", "inception_v3_model.layers.pop()\n", "inception_v3_model = Model(inputs=inception_v3_model.inputs, outputs=inception_v3_model.layers[-2].output)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "b2da199159404d448ae8ba13821c5f91", "b06fe45bedbb4cfcbed13221da0f310d", "9241db4ae59443e699e961bed092e30a", "7ea89af5bc924d60a3c0de3d0da4bf5f", "95100c1962114e2ba824b09cbbdd9d65", "7162d9e884c743848797676cb9736b4c", "f5ccda6522e04e3d86e130f838bc86d5", "d771633d9cd7481bbe613099e4a9e5fe", "ba0a1b76c2d34aa58a6d19c05e1dde80", "3f143ee29c7643a58822c4e23cbd8e22", "a7ec9a9e6dce405f8edb30b1415c1dbb", "13a664e157db4deda4dab5e749dc04ef" ] }, "execution": { "iopub.execute_input": "2025-07-01T17:00:13.774014Z", "iopub.status.busy": "2025-07-01T17:00:13.77351Z", "iopub.status.idle": "2025-07-01T17:14:26.297575Z", "shell.execute_reply": "2025-07-01T17:14:26.296793Z", "shell.execute_reply.started": "2025-07-01T17:00:13.773984Z" }, "id": "p3lIC5Khy9mC", "outputId": "839f4407-3571-442f-86dd-094c645a4b13", "trusted": true }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "13a664e157db4deda4dab5e749dc04ef", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/8091 [00:00Model: \"Image_Captioning\"\n", "\n" ], "text/plain": [ "\u001b[1mModel: \"Image_Captioning\"\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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              "┃ Layer (type)               Output Shape                   Param #  Connected to           ┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
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              "│ (InputLayer)              │                        │                │                        │\n",
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              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
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return model\n", "\n", "caption_model = build_model(vocab_size, max_caption_length, cnn_output_dim)\n", "\n", "optimizer = Adam(learning_rate=0.01, clipnorm=1.0)\n", "caption_model.compile(loss='categorical_crossentropy', optimizer=optimizer)\n", "\n", "caption_model.summary()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 874 }, "execution": { "iopub.execute_input": "2025-07-01T17:14:26.556875Z", "iopub.status.busy": "2025-07-01T17:14:26.556694Z", "iopub.status.idle": "2025-07-01T17:14:26.777303Z", "shell.execute_reply": "2025-07-01T17:14:26.776538Z", "shell.execute_reply.started": "2025-07-01T17:14:26.55686Z" }, "id": "y3d2FpUHzB3U", "outputId": "d9863ac4-c38d-4deb-bccd-7c18a8af89fc", "trusted": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plot_model(caption_model)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "execution": { "iopub.execute_input": "2025-07-01T17:14:26.778553Z", "iopub.status.busy": "2025-07-01T17:14:26.778328Z", "iopub.status.idle": "2025-07-01T17:21:37.328154Z", "shell.execute_reply": "2025-07-01T17:21:37.327399Z", "shell.execute_reply.started": "2025-07-01T17:14:26.778537Z" }, "id": "-R7wg17dy_J7", "outputId": "bdd158b2-be87-47f2-f1d4-6fb96453ab5e", "trusted": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/15\n", "\u001b[1m127/127\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m59s\u001b[0m 416ms/step - loss: 5.2068 - val_loss: 3.7348 - learning_rate: 0.0055\n", "Epoch 2/15\n", "\u001b[1m127/127\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 416ms/step - loss: 3.3704 - val_loss: 3.3596 - learning_rate: 0.0030\n", "Epoch 3/15\n", "\u001b[1m127/127\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 423ms/step - loss: 2.9723 - val_loss: 3.2542 - learning_rate: 0.0017\n", "Epoch 4/15\n", "\u001b[1m127/127\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m55s\u001b[0m 433ms/step - loss: 2.7517 - val_loss: 3.2249 - learning_rate: 9.0718e-04\n", "Epoch 5/15\n", "\u001b[1m127/127\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m55s\u001b[0m 432ms/step - loss: 2.6226 - val_loss: 3.2236 - learning_rate: 4.9787e-04\n", "Epoch 6/15\n", "\u001b[1m127/127\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m52s\u001b[0m 410ms/step - loss: 2.5422 - val_loss: 3.2239 - learning_rate: 2.7324e-04\n", "Epoch 7/15\n", "\u001b[1m127/127\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m52s\u001b[0m 411ms/step - loss: 2.4956 - val_loss: 3.2279 - learning_rate: 1.4996e-04\n", "Epoch 8/15\n", "\u001b[1m127/127\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m51s\u001b[0m 406ms/step - loss: 2.4639 - val_loss: 3.2313 - learning_rate: 8.2297e-05\n" ] } ], "source": [ "# Define output_signature for the generator\n", "output_signature = (\n", " (\n", " tf.TensorSpec(shape=(None, cnn_output_dim), dtype=tf.float32), # X_images\n", " tf.TensorSpec(shape=(None, max_caption_length), dtype=tf.int32), # X_captions\n", " ),\n", " tf.TensorSpec(shape=(None, vocab_size), dtype=tf.float32) # y\n", ")\n", "\n", "# Create TensorFlow datasets from generators\n", "train_dataset = tf.data.Dataset.from_generator(\n", " lambda: data_generator(train_captions, train_image_features, tokenizer, max_caption_length, batch_size_train),\n", " output_signature=output_signature\n", ")\n", "\n", "val_dataset = tf.data.Dataset.from_generator(\n", " lambda: data_generator(val_captions, val_image_features, tokenizer, max_caption_length, batch_size_val),\n", " output_signature=output_signature\n", ")\n", "\n", "# Define callbacks\n", "early_stopping = EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)\n", "\n", "def lr_scheduler(epoch, lr):\n", " return lr * math.exp(-0.6) # Use math.exp instead of tf.math.exp\n", "\n", "lr_schedule = LearningRateScheduler(lr_scheduler)\n", "\n", "# Train the model\n", "history = caption_model.fit(\n", " train_dataset,\n", " steps_per_epoch=len(train_captions) // batch_size_train,\n", " validation_data=val_dataset,\n", " validation_steps=len(val_captions) // batch_size_val,\n", " epochs=15,\n", " callbacks=[early_stopping, lr_schedule]\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-07-01T17:28:20.512117Z", "iopub.status.busy": "2025-07-01T17:28:20.511804Z", "iopub.status.idle": "2025-07-01T17:28:20.870202Z", "shell.execute_reply": "2025-07-01T17:28:20.869399Z", "shell.execute_reply.started": "2025-07-01T17:28:20.512097Z" }, "id": "ouME1Lw7i9Ii", "trusted": true }, "outputs": [], "source": [ "# Save the entire model to a HDF5 file\n", "caption_model.save('caption_model.h5')\n", "caption_model.save('kaggle/working/caption_model.h5')\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 333 }, "execution": { "iopub.execute_input": "2025-07-01T17:21:37.475577Z", "iopub.status.busy": "2025-07-01T17:21:37.475306Z", "iopub.status.idle": "2025-07-01T17:21:37.911853Z", "shell.execute_reply": "2025-07-01T17:21:37.911104Z", "shell.execute_reply.started": "2025-07-01T17:21:37.475558Z" }, "id": "JnqCwzEFzEke", "outputId": "ba16dcb4-e746-4a79-90d6-2d50ba92c429", "trusted": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(15, 7), dpi=200)\n", "sns.set_style('whitegrid')\n", "plt.plot([x+1 for x in range(len(history.history['loss']))], history.history['loss'], color='#E74C3C', marker='o')\n", "plt.plot([x+1 for x in range(len(history.history['loss']))], history.history['val_loss'], color='#641E16', marker='h')\n", "plt.title('Train VS Validation', fontsize=15, fontweight='bold')\n", "plt.xticks(fontweight='bold')\n", "plt.yticks(fontweight='bold')\n", "plt.xlabel('Epoch', fontweight='bold')\n", "plt.ylabel('Loss', fontweight='bold')\n", "plt.legend(['Train Loss', 'Validation Loss'], loc='best')\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": { "id": "53bb0a1e" }, "source": [ "## 4. Model Evaluation and Prediction" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-07-01T17:21:37.912899Z", "iopub.status.busy": "2025-07-01T17:21:37.912637Z", "iopub.status.idle": "2025-07-01T17:21:37.918061Z", "shell.execute_reply": "2025-07-01T17:21:37.917307Z", "shell.execute_reply.started": "2025-07-01T17:21:37.912883Z" }, "id": "eXjuvWMmzFsL", "trusted": true }, "outputs": [], "source": [ "def greedy_generator(image_features):\n", " in_text = 'start '\n", " for _ in range(max_caption_length):\n", " sequence = tokenizer.texts_to_sequences([in_text])[0]\n", " sequence = pad_sequences([sequence], maxlen=max_caption_length, padding='post').reshape((1,max_caption_length))\n", " prediction = caption_model.predict([image_features.reshape(1,cnn_output_dim), sequence], verbose=0)\n", " idx = np.argmax(prediction)\n", " word = tokenizer.index_word[idx]\n", " in_text += ' ' + word\n", " if word == 'end':\n", " break\n", "\n", " in_text = in_text.replace('start ', '')\n", " in_text = in_text.replace(' end', '')\n", "\n", " return in_text" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-07-01T17:21:37.919192Z", "iopub.status.busy": "2025-07-01T17:21:37.918841Z", "iopub.status.idle": "2025-07-01T17:21:37.933077Z", "shell.execute_reply": "2025-07-01T17:21:37.932458Z", "shell.execute_reply.started": "2025-07-01T17:21:37.919171Z" }, "id": "lHCIHJcEzGnz", "trusted": true }, "outputs": [], "source": [ "def beam_search_generator(image_features, K_beams = 3, log = False):\n", " start = [tokenizer.word_index['start']]\n", " start_word = [[start, 0.0]]\n", " for _ in range(max_caption_length):\n", " temp = []\n", " for s in start_word:\n", " sequence = pad_sequences([s[0]], maxlen=max_caption_length, padding='post').reshape((1,max_caption_length))\n", " preds = caption_model.predict([image_features.reshape(1,cnn_output_dim), sequence], verbose=0)\n", " word_preds = np.argsort(preds[0])[-K_beams:]\n", " for w in word_preds:\n", " next_cap, prob = s[0][:], s[1]\n", " next_cap.append(w)\n", " if log:\n", " prob += np.log(preds[0][w]) # assign a probability to each K words\n", " else:\n", " prob += preds[0][w]\n", " temp.append([next_cap, prob])\n", "\n", " start_word = temp\n", " start_word = sorted(start_word, reverse=False, key=lambda l: l[1])\n", " start_word = start_word[-K_beams:]\n", "\n", " start_word = start_word[-1][0]\n", " captions_ = [tokenizer.index_word[i] for i in start_word]\n", " final_caption = []\n", " for i in captions_:\n", " if i != 'end':\n", " final_caption.append(i)\n", " else:\n", " break\n", "\n", " final_caption = ' '.join(final_caption[1:])\n", " return final_caption" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-07-01T17:21:37.933995Z", "iopub.status.busy": "2025-07-01T17:21:37.933759Z", "iopub.status.idle": "2025-07-01T17:21:37.944897Z", "shell.execute_reply": "2025-07-01T17:21:37.944244Z", "shell.execute_reply.started": "2025-07-01T17:21:37.933974Z" }, "id": "kqpXObrkzITr", "trusted": true }, "outputs": [], "source": [ "def BLEU_score(actual, greedy, beam_search):\n", " score_greedy_1 = corpus_bleu(actual, greedy, weights=(0.3, 0.3, 0.3, 0))\n", " score_greedy_2 = corpus_bleu(actual, greedy, weights=(0.25, 0.25, 0.25, 0.25))\n", " score_BS_1 = corpus_bleu(actual, beam_search, weights=(0.3, 0.3, 0.3, 0))\n", " score_BS_2 = corpus_bleu(actual, beam_search, weights=(0.25, 0.25, 0.25, 0.25))\n", "\n", " return [\n", " (f'BLEU-2 Greedy: {round(score_BS_2, 5)}'),\n", " (f'BLEU-1 Greedy: {round(score_BS_1, 5)}'),\n", " (f'Greedy: {greedy[0]}'),\n", " (f'BLEU-2 Beam Search: {round(score_greedy_2, 5)}'),\n", " (f'BLEU-1 Beam Search: {round(score_greedy_1, 5)}'),\n", " (f'Beam Search: {beam_search[0]}')\n", " ]\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "00ade54ac94d445a890015cbd0ec5c1f", "396d9dbabb7f4ce289154d4505acfbce", "eaefb9a44eb94c7898d2d7c2198f6e4b", "ab814a09ad3c4f359c509645d32224f9", "fa6d2d99362e46b1a1c47642b35c0e5e", "b8f3b631cabd4528882dacb7c2c323fb", "c7fa39cc54fa403ab3be28e8e2dba179", "c1d4276e13d24464be6349bb5f4391f1", "85fafa2926bc4c68bbf698f0ae4d59f9", "d59c822681a545b48a4711ad3c650250", "af759a365c1f47ec951d05213dc94cb8", "62d85aefd8d04e068c90f56d93b59b80" ] }, "execution": { "iopub.execute_input": "2025-07-01T17:21:37.945881Z", "iopub.status.busy": "2025-07-01T17:21:37.945662Z", "iopub.status.idle": "2025-07-01T17:23:27.275031Z", "shell.execute_reply": "2025-07-01T17:23:27.274271Z", "shell.execute_reply.started": "2025-07-01T17:21:37.945861Z" }, "id": "u4NgkZuTzJks", "outputId": "7ee7d2c4-be2e-456b-8c14-54687f012f34", "trusted": true }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "62d85aefd8d04e068c90f56d93b59b80", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/122 [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def visualization(data, greedy_caps, beamS_generator, evaluator, num_of_images):\n", " keys = list(data.keys())\n", " images = [np.random.choice(keys) for i in range(num_of_images)] # Randomly selected images\n", " count = 1\n", " fig = plt.figure(figsize=(6,20))\n", " for filename in images:\n", " actual_cap = data[filename]\n", " actual_cap = [x.replace(\"start \", \"\") for x in actual_cap]\n", " actual_cap = [x.replace(\" end\", \"\") for x in actual_cap]\n", "\n", " greedy_cap = greedy_caps[filename]\n", " beamS_cap = beamS_generator(test_image_features[filename])\n", "\n", " caps_with_score = evaluator(actual_cap, [greedy_cap]*(len(actual_cap)), [beamS_cap]*(len(actual_cap)))\n", "\n", " image_load = load_img(images_directory+filename, target_size=(199,199,3))\n", " ax = fig.add_subplot(num_of_images,2,count,xticks=[],yticks=[])\n", " ax.imshow(image_load)\n", " count += 1\n", "\n", " ax = fig.add_subplot(num_of_images,2,count)\n", " plt.axis('off')\n", " ax.plot()\n", " ax.set_xlim(0,1)\n", " ax.set_ylim(0,len(caps_with_score))\n", " for i, text in enumerate(caps_with_score):\n", " ax.text(0,i,text,fontsize=10)\n", " count += 1\n", " plt.show()\n", "\n", "visualization(test_actual_captions, generated_captions, beam_search_generator, BLEU_score, 7)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-07-01T17:24:26.954094Z", "iopub.status.busy": "2025-07-01T17:24:26.953849Z", "iopub.status.idle": "2025-07-01T17:24:27.172469Z", "shell.execute_reply": "2025-07-01T17:24:27.17161Z", "shell.execute_reply.started": "2025-07-01T17:24:26.954075Z" }, "id": "YO8iYpu-i9Iw", "trusted": true }, "outputs": [], "source": [ "!wget https://www.google.com/url?sa=i&url=https%3A%2F%2Fwww.istockphoto.com%2Fphotos%2Fgroup-of-teenage-boys-and-girls-dancing-at-nightclub&psig=AOvVaw0zusEdL6oHWKbtwZWAIl42&ust=1745780341236000&source=images&cd=vfe&opi=89978449&ved=0CBQQjRxqFwoTCNC8jN2w9owDFQAAAAAdAAAAABAT" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "execution": { "iopub.execute_input": "2025-07-01T17:24:27.17435Z", "iopub.status.busy": "2025-07-01T17:24:27.17399Z", "iopub.status.idle": "2025-07-01T17:24:27.378548Z", "shell.execute_reply": "2025-07-01T17:24:27.377646Z", "shell.execute_reply.started": "2025-07-01T17:24:27.174313Z" }, "id": "UKsVSbXBi9Ix", "outputId": "3646fdb3-235b-48ec-c7b4-6d253b56f8bb", "trusted": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "caption_model.h5 model.png\n" ] } ], "source": [ "!ls" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "execution": { "iopub.execute_input": "2025-07-01T17:24:27.380221Z", "iopub.status.busy": "2025-07-01T17:24:27.379912Z", "iopub.status.idle": "2025-07-01T17:24:36.87914Z", "shell.execute_reply": "2025-07-01T17:24:36.878516Z", "shell.execute_reply.started": "2025-07-01T17:24:27.380197Z" }, "id": "nMPzyZ_ti9Ix", "outputId": "8dd24cf1-3d4f-4ee7-8c46-557be02109f4", "trusted": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Greedy Caption: a white dog is playing with a white ball in the water\n", "Beam Search Caption: a white dog is playing with a white ball in its mouth\n" ] } ], "source": [ "\n", "# 2. Preprocess the image\n", "image_path = f'{path}/Images/1012212859_01547e3f17.jpg'\n", "processed_image = preprocess_image(image_path)\n", "\n", "# 3. Extract image features\n", "image_features = inception_v3_model.predict(processed_image, verbose=0)\n", "image_features = image_features.flatten()\n", "\n", "# 4. Generate captions\n", "greedy_caption = greedy_generator(image_features)\n", "print(\"Greedy Caption:\", greedy_caption)\n", "\n", "beam_search_caption = beam_search_generator(image_features)\n", "print(\"Beam Search Caption:\", beam_search_caption)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "execution": { "iopub.execute_input": "2025-07-01T17:24:36.880206Z", "iopub.status.busy": "2025-07-01T17:24:36.879917Z", "iopub.status.idle": "2025-07-01T17:24:36.901729Z", "shell.execute_reply": "2025-07-01T17:24:36.901144Z", "shell.execute_reply.started": "2025-07-01T17:24:36.880186Z" }, "id": "ftKZ7j6Ci9Iz", "outputId": "9954f75e-b5ad-4831-f9ec-762138f1b4bf", "trusted": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Image downloaded as fight.jpg\n" ] } ], "source": [ "import requests\n", "\n", "image_url = \"https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTpmMGdUkQ16NxGZMuMRm2Srfjsj2TYh6MulA&s\"\n", "image_name = \"fight.jpg\" # Choose a filename for the image\n", "\n", "response = requests.get(image_url, stream=True)\n", "response.raise_for_status() # Raise an exception for bad responses\n", "\n", "with open(image_name, 'wb') as file:\n", " for chunk in response.iter_content(chunk_size=8192):\n", " file.write(chunk)\n", "\n", "print(f\"Image downloaded as {image_name}\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-07-01T17:50:37.164341Z", "iopub.status.busy": "2025-07-01T17:50:37.164068Z", "iopub.status.idle": "2025-07-01T17:50:46.900016Z", "shell.execute_reply": "2025-07-01T17:50:46.899246Z", "shell.execute_reply.started": "2025-07-01T17:50:37.16432Z" }, "id": "QMZKpSEFi9I0", "outputId": "50159467-35d6-4e46-ccfc-f6c5498d92fe", "trusted": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Greedy Caption: a basketball player in a white uniform is playing a game\n", "Beam Search Caption: a basketball player in a white uniform is playing a basketball\n" ] } ], "source": [ "\n", "# 2. Preprocess the image\n", "image_path = 'fight.jpg'\n", "processed_image = preprocess_image(image_path)\n", "\n", "# 3. Extract image features\n", "image_features = inception_v3_model.predict(processed_image, verbose=0)\n", "image_features = image_features.flatten()\n", "\n", "# 4. Generate captions\n", "greedy_caption = greedy_generator(image_features)\n", "print(\"Greedy Caption:\", greedy_caption)\n", "\n", "beam_search_caption = beam_search_generator(image_features)\n", "print(\"Beam Search Caption:\", beam_search_caption)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-07-01T17:51:55.659052Z", "iopub.status.busy": "2025-07-01T17:51:55.658765Z", "iopub.status.idle": "2025-07-01T17:51:55.663474Z", "shell.execute_reply": "2025-07-01T17:51:55.662674Z", "shell.execute_reply.started": "2025-07-01T17:51:55.659033Z" }, "id": "HoGekN1iSBud", "trusted": true }, "outputs": [], "source": [ "def predict_image_caption(image_path):\n", " # Step 2: Preprocess the image\n", " processed_image = preprocess_image(image_path)\n", "\n", " # Step 3: Extract image features using the InceptionV3 model\n", " image_features = inception_v3_model.predict(processed_image, verbose=0)\n", " image_features = image_features.flatten()\n", "\n", " # Step 4: Generate captions using both methods\n", " greedy_caption = greedy_generator(image_features)\n", " beam_search_caption = beam_search_generator(image_features)\n", "\n", " # Print captions\n", " print(\"Greedy Caption:\", greedy_caption)\n", " print(\"Beam Search Caption:\", beam_search_caption)\n", "\n", " # Return captions\n", " return greedy_caption, beam_search_caption\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-07-01T19:50:45.412122Z", "iopub.status.busy": "2025-07-01T19:50:45.411453Z", "iopub.status.idle": "2025-07-01T19:50:45.438631Z", "shell.execute_reply": "2025-07-01T19:50:45.437652Z", "shell.execute_reply.started": "2025-07-01T19:50:45.412098Z" }, "id": "pcOC44W0SBud", "outputId": "ddee1e17-987a-4571-d2b6-8d416f34d119", "trusted": true }, "outputs": [ { "ename": "ValueError", "evalue": "Unknown layer: 'NotEqual'. Please ensure you are using a `keras.utils.custom_object_scope` and that this object is included in the scope. See https://www.tensorflow.org/guide/keras/save_and_serialize#registering_the_custom_object for details.", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipykernel_35/643383252.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcaption_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/kaggle/working/caption_model.h5'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/keras/src/saving/saving_api.py\u001b[0m in \u001b[0;36mload_model\u001b[0;34m(filepath, custom_objects, compile, safe_mode)\u001b[0m\n\u001b[1;32m 194\u001b[0m )\n\u001b[1;32m 195\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\".h5\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\".hdf5\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 196\u001b[0;31m return legacy_h5_format.load_model_from_hdf5(\n\u001b[0m\u001b[1;32m 197\u001b[0m \u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcustom_objects\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcustom_objects\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompile\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 198\u001b[0m )\n", "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/keras/src/legacy/saving/legacy_h5_format.py\u001b[0m in \u001b[0;36mload_model_from_hdf5\u001b[0;34m(filepath, custom_objects, compile)\u001b[0m\n\u001b[1;32m 131\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 132\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0msaving_options\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras_option_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muse_legacy_config\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 133\u001b[0;31m model = saving_utils.model_from_config(\n\u001b[0m\u001b[1;32m 134\u001b[0m \u001b[0mmodel_config\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcustom_objects\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcustom_objects\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 135\u001b[0m )\n", "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/keras/src/legacy/saving/saving_utils.py\u001b[0m in \u001b[0;36mmodel_from_config\u001b[0;34m(config, custom_objects)\u001b[0m\n\u001b[1;32m 83\u001b[0m \u001b[0mconfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_find_replace_nested_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"keras.\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"keras.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 84\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 85\u001b[0;31m return serialization.deserialize_keras_object(\n\u001b[0m\u001b[1;32m 86\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 87\u001b[0m \u001b[0mmodule_objects\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mMODULE_OBJECTS\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mALL_OBJECTS\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/keras/src/legacy/saving/serialization.py\u001b[0m in \u001b[0;36mdeserialize_keras_object\u001b[0;34m(identifier, module_objects, custom_objects, printable_module_name)\u001b[0m\n\u001b[1;32m 493\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 494\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m\"custom_objects\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marg_spec\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 495\u001b[0;31m deserialized_obj = cls.from_config(\n\u001b[0m\u001b[1;32m 496\u001b[0m \u001b[0mcls_config\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 497\u001b[0m custom_objects={\n", "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/keras/src/models/model.py\u001b[0m in \u001b[0;36mfrom_config\u001b[0;34m(cls, config, custom_objects)\u001b[0m\n\u001b[1;32m 580\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunctional\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mfunctional_from_config\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 581\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 582\u001b[0;31m return functional_from_config(\n\u001b[0m\u001b[1;32m 583\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcustom_objects\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcustom_objects\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 584\u001b[0m )\n", "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/keras/src/models/functional.py\u001b[0m in \u001b[0;36mfunctional_from_config\u001b[0;34m(cls, config, custom_objects)\u001b[0m\n\u001b[1;32m 549\u001b[0m \u001b[0;31m# First, we create all layers and enqueue nodes to be processed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 550\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mlayer_data\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfunctional_config\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"layers\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 551\u001b[0;31m \u001b[0mprocess_layer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 552\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 553\u001b[0m \u001b[0;31m# Then we process nodes in order of layer depth.