Upload 28 files

Task 2/Eng_French.ipynb

@@ -0,0 +1,953 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "# Machine Translation Project (English to French)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Fetching 30 files: 100%|██████████| 30/30 [00:00<?, ?it/s]\n"
+     ]
+    },
+    {
+     "ename": "OSError",
+     "evalue": "SavedModel file does not exist at: C:\\Users\\prajw\\.cache\\huggingface\\hub\\models--prajwath--NullClass_Task-1\\snapshots\\db56a5e525e47ee9bde30fd099bea336bba3908f\\{saved_model.pbtxt|saved_model.pb}",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mOSError\u001b[0m                                   Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[6], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mhuggingface_hub\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m from_pretrained_keras\n\u001b[1;32m----> 3\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mfrom_pretrained_keras\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mprajwath/NullClass_Task-1\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\prajw\\anaconda3\\envs\\TestNullClass\\lib\\site-packages\\huggingface_hub\\keras_mixin.py:293\u001b[0m, in \u001b[0;36mfrom_pretrained_keras\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    240\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfrom_pretrained_keras\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mKerasModelHubMixin\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m    241\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    242\u001b[0m \u001b[38;5;124;03m    Instantiate a pretrained Keras model from a pre-trained model from the Hub.\u001b[39;00m\n\u001b[0;32m    243\u001b[0m \u001b[38;5;124;03m    The model is expected to be in `SavedModel` format.\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    291\u001b[0m \u001b[38;5;124;03m    </Tip>\u001b[39;00m\n\u001b[0;32m    292\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 293\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m KerasModelHubMixin\u001b[38;5;241m.\u001b[39mfrom_pretrained(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32mc:\\Users\\prajw\\anaconda3\\envs\\TestNullClass\\lib\\site-packages\\huggingface_hub\\utils\\_validators.py:114\u001b[0m, in \u001b[0;36mvalidate_hf_hub_args.<locals>._inner_fn\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    111\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m check_use_auth_token:\n\u001b[0;32m    112\u001b[0m     kwargs \u001b[38;5;241m=\u001b[39m smoothly_deprecate_use_auth_token(fn_name\u001b[38;5;241m=\u001b[39mfn\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, has_token\u001b[38;5;241m=\u001b[39mhas_token, kwargs\u001b[38;5;241m=\u001b[39mkwargs)\n\u001b[1;32m--> 114\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32mc:\\Users\\prajw\\anaconda3\\envs\\TestNullClass\\lib\\site-packages\\huggingface_hub\\hub_mixin.py:558\u001b[0m, in \u001b[0;36mModelHubMixin.from_pretrained\u001b[1;34m(cls, pretrained_model_name_or_path, force_download, resume_download, proxies, token, cache_dir, local_files_only, revision, **model_kwargs)\u001b[0m\n\u001b[0;32m    555\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_hub_mixin_inject_config \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconfig\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m model_kwargs:\n\u001b[0;32m    556\u001b[0m         model_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconfig\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m config\n\u001b[1;32m--> 558\u001b[0m instance \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_from_pretrained(\n\u001b[0;32m    559\u001b[0m     model_id\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mstr\u001b[39m(model_id),\n\u001b[0;32m    560\u001b[0m     revision\u001b[38;5;241m=\u001b[39mrevision,\n\u001b[0;32m    561\u001b[0m     cache_dir\u001b[38;5;241m=\u001b[39mcache_dir,\n\u001b[0;32m    562\u001b[0m     force_download\u001b[38;5;241m=\u001b[39mforce_download,\n\u001b[0;32m    563\u001b[0m     proxies\u001b[38;5;241m=\u001b[39mproxies,\n\u001b[0;32m    564\u001b[0m     resume_download\u001b[38;5;241m=\u001b[39mresume_download,\n\u001b[0;32m    565\u001b[0m     local_files_only\u001b[38;5;241m=\u001b[39mlocal_files_only,\n\u001b[0;32m    566\u001b[0m     token\u001b[38;5;241m=\u001b[39mtoken,\n\u001b[0;32m    567\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mmodel_kwargs,\n\u001b[0;32m    568\u001b[0m )\n\u001b[0;32m    570\u001b[0m \u001b[38;5;66;03m# Implicitly set the config as instance attribute if not already set by the class\u001b[39;00m\n\u001b[0;32m    571\u001b[0m \u001b[38;5;66;03m# This way `config` will be available when calling `save_pretrained` or `push_to_hub`.\u001b[39;00m\n\u001b[0;32m    572\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m config \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m (\u001b[38;5;28mgetattr\u001b[39m(instance, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_hub_mixin_config\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;28;01mNone\u001b[39;00m, {})):\n",
+      "File \u001b[1;32mc:\\Users\\prajw\\anaconda3\\envs\\TestNullClass\\lib\\site-packages\\huggingface_hub\\keras_mixin.py:494\u001b[0m, in \u001b[0;36mKerasModelHubMixin._from_pretrained\u001b[1;34m(cls, model_id, revision, cache_dir, force_download, proxies, resume_download, local_files_only, token, config, **model_kwargs)\u001b[0m\n\u001b[0;32m    491\u001b[0m     storage_folder \u001b[38;5;241m=\u001b[39m model_id\n\u001b[0;32m    493\u001b[0m \u001b[38;5;66;03m# TODO: change this in a future PR. We are not returning a KerasModelHubMixin instance here...\u001b[39;00m\n\u001b[1;32m--> 494\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mkeras\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodels\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstorage_folder\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    496\u001b[0m \u001b[38;5;66;03m# For now, we add a new attribute, config, to store the config loaded from the hub/a local dir.\u001b[39;00m\n\u001b[0;32m    497\u001b[0m model\u001b[38;5;241m.\u001b[39mconfig \u001b[38;5;241m=\u001b[39m config\n",
+      "File \u001b[1;32mc:\\Users\\prajw\\anaconda3\\envs\\TestNullClass\\lib\\site-packages\\keras\\utils\\traceback_utils.py:70\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     67\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[0;32m     68\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[0;32m     69\u001b[0m     \u001b[38;5;66;03m# `tf.debugging.disable_traceback_filtering()`\u001b[39;00m\n\u001b[1;32m---> 70\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m     71\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     72\