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/keras/src/models/functional.py\u001b[0m in \u001b[0;36mprocess_layer\u001b[0;34m(layer_data)\u001b[0m\n\u001b[1;32m 517\u001b[0m \u001b[0;31m# Legacy format deserialization (no \"module\" key)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 518\u001b[0m \u001b[0;31m# used for H5 and SavedModel formats\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 519\u001b[0;31m layer = saving_utils.model_from_config(\n\u001b[0m\u001b[1;32m 520\u001b[0m \u001b[0mlayer_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcustom_objects\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcustom_objects\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 521\u001b[0m )\n", "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/keras/src/legacy/saving/saving_utils.py\u001b[0m in \u001b[0;36mmodel_from_config\u001b[0;34m(config, custom_objects)\u001b[0m\n\u001b[1;32m 83\u001b[0m \u001b[0mconfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_find_replace_nested_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"keras.\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"keras.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 84\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 85\u001b[0;31m return serialization.deserialize_keras_object(\n\u001b[0m\u001b[1;32m 86\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 87\u001b[0m \u001b[0mmodule_objects\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mMODULE_OBJECTS\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mALL_OBJECTS\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/keras/src/legacy/saving/serialization.py\u001b[0m in \u001b[0;36mdeserialize_keras_object\u001b[0;34m(identifier, module_objects, custom_objects, printable_module_name)\u001b[0m\n\u001b[1;32m 471\u001b[0m \u001b[0;31m# In this case we are dealing with a Keras config dictionary.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 472\u001b[0m \u001b[0mconfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0midentifier\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 473\u001b[0;31m (cls, cls_config) = class_and_config_for_serialized_keras_object(\n\u001b[0m\u001b[1;32m 474\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodule_objects\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcustom_objects\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprintable_module_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 475\u001b[0m )\n", "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/keras/src/legacy/saving/serialization.py\u001b[0m in \u001b[0;36mclass_and_config_for_serialized_keras_object\u001b[0;34m(config, module_objects, custom_objects, printable_module_name)\u001b[0m\n\u001b[1;32m 352\u001b[0m )\n\u001b[1;32m 353\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcls\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 354\u001b[0;31m raise ValueError(\n\u001b[0m\u001b[1;32m 355\u001b[0m \u001b[0;34mf\"Unknown {printable_module_name}: '{class_name}'. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 356\u001b[0m \u001b[0;34m\"Please ensure you are using a `keras.utils.custom_object_scope` \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: Unknown layer: 'NotEqual'. Please ensure you are using a `keras.utils.custom_object_scope` and that this object is included in the scope. See https://www.tensorflow.org/guide/keras/save_and_serialize#registering_the_custom_object for details." ] } ], "source": [ "caption_model = load_model('/kaggle/working/caption_model.h5')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-07-01T19:49:34.768653Z", "iopub.status.busy": "2025-07-01T19:49:34.768367Z", "iopub.status.idle": "2025-07-01T19:49:34.972914Z", "shell.execute_reply": "2025-07-01T19:49:34.971835Z", "shell.execute_reply.started": "2025-07-01T19:49:34.768633Z" }, "id": "VNLZO6uWSBue", "outputId": "59b66a6c-e7c9-4f2f-e969-fbbef739fa5c", "trusted": true }, "outputs": [ { "ename": "InvalidArgumentError", "evalue": "Graph execution error:\n\nDetected at node Image_Captioning_1/lstm_1/Assert/Assert defined at (most recent call last):\n File \"\", line 198, in _run_module_as_main\n\n File \"\", line 88, in _run_code\n\n File \"/usr/local/lib/python3.11/dist-packages/colab_kernel_launcher.py\", line 37, in \n\n File \"/usr/local/lib/python3.11/dist-packages/traitlets/config/application.py\", line 992, in launch_instance\n\n File \"/usr/local/lib/python3.11/dist-packages/ipykernel/kernelapp.py\", line 712, in start\n\n File \"/usr/local/lib/python3.11/dist-packages/tornado/platform/asyncio.py\", line 205, in start\n\n File \"/usr/lib/python3.11/asyncio/base_events.py\", line 608, in run_forever\n\n File \"/usr/lib/python3.11/asyncio/base_events.py\", line 1936, in _run_once\n\n File \"/usr/lib/python3.11/asyncio/events.py\", line 84, in _run\n\n File \"/usr/local/lib/python3.11/dist-packages/ipykernel/kernelbase.py\", line 510, in dispatch_queue\n\n File \"/usr/local/lib/python3.11/dist-packages/ipykernel/kernelbase.py\", line 499, in process_one\n\n File \"/usr/local/lib/python3.11/dist-packages/ipykernel/kernelbase.py\", line 406, in dispatch_shell\n\n File \"/usr/local/lib/python3.11/dist-packages/ipykernel/kernelbase.py\", line 730, in execute_request\n\n File \"/usr/local/lib/python3.11/dist-packages/ipykernel/ipkernel.py\", line 383, in do_execute\n\n File \"/usr/local/lib/python3.11/dist-packages/ipykernel/zmqshell.py\", line 528, in run_cell\n\n File \"/usr/local/lib/python3.11/dist-packages/IPython/core/interactiveshell.py\", line 2975, in run_cell\n\n File \"/usr/local/lib/python3.11/dist-packages/IPython/core/interactiveshell.py\", line 3030, in _run_cell\n\n File \"/usr/local/lib/python3.11/dist-packages/IPython/core/async_helpers.py\", line 78, in _pseudo_sync_runner\n\n File \"/usr/local/lib/python3.11/dist-packages/IPython/core/interactiveshell.py\", line 3257, in run_cell_async\n\n File \"/usr/local/lib/python3.11/dist-packages/IPython/core/interactiveshell.py\", line 3473, in run_ast_nodes\n\n File \"/usr/local/lib/python3.11/dist-packages/IPython/core/interactiveshell.py\", line 3553, in run_code\n\n File \"/tmp/ipykernel_35/3044426981.py\", line 5, in \n\n File \"/tmp/ipykernel_35/111999354.py\", line 6, in greedy_generator\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py\", line 117, in error_handler\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/backend/tensorflow/trainer.py\", line 562, in predict\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/backend/tensorflow/trainer.py\", line 259, in one_step_on_data_distributed\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/backend/tensorflow/trainer.py\", line 249, in one_step_on_data\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/backend/tensorflow/trainer.py\", line 104, in predict_step\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py\", line 117, in error_handler\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/layers/layer.py\", line 908, in __call__\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py\", line 117, in error_handler\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/ops/operation.py\", line 46, in __call__\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py\", line 156, in error_handler\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/models/functional.py\", line 182, in call\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/ops/function.py\", line 171, in _run_through_graph\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/models/functional.py\", line 637, in call\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py\", line 117, in error_handler\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/layers/layer.py\", line 908, in __call__\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py\", line 117, in error_handler\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/ops/operation.py\", line 46, in __call__\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py\", line 156, in error_handler\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/layers/rnn/lstm.py\", line 584, in call\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/layers/rnn/rnn.py\", line 402, in call\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/layers/rnn/lstm.py\", line 551, in inner_loop\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/backend/tensorflow/rnn.py\", line 841, in lstm\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/backend/tensorflow/rnn.py\", line 874, in _cudnn_lstm\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/backend/tensorflow/rnn.py\", line 557, in _assert_valid_mask\n\nassertion failed: [You are passing a RNN mask that does not correspond to right-padded sequences, while using cuDNN, which is not supported. With cuDNN, RNN masks can only be used for right-padding, e.g. `[[True, True, False, False]]` would be a valid mask, but any mask that isn\\'t just contiguous `True`\\'s on the left and contiguous `False`\\'s on the right would be invalid. You can pass `use_cudnn=False` to your RNN layer to stop using cuDNN (this may be slower).]\n\t [[{{node Image_Captioning_1/lstm_1/Assert/Assert}}]] [Op:__inference_one_step_on_data_distributed_450121]", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mInvalidArgumentError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipykernel_35/2503622370.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpredict_image_caption\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/kaggle/working/fight.jpg'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/tmp/ipykernel_35/4218869378.py\u001b[0m in \u001b[0;36mpredict_image_caption\u001b[0;34m(image_path)\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;31m# Step 4: Generate captions using both methods\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mgreedy_caption\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgreedy_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_features\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 11\u001b[0m \u001b[0mbeam_search_caption\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbeam_search_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_features\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/tmp/ipykernel_35/253515229.py\u001b[0m in \u001b[0;36mgreedy_generator\u001b[0;34m(image_features)\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0msequence\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtexts_to_sequences\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0min_text\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0msequence\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpreprocessing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msequence\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpad_sequences\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msequence\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaxlen\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_caption_length\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmax_caption_length\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mprediction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcaption_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mimage_features\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcnn_output_dim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msequence\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 15\u001b[0m \u001b[0midx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprediction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0mword\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex_word\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 120\u001b[0m \u001b[0;31m# To get the full stack trace, call:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[0;31m# `keras.config.disable_traceback_filtering()`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 122\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 123\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 124\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m\" name: \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_status_to_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 59\u001b[0;31m \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 60\u001b[0m \u001b[0mkeras_symbolic_tensors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minputs\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m_is_keras_symbolic_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 61\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mkeras_symbolic_tensors\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mInvalidArgumentError\u001b[0m: Graph execution error:\n\nDetected at node Image_Captioning_1/lstm_1/Assert/Assert defined at (most recent call last):\n File \"\", line 198, in _run_module_as_main\n\n File \"\", line 88, in _run_code\n\n File \"/usr/local/lib/python3.11/dist-packages/colab_kernel_launcher.py\", line 37, in \n\n File \"/usr/local/lib/python3.11/dist-packages/traitlets/config/application.py\", line 992, in launch_instance\n\n File \"/usr/local/lib/python3.11/dist-packages/ipykernel/kernelapp.py\", line 712, in start\n\n File \"/usr/local/lib/python3.11/dist-packages/tornado/platform/asyncio.py\", line 205, in start\n\n File \"/usr/lib/python3.11/asyncio/base_events.py\", line 608, in run_forever\n\n File \"/usr/lib/python3.11/asyncio/base_events.py\", line 1936, in _run_once\n\n File \"/usr/lib/python3.11/asyncio/events.py\", line 84, in _run\n\n File \"/usr/local/lib/python3.11/dist-packages/ipykernel/kernelbase.py\", line 510, in dispatch_queue\n\n File \"/usr/local/lib/python3.11/dist-packages/ipykernel/kernelbase.py\", line 499, in process_one\n\n File \"/usr/local/lib/python3.11/dist-packages/ipykernel/kernelbase.py\", line 406, in dispatch_shell\n\n File \"/usr/local/lib/python3.11/dist-packages/ipykernel/kernelbase.py\", line 730, in execute_request\n\n File \"/usr/local/lib/python3.11/dist-packages/ipykernel/ipkernel.py\", line 383, in do_execute\n\n File \"/usr/local/lib/python3.11/dist-packages/ipykernel/zmqshell.py\", line 528, in run_cell\n\n File \"/usr/local/lib/python3.11/dist-packages/IPython/core/interactiveshell.py\", line 2975, in run_cell\n\n File \"/usr/local/lib/python3.11/dist-packages/IPython/core/interactiveshell.py\", line 3030, in _run_cell\n\n File \"/usr/local/lib/python3.11/dist-packages/IPython/core/async_helpers.py\", line 78, in _pseudo_sync_runner\n\n File \"/usr/local/lib/python3.11/dist-packages/IPython/core/interactiveshell.py\", line 3257, in run_cell_async\n\n File \"/usr/local/lib/python3.11/dist-packages/IPython/core/interactiveshell.py\", line 3473, in run_ast_nodes\n\n File \"/usr/local/lib/python3.11/dist-packages/IPython/core/interactiveshell.py\", line 3553, in run_code\n\n File \"/tmp/ipykernel_35/3044426981.py\", line 5, in \n\n File \"/tmp/ipykernel_35/111999354.py\", line 6, in greedy_generator\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py\", line 117, in error_handler\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/backend/tensorflow/trainer.py\", line 562, in predict\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/backend/tensorflow/trainer.py\", line 259, in one_step_on_data_distributed\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/backend/tensorflow/trainer.py\", line 249, in one_step_on_data\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/backend/tensorflow/trainer.py\", line 104, in predict_step\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py\", line 117, in error_handler\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/layers/layer.py\", line 908, in __call__\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py\", line 117, in error_handler\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/ops/operation.py\", line 46, in __call__\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py\", line 156, in error_handler\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/models/functional.py\", line 182, in call\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/ops/function.py\", line 171, in _run_through_graph\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/models/functional.py\", line 637, in call\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py\", line 117, in error_handler\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/layers/layer.py\", line 908, in __call__\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py\", line 117, in error_handler\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/ops/operation.py\", line 46, in __call__\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py\", line 156, in error_handler\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/layers/rnn/lstm.py\", line 584, in call\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/layers/rnn/rnn.py\", line 402, in call\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/layers/rnn/lstm.py\", line 551, in inner_loop\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/backend/tensorflow/rnn.py\", line 841, in lstm\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/backend/tensorflow/rnn.py\", line 874, in _cudnn_lstm\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/backend/tensorflow/rnn.py\", line 557, in _assert_valid_mask\n\nassertion failed: [You are passing a RNN mask that does not correspond to right-padded sequences, while using cuDNN, which is not supported. With cuDNN, RNN masks can only be used for right-padding, e.g. `[[True, True, False, False]]` would be a valid mask, but any mask that isn\\'t just contiguous `True`\\'s on the left and contiguous `False`\\'s on the right would be invalid. You can pass `use_cudnn=False` to your RNN layer to stop using cuDNN (this may be slower).]\n\t [[{{node Image_Captioning_1/lstm_1/Assert/Assert}}]] [Op:__inference_one_step_on_data_distributed_450121]" ] } ], "source": [ "predict_image_caption('/kaggle/working/fight.jpg')" ] }, { "cell_type": "markdown", "metadata": { "id": "bffc22e5" }, "source": [ "## 5. Gradio Interface (Optional)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-07-01T17:24:46.419505Z", "iopub.status.busy": "2025-07-01T17:24:46.419239Z", "iopub.status.idle": "2025-07-01T17:24:46.748671Z", "shell.execute_reply": "2025-07-01T17:24:46.747698Z", "shell.execute_reply.started": "2025-07-01T17:24:46.419483Z" }, "id": "1ffIeuGMi9I2", "outputId": "b738d264-15d8-47d3-9ba6-e9198037f1e7", "trusted": true }, "outputs": [ { "ename": "ModuleNotFoundError", "evalue": "No module named 'gradio'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipykernel_35/4245231381.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# prompt: create gradio app to load the model and run it model is saved as caption_model.h5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mgradio\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mgr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtensorflow\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload_model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'gradio'" ] } ], "source": [ "\n", "import gradio as gr\n", "import tensorflow as tf\n", "from tensorflow.keras.models import load_model\n", "from tensorflow.keras.preprocessing.image import img_to_array, load_img\n", "import numpy as np\n", "import requests\n", "\n", "# Load the model\n", "caption_model = load_model('caption_model.h5')\n", "\n", "# Load necessary components (assuming they're defined in your original code)\n", "# ... (Load tokenizer, inception_v3_model, etc.) ...\n", "# Replace these placeholders with your actual loading code.\n", "# Example:\n", "# tokenizer = ... # Load tokenizer\n", "inception_v3_model = tf.keras.applications.InceptionV3(weights='imagenet', input_shape=(299, 299, 3))\n", "inception_v3_model.layers.pop()\n", "inception_v3_model = tf.keras.models.Model(inputs=inception_v3_model.inputs, outputs=inception_v3_model.layers[-2].output)\n", "max_caption_length = 34 # Replace with your actual value\n", "cnn_output_dim = 2048 #Replace with your actual value\n", "tokenizer = None # Replace with your actual tokenizer\n", "\n", "def preprocess_image(image):\n", " img = load_img(image, target_size=(299, 299))\n", " img = img_to_array(img)\n", " img = np.expand_dims(img, axis=0)\n", " img = tf.keras.applications.inception_v3.preprocess_input(img)\n", " return img\n", "\n", "def greedy_generator(image_features):\n", " # Your greedy_generator function from the original code\n", " in_text = 'start '\n", " for _ in range(max_caption_length):\n", " sequence = tokenizer.texts_to_sequences([in_text])[0]\n", " sequence = tf.keras.preprocessing.sequence.pad_sequences([sequence], maxlen=max_caption_length).reshape((1,max_caption_length))\n", " prediction = caption_model.predict([image_features.reshape(1,cnn_output_dim), sequence], verbose=0)\n", " idx = np.argmax(prediction)\n", " word = tokenizer.index_word[idx]\n", " in_text += ' ' + word\n", " if word == 'end':\n", " break\n", "\n", " in_text = in_text.replace('start ', '')\n", " in_text = in_text.replace(' end', '')\n", "\n", " return in_text\n", "\n", "\n", "def predict(image):\n", " processed_image = preprocess_image(image)\n", " image_features = inception_v3_model.predict(processed_image, verbose=0)\n", " image_features = image_features.flatten()\n", " caption = greedy_generator(image_features)\n", " return caption\n", "\n", "iface = gr.Interface(\n", " fn=predict,\n", " inputs=gr.Image(type=\"filepath\"), # changed to filepath to handle local files correctly.\n", " outputs=\"text\",\n", " title=\"Image Captioning\",\n", " description=\"Upload an image to generate a caption.\"\n", ")\n", "\n", "iface.launch()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-07-01T19:23:32.329991Z", "iopub.status.busy": "2025-07-01T19:23:32.329298Z", "iopub.status.idle": "2025-07-01T19:23:32.336302Z", "shell.execute_reply": "2025-07-01T19:23:32.335557Z", "shell.execute_reply.started": "2025-07-01T19:23:32.329967Z" }, "id": "hsSDZ7AVSfVv", "trusted": true }, "outputs": [], "source": [ "def preprocess_image(image):\n", " img = load_img(image, target_size=(299, 299))\n", " img = img_to_array(img)\n", " img = np.expand_dims(img, axis=0)\n", " img = tf.keras.applications.inception_v3.preprocess_input(img)\n", " return img\n", "\n", "def greedy_generator(image_features):\n", " # Your greedy_generator function from the original code\n", " in_text = 'start '\n", " for _ in range(max_caption_length):\n", " sequence = tokenizer.texts_to_sequences([in_text])[0]\n", " sequence = tf.keras.preprocessing.sequence.pad_sequences([sequence], maxlen=max_caption_length).reshape((1,max_caption_length))\n", " prediction = caption_model.predict([image_features.reshape(1,cnn_output_dim), sequence], verbose=0)\n", " idx = np.argmax(prediction)\n", " word = tokenizer.index_word[idx]\n", " in_text += ' ' + word\n", " if word == 'end':\n", " break\n", "\n", " in_text = in_text.replace('start ', '')\n", " in_text = in_text.replace(' end', '')\n", "\n", " return in_text\n", "\n", "def predict(image):\n", " processed_image = preprocess_image(image)\n", " image_features = inception_v3_model.predict(processed_image, verbose=0)\n", " image_features = image_features.flatten()\n", " caption = greedy_generator(image_features)\n", " return caption\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-07-01T19:31:29.819044Z", "iopub.status.busy": "2025-07-01T19:31:29.818761Z", "iopub.status.idle": "2025-07-01T19:31:32.448119Z", "shell.execute_reply": "2025-07-01T19:31:32.447451Z", "shell.execute_reply.started": "2025-07-01T19:31:29.819024Z" }, "id": "ZhepOBFDSBuq", "outputId": "ac02bcc8-8af3-4efe-890d-556b4a5add7a", "trusted": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Name: tensorflow\n", "Version: 2.18.0\n", "Summary: TensorFlow is an open source machine learning framework for everyone.\n", "Home-page: https://www.tensorflow.org/\n", "Author: Google Inc.\n", "Author-email: packages@tensorflow.org\n", "License: Apache 2.0\n", "Location: /usr/local/lib/python3.11/dist-packages\n", "Requires: absl-py, astunparse, flatbuffers, gast, google-pasta, grpcio, h5py, keras, libclang, ml-dtypes, numpy, opt-einsum, packaging, protobuf, requests, setuptools, six, tensorboard, tensorflow-io-gcs-filesystem, termcolor, typing-extensions, wrapt\n", "Required-by: dopamine_rl, tensorflow-text, tensorflow_decision_forests, tf_keras\n" ] } ], "source": [ "!pip show tensorflow" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-07-01T19:31:36.797483Z", "iopub.status.busy": "2025-07-01T19:31:36.796832Z", "iopub.status.idle": "2025-07-01T19:31:37.002118Z", "shell.execute_reply": "2025-07-01T19:31:37.001405Z", "shell.execute_reply.started": "2025-07-01T19:31:36.797455Z" }, "id": "U6-2BwNOSBur", "outputId": "3cc7ddbe-8684-4f80-e90b-a3d9970179b7", "trusted": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Python 3.11.11\n" ] } ], "source": [ "!python --version\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-07-01T19:53:25.046957Z", "iopub.status.busy": "2025-07-01T19:53:25.04639Z", "iopub.status.idle": "2025-07-01T19:53:25.056183Z", "shell.execute_reply": "2025-07-01T19:53:25.055409Z", "shell.execute_reply.started": "2025-07-01T19:53:25.046932Z" }, "id": "YVeRXnrdSBur", "trusted": true }, "outputs": [], "source": [ "def beam_search_generator(image_features, K_beams = 3, log = False):\n", " start = [tokenizer.word_index['start']]\n", " start_word = [[start, 0.0]]\n", " for _ in range(max_caption_length):\n", " temp = []\n", " for s in start_word:\n", " sequence = pad_sequences([s[0]], maxlen=max_caption_length, padding='post').reshape((1,max_caption_length)).astype('float32') # Cast to float32\n", " preds = caption_model.predict([image_features.reshape(1,cnn_output_dim), sequence], verbose=0)\n", " word_preds = np.argsort(preds[0])[-K_beams:]\n", " for w in word_preds:\n", " next_cap, prob = s[0][:], s[1]\n", " next_cap.append(w)\n", " if log:\n", " prob += np.log(preds[0][w]) # assign a probability to each K words\n", " else:\n", " prob += preds[0][w]\n", " temp.append([next_cap, prob])\n", "\n", " start_word = temp\n", " start_word = sorted(start_word, reverse=False, key=lambda l: l[1])\n", " start_word = start_word[-K_beams:]\n", "\n", " start_word = start_word[-1][0]\n", " captions_ = [tokenizer.index_word[i] for i in start_word]\n", " final_caption = []\n", " for i in captions_:\n", " if i != 'end':\n", " final_caption.append(i)\n", " else:\n", " break\n", "\n", " final_caption = ' '.join(final_caption[1:])\n", " return final_caption\n", "\n", "def greedy_generator(image_features):\n", " in_text = 'start '\n", " for _ in range(max_caption_length):\n", " sequence = tokenizer.texts_to_sequences([in_text])[0]\n", " sequence = pad_sequences([sequence], maxlen=max_caption_length, padding='post').reshape((1,max_caption_length)).astype('float32') # Cast to float32\n", " prediction = caption_model.predict([image_features.reshape(1,cnn_output_dim), sequence], verbose=0)\n", " idx = np.argmax(prediction)\n", " word = tokenizer.index_word[idx]\n", " in_text += ' ' + word\n", " if word == 'end':\n", " break\n", "\n", " in_text = in_text.replace('start ', '')\n", " in_text = in_text.replace(' end', '')\n", "\n", " return in_text" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-07-01T19:47:58.944818Z", "iopub.status.busy": "2025-07-01T19:47:58.944551Z", "iopub.status.idle": "2025-07-01T19:47:59.163988Z", "shell.execute_reply": "2025-07-01T19:47:59.163205Z", "shell.execute_reply.started": "2025-07-01T19:47:58.944799Z" }, "id": "oDFRihgFSBus", "trusted": true }, "outputs": [], "source": [ "import tensorflow as tf\n", "from tensorflow.keras.models import load_model\n", "from tensorflow.keras.preprocessing.image import load_img, img_to_array\n", "import numpy as np\n", "import os\n", "from tensorflow.keras.layers import Add # Import the Add layer\n", "\n", "# Load the saved model, specifying custom objects\n", "loaded_caption_model = load_model('kaggle/working/caption_model.keras')\n", "\n", "# Assume tokenizer, inception_v3_model, max_caption_length, and cnn_output_dim are already defined in the notebook\n", "# If not, you would need to load/define them here.\n", "# Example (replace with your actual loading/definition if needed):\n", "# tokenizer = ...\n", "# inception_v3_model = ...\n", "# max_caption_length = ...\n", "# cnn_output_dim = ...\n", "\n", "def predict_caption(image_path):\n", " \"\"\"\n", " Predicts a caption for a given image.\n", "\n", " Args:\n", " image_path (str): The path to the image file.\n", "\n", " Returns:\n", " str: The generated caption.\n", " \"\"\"\n", " # Preprocess the image\n", " img = load_img(image_path, target_size=(299, 299))\n", " img = img_to_array(img)\n", " img = np.expand_dims(img, axis=0)\n", " img = tf.keras.applications.inception_v3.preprocess_input(img)\n", "\n", " # Extract image features using the InceptionV3 model\n", " image_features = inception_v3_model.predict(img, verbose=0)\n", " image_features = image_features.flatten()\n", "\n", " # Generate caption using the greedy search method (assuming greedy_generator is defined)\n", " # If you want to use beam search, call beam_search_generator instead.\n", " predicted_caption = greedy_generator(image_features)\n", "\n", " return predicted_caption\n", "\n", "# Example usage:\n", "# image_path_to_predict = 'path/to/your/image.jpg' # Replace with your image path\n", "# generated_caption = predict_caption(image_path_to_predict)\n", "# print(\"Predicted Caption:\", generated_caption)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-07-01T19:47:09.718448Z", "iopub.status.busy": "2025-07-01T19:47:09.717838Z", "iopub.status.idle": "2025-07-01T19:47:09.971953Z", "shell.execute_reply": "2025-07-01T19:47:09.971366Z", "shell.execute_reply.started": "2025-07-01T19:47:09.71841Z" }, "id": "43mgfN38SBus", "trusted": true }, "outputs": [], "source": [ "# Save the entire model to a HDF5 file\n", "caption_model.save('kaggle/working/caption_model.keras')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2025-07-01T19:53:25.724198Z", "iopub.status.busy": "2025-07-01T19:53:25.723952Z", "iopub.status.idle": "2025-07-01T19:53:26.940272Z", "shell.execute_reply": "2025-07-01T19:53:26.939633Z", "shell.execute_reply.started": "2025-07-01T19:53:25.724179Z" }, "id": "5lEsFeVRSBus", "outputId": "e3e9b735-a70f-45c7-900f-408ccb7c58e5", "trusted": true }, "outputs": [ { "data": { "text/plain": [ "' a basketball player in a white uniform is playing a game'" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "predict_caption('/kaggle/working/fight.jpg')" ] }, { "cell_type": "markdown", "metadata": { "id": "Px7KnkTEPtfN" }, "source": [ "# Image Captioning with CLIP and GPT-2 from scratch" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2022-08-21T14:09:55.470695Z", "iopub.status.busy": "2022-08-21T14:09:55.468967Z", "iopub.status.idle": "2022-08-21T14:09:55.485756Z", "shell.execute_reply": "2022-08-21T14:09:55.483936Z", "shell.execute_reply.started": "2022-08-21T14:09:55.470634Z" }, "id": "v-jDQ7ISyk_6", "trusted": true }, "outputs": [], "source": [ "import glob\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from PIL import Image\n", "from time import time\n", "from numpy import array" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "aRgupW-nyKb7", "trusted": true }, "outputs": [], "source": [ "df = pd.read_csv(\"../input/flickr8k/captions.txt\")\n", "df" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "EaqyXH3s0T0N", "trusted": true }, "outputs": [], "source": [ "# import re\n", "# def caption_preprocessing(text, remove_digits=True):\n", "# pattern=r'[^a-zA-z0-9\\s]'\n", "# text=re.sub(pattern,'',text)\n", "# # tokenize\n", "# text=text.split()\n", "# # convert to lower case\n", "# text = [word.lower() for word in text]\n", "# # remove hanging 's' and 'a'\n", "# # text = [word for word in text if len(word)>1]\n", "\n", "# # remove tokens with numbers in them\n", "# text = [word for word in text if word.isalpha()]\n", "# # store as string\n", "# text = ' '.join(text)\n", "\n", "# # insert 'startseq', 'endseq' cho chuỗi\n", "# text = 'startseq ' + text + ' endseq'\n", "# return text\n", "\n", "# print(caption_preprocessing('chao .. ban $ hello980 it\\'s a table.#'))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "J-KPJAwC0Tx5", "trusted": true }, "outputs": [], "source": [ "# df['caption'] = df['caption'].apply(caption_preprocessing)\n", "# df['caption']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "yhMV80IH0Tvx" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2022-08-21T14:10:16.098120Z", "iopub.status.busy": "2022-08-21T14:10:16.097540Z", "iopub.status.idle": "2022-08-21T14:10:50.889790Z", "shell.execute_reply": "2022-08-21T14:10:50.888047Z", "shell.execute_reply.started": "2022-08-21T14:10:16.098075Z" }, "id": "EFtYDUXI9_de", "outputId": "cfbee85e-213b-47b4-e03b-70db09df040c", "trusted": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting ftfy\n", " Downloading ftfy-6.1.1-py3-none-any.whl (53 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m53.1/53.1 kB\u001b[0m \u001b[31m441.4 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m \u001b[36m0:00:01\u001b[0m\n", "\u001b[?25hRequirement already satisfied: regex in /opt/conda/lib/python3.7/site-packages (2021.11.10)\n", "Requirement already satisfied: tqdm in /opt/conda/lib/python3.7/site-packages (4.64.0)\n", "Requirement already satisfied: wcwidth>=0.2.5 in /opt/conda/lib/python3.7/site-packages (from ftfy) (0.2.5)\n", "Installing collected packages: ftfy\n", "Successfully installed ftfy-6.1.1\n", "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", "\u001b[0mCollecting git+https://github.com/openai/CLIP.git\n", " Cloning https://github.com/openai/CLIP.git to /tmp/pip-req-build-5rj5rx8r\n", " Running command git clone --filter=blob:none --quiet https://github.com/openai/CLIP.git /tmp/pip-req-build-5rj5rx8r\n", " Resolved https://github.com/openai/CLIP.git to commit d50d76daa670286dd6cacf3bcd80b5e4823fc8e1\n", " Preparing metadata (setup.py) ... \u001b[?25ldone\n", "\u001b[?25hRequirement already satisfied: ftfy in /opt/conda/lib/python3.7/site-packages (from clip==1.0) (6.1.1)\n", "Requirement already satisfied: regex in /opt/conda/lib/python3.7/site-packages (from clip==1.0) (2021.11.10)\n", "Requirement already satisfied: tqdm in /opt/conda/lib/python3.7/site-packages (from clip==1.0) (4.64.0)\n", "Requirement already satisfied: torch in /opt/conda/lib/python3.7/site-packages (from clip==1.0) (1.11.0)\n", "Requirement already satisfied: torchvision in /opt/conda/lib/python3.7/site-packages (from clip==1.0) (0.12.0)\n", "Requirement already satisfied: wcwidth>=0.2.5 in /opt/conda/lib/python3.7/site-packages (from ftfy->clip==1.0) (0.2.5)\n", "Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.7/site-packages (from torch->clip==1.0) (4.1.1)\n", "Requirement already satisfied: requests in /opt/conda/lib/python3.7/site-packages (from torchvision->clip==1.0) (2.28.1)\n", "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/lib/python3.7/site-packages (from torchvision->clip==1.0) (9.1.1)\n", "Requirement already satisfied: numpy in /opt/conda/lib/python3.7/site-packages (from torchvision->clip==1.0) (1.21.6)\n", "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests->torchvision->clip==1.0) (3.3)\n", "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests->torchvision->clip==1.0) (1.26.9)\n", "Requirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.7/site-packages (from requests->torchvision->clip==1.0) (2.1.0)\n", "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests->torchvision->clip==1.0) (2022.6.15)\n", "Building wheels for collected packages: clip\n", " Building wheel for clip (setup.py) ... \u001b[?25ldone\n", "\u001b[?25h Created wheel for clip: filename=clip-1.0-py3-none-any.whl size=1369409 sha256=7f811284f489386213b3f478a320e5d518e6a7213cc4ec61aa5268c4680f9a1f\n", " Stored in directory: /tmp/pip-ephem-wheel-cache-n62gwy5s/wheels/fd/b9/c3/5b4470e35ed76e174bff77c92f91da82098d5e35fd5bc8cdac\n", "Successfully built clip\n", "Installing collected packages: clip\n", "Successfully installed clip-1.0\n", "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", "\u001b[0m" ] } ], "source": [ "# $ conda install --yes -c pytorch pytorch=1.7.1 torchvision cudatoolkit=11.0\n", "! pip install ftfy regex tqdm\n", "! pip install git+https://github.com/openai/CLIP.git" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2022-08-21T14:11:11.841075Z", "iopub.status.busy": "2022-08-21T14:11:11.839855Z", "iopub.status.idle": "2022-08-21T14:11:11.848728Z", "shell.execute_reply": "2022-08-21T14:11:11.846607Z", "shell.execute_reply.started": "2022-08-21T14:11:11.841040Z" }, "id": "RXh8smZzyvgP", "trusted": true }, "outputs": [], "source": [ "import torch\n", "import skimage.io as io\n", "import clip\n", "from PIL import Image\n", "import pickle\n", "import json\n", "import os\n", "from tqdm import tqdm\n", "import argparse" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "FF98n4HEm9Q-", "trusted": true }, "outputs": [], "source": [ "def parse(clip_model_type: str):\n", " device = torch.device('cuda:0')\n", " clip_model_name = clip_model_type.replace('/', '_')\n", " out_path = f\"./oscar_split_{clip_model_name}_train.pkl\"\n", " clip_model, preprocess = clip.load(clip_model_type, device=device, jit=False)\n", " all_embeddings = []\n", " all_captions = []\n", " for i in tqdm(range(len(df))):\n", " d=df.iloc[i]\n", " img_id = d[\"image\"]\n", " filename = f\"../input/flickr8k/Images/{img_id}\"\n", " image = Image.open(filename).convert(\"RGB\")\n", " image = preprocess(image).unsqueeze(0).to(device)\n", " with torch.no_grad():\n", " prefix = clip_model.encode_image(image).cpu()\n", " d[\"clip_embedding\"] = i\n", " all_embeddings.append(prefix)\n", " all_captions.append(d)\n", " if (i + 1) % 10000 == 0:\n", " with open(out_path, 'wb') as f:\n", " pickle.dump({\"clip_embedding\": torch.cat(all_embeddings, dim=0), \"captions\": all_captions}, f)\n", "\n", " with open(out_path, 'wb') as f:\n", " pickle.dump({\"clip_embedding\": torch.cat(all_embeddings, dim=0), \"captions\": all_captions}, f)\n", "\n", " print('Done')\n", " print(\"%0d embeddings saved \" % len(all_embeddings))\n", " return 0\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "CtTCRq_Ysvvn", "trusted": true }, "outputs": [], "source": [ "clip.available_models()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "XEvB5fGpsvvn", "trusted": true }, "outputs": [], "source": [ "parse('RN50x4')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2022-08-21T14:11:16.648924Z", "iopub.status.busy": "2022-08-21T14:11:16.648493Z", "iopub.status.idle": "2022-08-21T14:11:16.661270Z", "shell.execute_reply": "2022-08-21T14:11:16.659264Z", "shell.execute_reply.started": "2022-08-21T14:11:16.648890Z" }, "id": "3HJPYHD1svvo", "trusted": true }, "outputs": [], "source": [ "import clip\n", "import os\n", "from torch import nn\n", "import numpy as np\n", "import torch\n", "import torch.nn.functional as nnf\n", "import sys\n", "from typing import Tuple, List, Union, Optional\n", "from transformers import GPT2Tokenizer, GPT2LMHeadModel, AdamW, get_linear_schedule_with_warmup\n", "from tqdm import tqdm, trange\n", "# from google.colab import files\n", "import skimage.io as io\n", "import PIL.Image\n", "# from IPython.display import Image\n", "from enum import Enum\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2022-08-21T14:11:18.312187Z", "iopub.status.busy": "2022-08-21T14:11:18.311403Z", "iopub.status.idle": "2022-08-21T14:11:18.335790Z", "shell.execute_reply": "2022-08-21T14:11:18.334179Z", "shell.execute_reply.started": "2022-08-21T14:11:18.312122Z" }, "id": "XzHfgs7Jsvvo", "trusted": true }, "outputs": [], "source": [ "import torch\n", "import torch.nn as nn\n", "from torch.nn import functional as nnf\n", "from torch.utils.data import Dataset, DataLoader\n", "from enum import Enum\n", "from transformers import GPT2Tokenizer, GPT2LMHeadModel, AdamW, get_linear_schedule_with_warmup\n", "from tqdm import tqdm\n", "import os\n", "import pickle\n", "import sys\n", "import argparse\n", "import json\n", "from typing import Tuple, Optional, Union\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Wql6woxXsvvp", "trusted": true }, "outputs": [], "source": [ "class ClipCocoDataset(Dataset):\n", "\n", " def __len__(self) -> int:\n", " return len(self.captions_tokens)\n", "\n", " def pad_tokens(self, item: int):\n", " tokens = self.captions_tokens[item]\n", " padding = self.max_seq_len - tokens.shape[0]\n", " if padding > 0:\n", " tokens = torch.cat((tokens, torch.zeros(padding, dtype=torch.int64) - 1))\n", " self.captions_tokens[item] = tokens\n", " elif padding < 0:\n", " tokens = tokens[:self.max_seq_len]\n", " self.captions_tokens[item] = tokens\n", " mask = tokens.ge(0) # mask is zero where we out of sequence\n", " tokens[~mask] = 0\n", " mask = mask.float()\n", " mask = torch.cat((torch.ones(self.prefix_length), mask), dim=0) # adding prefix mask\n", " return tokens, mask\n", "\n", " def __getitem__(self, item: int) -> Tuple[torch.Tensor, ...]:\n", " tokens, mask = self.pad_tokens(item)\n", " prefix = self.prefixes[self.caption2embedding[item]]\n", " if self.normalize_prefix:\n", " prefix = prefix.float()\n", " prefix = prefix / prefix.norm(2, -1)\n", " return tokens, mask, prefix\n", "\n", " def __init__(self, data_path: str, prefix_length: int, gpt2_type: str = \"gpt2\",\n", " normalize_prefix=False):\n", " self.tokenizer = GPT2Tokenizer.from_pretrained(gpt2_type)\n", " self.prefix_length = prefix_length\n", " self.normalize_prefix = normalize_prefix\n", " with open(data_path, 'rb') as f:\n", " all_data = pickle.load(f)\n", " print(\"Data size is %0d\" % len(all_data[\"clip_embedding\"]))\n", " sys.stdout.flush()\n", " self.prefixes = all_data[\"clip_embedding\"]\n", " captions_raw = all_data[\"captions\"]\n", " self.image_ids = [caption[\"image\"] for caption in captions_raw]\n", " self.captions = [caption['caption'] for caption in captions_raw]\n", " if os.path.isfile(f\"{data_path[:-4]}_tokens.pkl\"):\n", " with open(f\"{data_path[:-4]}_tokens.pkl\", 'rb') as f:\n", " self.captions_tokens, self.caption2embedding, self.max_seq_len = pickle.load(f)\n", " else:\n", " self.captions_tokens = []\n", " self.caption2embedding = []\n", " max_seq_len = 0\n", " for caption in captions_raw:\n", " self.captions_tokens.append(torch.tensor(self.tokenizer.encode(caption['caption']), dtype=torch.int64))\n", " self.caption2embedding.append(caption[\"clip_embedding\"])\n", " max_seq_len = max(max_seq_len, self.captions_tokens[-1].shape[0])\n", " # self.max_seq_len = max_seq_len\n", " with open(f\"{data_path[:-4]}_tokens.pkl\", 'wb') as f:\n", " pickle.dump([self.captions_tokens, self.caption2embedding, max_seq_len], f)\n", " all_len = torch.tensor([len(self.captions_tokens[i]) for i in range(len(self))]).float()\n", " self.max_seq_len = min(int(all_len.mean() + all_len.std() * 10), int(all_len.max()))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2022-08-21T14:11:25.205444Z", "iopub.status.busy": "2022-08-21T14:11:25.204960Z", "iopub.status.idle": "2022-08-21T14:11:25.257256Z", "shell.execute_reply": "2022-08-21T14:11:25.255194Z", "shell.execute_reply.started": "2022-08-21T14:11:25.205409Z" }, "id": "_LyQwFeSOqUG", "trusted": true }, "outputs": [], "source": [ "#@title Model\n", "\n", "\n", "class MappingType(Enum):\n", " MLP = 'mlp'\n", " Transformer = 'transformer'\n", "\n", "\n", "class MlpTransformer(nn.Module):\n", " def __init__(self, in_dim, h_dim, out_d: Optional[int] = None, act=nnf.relu, dropout=0.):\n", " super().__init__()\n", " out_d = out_d if out_d is not None else in_dim\n", " self.fc1 = nn.Linear(in_dim, h_dim)\n", " self.act = act\n", " self.fc2 = nn.Linear(h_dim, out_d)\n", " self.dropout = nn.Dropout(dropout)\n", "\n", " def forward(self, x):\n", " x = self.fc1(x)\n", " x = self.act(x)\n", " x = self.dropout(x)\n", " x = self.fc2(x)\n", " x = self.dropout(x)\n", " return x\n", "\n", "class MLP(nn.Module):\n", "\n", " def forward(self, x: torch.Tensor) -> torch.Tensor:\n", " return self.model(x)\n", "\n", " def __init__(self, sizes: Tuple[int, ...], bias=True, act=nn.Tanh):\n", " super(MLP, self).__init__()\n", " layers = []\n", " for i in range(len(sizes) - 1):\n", " layers.append(nn.Linear(sizes[i], sizes[i + 1], bias=bias))\n", " if i < len(sizes) - 2:\n", " layers.append(act())\n", " self.model = nn.Sequential(*layers)\n", "\n", "\n", "class MultiHeadAttention(nn.Module):\n", "\n", " def __init__(self, dim_self, dim_ref, num_heads, bias=True, dropout=0.):\n", " super().__init__()\n", " self.num_heads = num_heads\n", " head_dim = dim_self // num_heads\n", " self.scale = head_dim ** -0.5\n", " self.to_queries = nn.Linear(dim_self, dim_self, bias=bias)\n", " self.to_keys_values = nn.Linear(dim_ref, dim_self * 2, bias=bias)\n", " self.project = nn.Linear(dim_self, dim_self)\n", " self.dropout = nn.Dropout(dropout)\n", "\n", " def forward(self, x, y=None, mask=None):\n", " y = y if y is not None else x\n", " b, n, c = x.shape\n", " _, m, d = y.shape\n", " # b n h dh\n", " queries = self.to_queries(x).reshape(b, n, self.num_heads, c // self.num_heads)\n", " # b m 2 h dh\n", " keys_values = self.to_keys_values(y).reshape(b, m, 2, self.num_heads, c // self.num_heads)\n", " keys, values = keys_values[:, :, 0], keys_values[:, :, 1]\n", " attention = torch.einsum('bnhd,bmhd->bnmh', queries, keys) * self.scale\n", " if mask is not None:\n", " if mask.dim() == 2:\n", " mask = mask.unsqueeze(1)\n", " attention = attention.masked_fill(mask.unsqueeze(3), float(\"-inf\"))\n", " attention = attention.softmax(dim=2)\n", " out = torch.einsum('bnmh,bmhd->bnhd', attention, values).reshape(b, n, c)\n", " out = self.project(out)\n", " return out, attention\n", "\n", "\n", "class TransformerLayer(nn.Module):\n", "\n", " def forward_with_attention(self, x, y=None, mask=None):\n", " x_, attention = self.attn(self.norm1(x), y, mask)\n", " x = x + x_\n", " x = x + self.mlp(self.norm2(x))\n", " return x, attention\n", "\n", " def forward(self, x, y=None, mask=None):\n", " x = x + self.attn(self.norm1(x), y, mask)[0]\n", " x = x + self.mlp(self.norm2(x))\n", " return x\n", "\n", " def __init__(self, dim_self, dim_ref, num_heads, mlp_ratio=4., bias=False, dropout=0., act=nnf.relu,\n", " norm_layer: nn.Module = nn.LayerNorm):\n", " super().__init__()\n", " self.norm1 = norm_layer(dim_self)\n", " self.attn = MultiHeadAttention(dim_self, dim_ref, num_heads, bias=bias, dropout=dropout)\n", " self.norm2 = norm_layer(dim_self)\n", " self.mlp = MlpTransformer(dim_self, int(dim_self * mlp_ratio), act=act, dropout=dropout)\n", "\n", "\n", "class Transformer(nn.Module):\n", "\n", " def forward_with_attention(self, x, y=None, mask=None):\n", " attentions = []\n", " for layer in self.layers:\n", " x, att = layer.forward_with_attention(x, y, mask)\n", " attentions.append(att)\n", " return x, attentions\n", "\n", " def forward(self, x, y=None, mask=None):\n", " for i, layer in enumerate(self.layers):\n", " if i % 2 == 0 and self.enc_dec: # cross\n", " x = layer(x, y)\n", " elif self.enc_dec: # self\n", " x = layer(x, x, mask)\n", " else: # self or cross\n", " x = layer(x, y, mask)\n", " return x\n", "\n", " def __init__(self, dim_self: int, num_heads: int, num_layers: int, dim_ref: Optional[int] = None,\n", " mlp_ratio: float = 2., act=nnf.relu, norm_layer: nn.Module = nn.LayerNorm, enc_dec: bool = False):\n", " super(Transformer, self).__init__()\n", " dim_ref = dim_ref if dim_ref is not None else dim_self\n", " self.enc_dec = enc_dec\n", " if enc_dec:\n", " num_layers = num_layers * 2\n", " layers = []\n", " for i in range(num_layers):\n", " if i % 2 == 0 and enc_dec: # cross\n", " layers.append(TransformerLayer(dim_self, dim_ref, num_heads, mlp_ratio, act=act, norm_layer=norm_layer))\n", " elif enc_dec: # self\n", " layers.append(TransformerLayer(dim_self, dim_self, num_heads, mlp_ratio, act=act, norm_layer=norm_layer))\n", " else: # self or cross\n", " layers.append(TransformerLayer(dim_self, dim_ref, num_heads, mlp_ratio, act=act, norm_layer=norm_layer))\n", " self.layers = nn.ModuleList(layers)\n", "\n", "\n", "class TransformerMapper(nn.Module):\n", "\n", " def forward(self, x):\n", " x = self.linear(x).view(x.shape[0], self.clip_length, -1)\n", " prefix = self.prefix_const.unsqueeze(0).expand(x.shape[0], *self.prefix_const.shape)\n", " prefix = torch.cat((x, prefix), dim=1)\n", " out = self.transformer(prefix)[:, self.clip_length:]\n", " return out\n", "\n", " def __init__(self, dim_clip: int, dim_embedding: int, prefix_length: int, clip_length: int, num_layers: int = 8):\n", " super(TransformerMapper, self).__init__()\n", " self.clip_length = clip_length\n", " self.transformer = Transformer(dim_embedding, 8, num_layers)\n", " self.linear = nn.Linear(dim_clip, clip_length * dim_embedding)\n", " self.prefix_const = nn.Parameter(torch.randn(prefix_length, dim_embedding), requires_grad=True)\n", "\n", "\n", "class ClipCaptionModel(nn.Module):\n", "\n", " def get_dummy_token(self, batch_size: int, device: torch.device) -> torch.Tensor:\n", " return torch.zeros(batch_size, self.prefix_length, dtype=torch.int64, device=device)\n", "\n", " def forward(self, tokens: torch.Tensor, prefix: torch.Tensor, mask: Optional[torch.Tensor] = None,\n", " labels: Optional[torch.Tensor] = None):\n", " embedding_text = self.gpt.transformer.wte(tokens)\n", " prefix_projections = self.clip_project(prefix).view(-1, self.prefix_length, self.gpt_embedding_size)\n", " embedding_cat = torch.cat((prefix_projections, embedding_text), dim=1)\n", " if labels is not None:\n", " dummy_token = self.get_dummy_token(tokens.shape[0], tokens.device)\n", " labels = torch.cat((dummy_token, tokens), dim=1)\n", " out = self.gpt(inputs_embeds=embedding_cat, labels=labels, attention_mask=mask)\n", " return out\n", "\n", " def __init__(self, prefix_length: int, clip_length: Optional[int] = None, prefix_size: int = 512,\n", " num_layers: int = 8, mapping_type: MappingType = MappingType.MLP):\n", " super(ClipCaptionModel, self).__init__()\n", " self.prefix_length = prefix_length\n", " self.gpt = GPT2LMHeadModel.from_pretrained('gpt2')\n", " self.gpt_embedding_size = self.gpt.transformer.wte.weight.shape[1]\n", " if mapping_type == MappingType.MLP:\n", " self.clip_project = MLP((prefix_size, (self.gpt_embedding_size * prefix_length) // 2,\n", " self.gpt_embedding_size * prefix_length))\n", " else:\n", " self.clip_project = TransformerMapper(prefix_size, self.gpt_embedding_size, prefix_length,\n", " clip_length, num_layers)\n", "\n", "\n", "class ClipCaptionPrefix(ClipCaptionModel):\n", "\n", " def parameters(self, recurse: bool = True):\n", " return self.clip_project.parameters()\n", "\n", " def train(self, mode: bool = True):\n", " super(ClipCaptionPrefix, self).train(mode)\n", " self.gpt.eval()\n", " return self\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "4RNrYAXZsvvq", "trusted": true }, "outputs": [], "source": [ "def train(dataset: ClipCocoDataset, model: ClipCaptionModel,\n", " bs=64,epochs=10,save_every=5,\n", " lr: float = 2e-5, warmup_steps: int = 5000, output_dir: str = \".\", output_prefix: str = \"coco_prefix\"):\n", "\n", " device = torch.device('cuda:0')\n", " batch_size = bs\n", " epochs = epochs\n", " if not os.path.exists(output_dir):\n", " os.makedirs(output_dir)\n", " model = model.to(device)\n", " model.train()\n", " optimizer = AdamW(model.parameters(), lr=lr)\n", " train_dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True, drop_last=True)\n", " scheduler = get_linear_schedule_with_warmup(\n", " optimizer, num_warmup_steps=warmup_steps, num_training_steps=epochs * len(train_dataloader)\n", " )\n", " # save_config(args)\n", " for epoch in range(epochs):\n", " print(f\">>> Training epoch {epoch}\")\n", " sys.stdout.flush()\n", " progress = tqdm(total=len(train_dataloader), desc=output_prefix)\n", " for idx, (tokens, mask, prefix) in enumerate(train_dataloader):\n", " model.zero_grad()\n", " tokens, mask, prefix = tokens.to(device), mask.to(device), prefix.to(device, dtype=torch.float32)\n", " outputs = model(tokens, prefix, mask)\n", " logits = outputs.logits[:, dataset.prefix_length - 1: -1]\n", " loss = nnf.cross_entropy(logits.reshape(-1, logits.shape[-1]), tokens.flatten(), ignore_index=0)\n", " loss.backward()\n", " optimizer.step()\n", " scheduler.step()\n", " optimizer.zero_grad()\n", " progress.set_postfix({\"loss\": loss.item()})\n", " progress.update()\n", " if (idx + 1) % 10000 == 0:\n", " torch.save(\n", " model.state_dict(),\n", " os.path.join(output_dir, f\"{output_prefix}_latest.pt\"),\n", " )\n", " progress.close()\n", " if epoch % save_every == 0 or epoch == epochs - 1:\n", " torch.save(\n", " model.state_dict(),\n", " os.path.join(output_dir, f\"{output_prefix}-{epoch:03d}.pt\"),\n", " )\n", " return model\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "6grUe8v7svvq", "trusted": true }, "outputs": [], "source": [ "prefix_length = 40\n", "prefix_dim = 640\n", "mapping_type = MappingType.Transformer\n", "data_path= './oscar_split_RN50x4_train.pkl'\n", "only_prefix = True\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "E10t15ihsvvq", "trusted": true }, "outputs": [], "source": [ "dataset = ClipCocoDataset(data_path, prefix_length)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ZcOAmF8Csvvr", "trusted": true }, "outputs": [], "source": [ "model=ClipCaptionPrefix(40, clip_length=40, prefix_size=640,\n", " num_layers=8, mapping_type='transformer')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "GTUArnEwsvvr", "trusted": true }, "outputs": [], "source": [ "# for param_tensor in model.state_dict():\n", "# print(param_tensor, \"\\t\", model.state_dict()[param_tensor].size())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "WdXESVvzsvvr", "trusted": true }, "outputs": [], "source": [ "train(dataset,model,\n", " bs=64,epochs=20,save_every=5,\n", " lr= 2e-5, warmup_steps = 5000, output_dir = \"./\", output_prefix = \"coco_prefix\")" ] }, { "cell_type": "markdown", "metadata": { "id": "dvjaDVaRsvvr" }, "source": [ "# predict" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2022-08-21T14:11:34.946167Z", "iopub.status.busy": "2022-08-21T14:11:34.945653Z", "iopub.status.idle": "2022-08-21T14:11:34.977626Z", "shell.execute_reply": "2022-08-21T14:11:34.975908Z", "shell.execute_reply.started": "2022-08-21T14:11:34.946114Z" }, "id": "NK31LkNNsvvr", "trusted": true }, "outputs": [], "source": [ "#@title Caption prediction\n", "\n", "def generate_beam(model, tokenizer, beam_size: int = 5, prompt=None, embed=None,\n", " entry_length=67, temperature=1., stop_token: str = '.'):\n", "\n", " model.eval()\n", " stop_token_index = tokenizer.encode(stop_token)[0]\n", " tokens = None\n", " scores = None\n", " device = next(model.parameters()).device\n", " seq_lengths = torch.ones(beam_size, device=device)\n", " is_stopped = torch.zeros(beam_size, device=device, dtype=torch.bool)\n", " with torch.no_grad():\n", " if embed is not None:\n", " generated = embed\n", " else:\n", " if tokens is None:\n", " tokens = torch.tensor(tokenizer.encode(prompt))\n", " tokens = tokens.unsqueeze(0).to(device)\n", " generated = model.gpt.transformer.wte(tokens)\n", " for i in range(entry_length):\n", " outputs = model.gpt(inputs_embeds=generated)\n", " logits = outputs.logits\n", " logits = logits[:, -1, :] / (temperature if temperature > 0 else 1.0)\n", " logits = logits.softmax(-1).log()\n", " if scores is None:\n", " scores, next_tokens = logits.topk(beam_size, -1)\n", " generated = generated.expand(beam_size, *generated.shape[1:])\n", " next_tokens, scores = next_tokens.permute(1, 0), scores.squeeze(0)\n", " if tokens is None:\n", " tokens = next_tokens\n", " else:\n", " tokens = tokens.expand(beam_size, *tokens.shape[1:])\n", " tokens = torch.cat((tokens, next_tokens), dim=1)\n", " else:\n", " logits[is_stopped] = -float(np.inf)\n", " logits[is_stopped, 0] = 0\n", " scores_sum = scores[:, None] + logits\n", " seq_lengths[~is_stopped] += 1\n", " scores_sum_average = scores_sum / seq_lengths[:, None]\n", " scores_sum_average, next_tokens = scores_sum_average.view(-1).topk(beam_size, -1)\n", " next_tokens_source = next_tokens // scores_sum.shape[1]\n", " seq_lengths = seq_lengths[next_tokens_source]\n", " next_tokens = next_tokens % scores_sum.shape[1]\n", " next_tokens = next_tokens.unsqueeze(1)\n", " tokens = tokens[next_tokens_source]\n", " tokens = torch.cat((tokens, next_tokens), dim=1)\n", " generated = generated[next_tokens_source]\n", " scores = scores_sum_average * seq_lengths\n", " is_stopped = is_stopped[next_tokens_source]\n", " next_token_embed = model.gpt.transformer.wte(next_tokens.squeeze()).view(generated.shape[0], 1, -1)\n", " generated = torch.cat((generated, next_token_embed), dim=1)\n", " is_stopped = is_stopped + next_tokens.eq(stop_token_index).squeeze()\n", " if is_stopped.all():\n", " break\n", " scores = scores / seq_lengths\n", " output_list = tokens.cpu().numpy()\n", " output_texts = [tokenizer.decode(output[:int(length)]) for output, length in zip(output_list, seq_lengths)]\n", " order = scores.argsort(descending=True)\n", " output_texts = [output_texts[i] for i in order]\n", " return output_texts\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "referenced_widgets": [ "2dc5ad69c37a4af4ad9cc161848691b6", "283a26d5102d4dc89a9e7e0b7c5d9718", "fc169428718245448dadb7cba64e7c75" ] }, "execution": { "iopub.execute_input": "2022-08-21T14:12:04.111812Z", "iopub.status.busy": "2022-08-21T14:12:04.111321Z", "iopub.status.idle": "2022-08-21T14:12:41.545687Z", "shell.execute_reply": "2022-08-21T14:12:41.544240Z", "shell.execute_reply.started": "2022-08-21T14:12:04.111774Z" }, "id": "EG7Cl6wDsvvr", "outputId": "f311e12c-b656-441a-cef8-4ea705e1e9af", "trusted": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|███████████████████████████████████████| 402M/402M [00:15<00:00, 28.1MiB/s]\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2dc5ad69c37a4af4ad9cc161848691b6", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading: 0%| | 0.00/0.99M [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "images = Image.open('../input/coco-2017-dataset/coco2017/test2017/000000000647.jpg').convert(\"RGB\")\n", "image = preprocess(images).unsqueeze(0).to(device)\n", "display(images)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2022-08-21T14:51:54.855345Z", "iopub.status.busy": "2022-08-21T14:51:54.854788Z", "iopub.status.idle": "2022-08-21T14:51:55.667671Z", "shell.execute_reply": "2022-08-21T14:51:55.665619Z", "shell.execute_reply.started": "2022-08-21T14:51:54.855307Z" }, "id": "ZarZUPIFsvvt", "outputId": "ab7482f0-db3e-4ff8-a135-2813f7fa51ad", "trusted": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:42: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "A blue and white plane is flying in midair....................\n" ] } ], "source": [ "use_beam_search = True #@param {type:\"boolean\"}\n", "\n", "# image = io.imread(UPLOADED_FILE)\n", "# pil_image = PIL.Image.fromarray(image)\n", "# #pil_img = Image(filename=UPLOADED_FILE)\n", "# display(pil_image)\n", "\n", "# image = preprocess(pil_image).unsqueeze(0).to(device)\n", "with torch.no_grad():\n", " prefix = clip_model.encode_image(image).to(device, dtype=torch.float32)\n", " prefix = prefix / prefix.norm(2, -1).item()\n", " prefix_embed = model.clip_project(prefix).reshape(1, prefix_length, -1)\n", "if use_beam_search:\n", " generated_text_prefix = generate_beam(model, tokenizer, beam_size=7,embed=prefix_embed,stop_token='<|endoftext|>',entry_length=30)[0]\n", "\n", "\n", "print('\\n')\n", "print(generated_text_prefix)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Trashed CLIP Model" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: pillow in /usr/local/lib/python3.11/dist-packages (11.1.0)\n" ] } ], "source": [ "!pip install pillow" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#colab \n", "# import kagglehub\n", "\n", "# # Download latest version\n", "# path = kagglehub.dataset_download(\"awsaf49/coco-2017-dataset\")\n", "\n", "# print(\"Path to dataset files:\", path)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#kaggle\n", "path = '/kaggle/input/coco-2017-dataset'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from torchvision.datasets import CocoCaptions" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import torch\n", "import torch.nn as nn\n", "from torch.utils.data import Dataset, DataLoader\n", "from torchvision import transforms\n", "from torchvision.models import resnet152, ResNet152_Weights\n", "from transformers import GPT2Tokenizer, GPT2LMHeadModel\n", "from PIL import Image\n", "import json\n", "import os\n", "\n", "\n", "CFG = {\n", " \"image_size\": 224,\n", " \"max_seq_len\": 64,\n", " \"embed_size\": 768,\n", " \"prefix_length\": 10,\n", " \"batch_size\": 32,\n", " \"num_epochs\": 40,\n", " \"learning_rate\": 1e-4\n", "}\n", "\n", "tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n", "tokenizer.pad_token = tokenizer.eos_token" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Dataset Class\n", "class CocoDataset(Dataset):\n", " def __init__(self, image_dir, ann_path, tokenizer, transform=None, subset_size=None):\n", " with open(ann_path) as f:\n", " annotations = json.load(f)['annotations']\n", " # Load a subset of annotations if subset_size is specified\n", " if subset_size is not None:\n", " self.annotations = annotations[:subset_size]\n", " else:\n", " self.annotations = annotations\n", "\n", " self.image_dir = image_dir\n", " self.tokenizer = tokenizer\n", " self.transform = transform\n", "\n", " def __len__(self):\n", " return len(self.annotations)\n", "\n", " def __getitem__(self, idx):\n", " ann = self.annotations[idx]\n", " image_id_str = str(ann['image_id']).zfill(12)\n", " image_path = os.path.join(self.image_dir, image_id_str + '.jpg')\n", " image = Image.open(image_path).convert('RGB')\n", "\n", " if self.transform:\n", " image = self.transform(image)\n", "\n", " caption = ann['caption']\n", " inputs = self.tokenizer(\n", " caption,\n", " max_length=CFG['max_seq_len'],\n", " padding='max_length',\n", " truncation=True,\n", " return_tensors='pt'\n", " )\n", "\n", " return image, inputs.input_ids.squeeze(0), inputs.attention_mask.squeeze(0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class ImageCaptionModel(nn.Module):\n", " def __init__(self, prefix_length=10):\n", " super().__init__()\n", " self.prefix_length = prefix_length\n", " self.cnn = nn.Sequential(*list(resnet152(weights=ResNet152_Weights.IMAGENET1K_V2).children())[:-1])\n", " for param in self.cnn.parameters():\n", " param.requires_grad = False\n", "\n", " # Project 2048-d image features to a sequence of embeddings\n", " self.projection = nn.Sequential(\n", " nn.Linear(2048, 768 * prefix_length),\n", " nn.ReLU(),\n", " )\n", "\n", " self.llm = GPT2LMHeadModel.from_pretrained('gpt2')\n", " self.llm.resize_token_embeddings(len(tokenizer))\n", "\n", " def forward(self, images, input_ids, attention_mask):\n", " with torch.no_grad():\n", " features = self.cnn(images).view(images.size(0), -1)\n", "\n", " # Project to sequence of embeddings\n", " img_seq = self.projection(features) # (B, 768 * prefix_len)\n", " img_seq = img_seq.view(-1, self.prefix_length, 768) # (B, prefix_len, 768)\n", "\n", " # Text embeddings (excluding last token for training)\n", " txt_embeds = self.llm.transformer.wte(input_ids[:, :-1]) # (B, T-1, 768)\n", "\n", " # Concatenate: [image embeddings | text embeddings]\n", " inputs_embeds = torch.cat([img_seq, txt_embeds], dim=1)\n", "\n", " # Adjust attention mask\n", " prefix_mask = torch.ones(input_ids.size(0), self.prefix_length).to(images.device)\n", " attention_mask = torch.cat([prefix_mask, attention_mask[:, :-1]], dim=1)\n", "\n", " # Forward\n", " outputs = self.llm(\n", " inputs_embeds=inputs_embeds,\n", " attention_mask=attention_mask\n", " )\n", "\n", " return outputs.logits\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\n", "# Training Setup\n", "def train():\n", " # Transforms\n", " transform = transforms.Compose([\n", " transforms.Resize((CFG['image_size'], CFG['image_size'])),\n", " transforms.ToTensor(),\n", " transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n", " ])\n", "\n", " # Tokenizer\n", " tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n", " tokenizer.pad_token = tokenizer.eos_token\n", "\n", " # Dataset & Loader\n", " # Using CocoDataset with a subset size of 8000\n", " dataset = CocoDataset(\n", " image_dir=f'{path}/coco2017/train2017',\n", " ann_path=f'{path}/coco2017/annotations/captions_train2017.json',\n", " tokenizer=tokenizer,\n", " transform=transform,\n", " subset_size=8000 # Load only 8000 entries\n", " )\n", " loader = DataLoader(dataset, batch_size=CFG['batch_size'], shuffle=True)\n", "\n", " # Model\n", " model = ImageCaptionModel().cuda()\n", " optimizer = torch.optim.Adam(model.parameters(), lr=CFG['learning_rate'])\n", " criterion = nn.CrossEntropyLoss(ignore_index=tokenizer.pad_token_id)\n", " \n", " for epoch in range(CFG['num_epochs']):\n", " model.train()\n", " for images, input_ids, attn_mask in loader:\n", " images = images.cuda()\n", " input_ids = input_ids.cuda()\n", " attn_mask = attn_mask.cuda()\n", " \n", " optimizer.zero_grad()\n", " logits = model(images, input_ids, attn_mask)\n", " \n", " # Shift logits and labels\n", " logits = logits[:, model.prefix_length:, :] # Ignore prefix outputs\n", " labels = input_ids[:, 1:] # Shift labels for teacher forcing\n", " \n", " loss = criterion(logits.reshape(-1, logits.size(-1)), labels.reshape(-1))\n", " loss.backward()\n", " optimizer.step()\n", " \n", " \n", "\n", " print(f\"Epoch: {epoch+1}, Loss: {loss.item():.4f}\")\n", " save_path = \"/kaggle/working/clip_caption_model.pth\"\n", " torch.save(model.state_dict(), save_path)\n", " print(f\"Model saved to {save_path}\")\n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 1, Loss: 2.7203\n", "Epoch: 2, Loss: 2.5815\n", "Epoch: 3, Loss: 2.1797\n", "Epoch: 4, Loss: 2.2764\n", "Epoch: 5, Loss: 1.8374\n", "Epoch: 6, Loss: 1.5654\n", "Epoch: 7, Loss: 1.4040\n", "Epoch: 8, Loss: 1.3104\n", "Epoch: 9, Loss: 1.0020\n", "Epoch: 10, Loss: 0.9107\n", "Epoch: 11, Loss: 0.7906\n", "Epoch: 12, Loss: 0.7477\n", "Epoch: 13, Loss: 0.6430\n", "Epoch: 14, Loss: 0.5511\n", "Epoch: 15, Loss: 0.5291\n", "Epoch: 16, Loss: 0.6169\n", "Epoch: 17, Loss: 0.6268\n", "Epoch: 18, Loss: 0.5461\n", "Epoch: 19, Loss: 0.4207\n", "Epoch: 20, Loss: 0.3933\n", "Epoch: 21, Loss: 0.3791\n", "Epoch: 22, Loss: 0.3602\n", "Epoch: 23, Loss: 0.3226\n", "Epoch: 24, Loss: 0.2650\n", "Epoch: 25, Loss: 0.3203\n", "Epoch: 26, Loss: 0.3200\n", "Epoch: 27, Loss: 0.3070\n", "Epoch: 28, Loss: 0.2853\n", "Epoch: 29, Loss: 0.2931\n", "Epoch: 30, Loss: 0.3709\n", "Epoch: 31, Loss: 0.2857\n", "Epoch: 32, Loss: 0.2847\n", "Epoch: 33, Loss: 0.3104\n", "Epoch: 34, Loss: 0.2917\n", "Epoch: 35, Loss: 0.2460\n", "Epoch: 36, Loss: 0.2131\n", "Epoch: 37, Loss: 0.2229\n", "Epoch: 38, Loss: 0.2643\n", "Epoch: 39, Loss: 0.2817\n", "Epoch: 40, Loss: 0.2538\n", "Model saved to /kaggle/working/clip_caption_model.pth\n" ] } ], "source": [ "\n", "# Usage\n", "if __name__ == \"__main__\":\n", " # Train the model\n", " train()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\n", "# Inference\n", "def generate_caption(image_path, model, tokenizer, device='cuda'):\n", " transform = transforms.Compose([\n", " transforms.Resize((CFG['image_size'], CFG['image_size'])),\n", " transforms.ToTensor(),\n", " transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n", " ])\n", "\n", " image = Image.open(image_path).convert('RGB')\n", " image = transform(image).unsqueeze(0).to(device)\n", "\n", " with torch.no_grad():\n", " features = model.cnn(image)\n", " features = features.view(features.size(0), -1)\n", " img_embed = model.projection(features).unsqueeze(1)\n", "\n", " generated = model.llm.generate(\n", " inputs_embeds=img_embed,\n", " max_length=CFG['max_seq_len'],\n", " num_beams=5,\n", " early_stopping=True,\n", " pad_token_id=tokenizer.pad_token_id\n", " )\n", "\n", " return tokenizer.decode(generated[0], skip_special_tokens=True)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Image saved to /kaggle/working/downloaded_image.jpg\n" ] } ], "source": [ "import requests\n", "\n", "# URL of the image\n", "url = \"https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRQrNtVwHLPW2MKg7qW4mkom5RN9HdfHgCgMw&s\"\n", "# Path to save the image\n", "save_path = \"/kaggle/working/downloaded_image.jpg\"\n", "\n", "# Download and save the image\n", "response = requests.get(url)\n", "if response.status_code == 200:\n", " with open(save_path, \"wb\") as f:\n", " f.write(response.content)\n", " print(f\"Image saved to {save_path}\")\n", "else:\n", " print(f\"Failed to download image. Status code: {response.status_code}\")\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def generate_caption(image_path, model, tokenizer, device='cuda'):\n", " transform = transforms.Compose([\n", " transforms.Resize((CFG['image_size'], CFG['image_size'])),\n", " transforms.ToTensor(),\n", " transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n", " ])\n", "\n", " image = Image.open(image_path).convert('RGB')\n", " image = transform(image).unsqueeze(0).to(device)\n", "\n", " with torch.no_grad():\n", " features = model.cnn(image)\n", " features = features.view(features.size(0), -1)\n", " img_embed = model.projection(features).unsqueeze(1)\n", "\n", " # Add prompt tokens\n", " prompt = \"A photo of\"\n", " prompt_ids = tokenizer(prompt, return_tensors='pt').input_ids.to(device)\n", " prompt_embeds = model.llm.transformer.wte(prompt_ids)\n", "\n", " inputs_embeds = torch.cat([img_embed, prompt_embeds], dim=1)\n", " attention_mask = torch.ones(inputs_embeds.shape[:-1], dtype=torch.long).to(device)\n", "\n", " # Sampling-based generation\n", " generated = model.llm.generate(\n", " inputs_embeds=inputs_embeds,\n", " attention_mask=attention_mask,\n", " max_length=CFG['max_seq_len'],\n", " do_sample=True,\n", " top_k=50,\n", " top_p=0.95,\n", " temperature=1.0,\n", " pad_token_id=tokenizer.pad_token_id\n", " )\n", "\n", " return tokenizer.decode(generated[0], skip_special_tokens=True)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Generated Caption: \n", "\n", "I, the man, the man, the man, the man, the man, the man, the man, the man, the man, the man, the man, the man, the man, the man, the man, the man, the man\n" ] } ], "source": [ "\n", " # Example inference\n", " tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n", " model = ImageCaptionModel().cuda().eval()\n", " caption = generate_caption(save_path, model, tokenizer)\n", " print(f\"Generated Caption: {caption}\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "BLIP Caption: a small dog wearing a red sweater running through the grass\n" ] } ], "source": [ "from transformers import BlipProcessor, BlipForConditionalGeneration\n", "from PIL import Image\n", "import requests\n", "import torch\n", "\n", "# Load model\n", "processor = BlipProcessor.from_pretrained(\"Salesforce/blip-image-captioning-base\")\n", "model = BlipForConditionalGeneration.from_pretrained(\"Salesforce/blip-image-captioning-base\").to(\"cuda\")\n", "\n", "# Load image\n", "img = Image.open(\"/kaggle/working/downloaded_image.jpg\").convert('RGB')\n", "\n", "# Preprocess\n", "inputs = processor(img, return_tensors=\"pt\").to(\"cuda\")\n", "\n", "# Generate caption\n", "out = model.generate(**inputs, max_new_tokens=64)\n", "caption = processor.decode(out[0], skip_special_tokens=True)\n", "print(\"BLIP Caption:\", caption)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def generate_caption(image_path, model, tokenizer, device='cuda'):\n", " transform = transforms.Compose([\n", " transforms.Resize((CFG['image_size'], CFG['image_size'])),\n", " transforms.ToTensor(),\n", " transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n", " ])\n", "\n", " image = Image.open(image_path).convert('RGB')\n", " image = transform(image).unsqueeze(0).to(device)\n", "\n", " with torch.no_grad():\n", " features = model.cnn(image).view(1, -1)\n", " img_seq = model.projection(features).view(1, model.prefix_length, 768)\n", "\n", " # Generate from image embeddings only\n", " attention_mask = torch.ones((1, model.prefix_length), device=device)\n", "\n", " generated = model.llm.generate(\n", " inputs_embeds=img_seq,\n", " attention_mask=attention_mask,\n", " max_length=CFG['max_seq_len'],\n", " do_sample=True,\n", " top_k=50,\n", " top_p=0.95,\n", " temperature=1.0,\n", " pad_token_id=tokenizer.pad_token_id\n", " )\n", "\n", " return tokenizer.decode(generated[0], skip_special_tokens=True)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "colab": { "provenance": [], "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 0 }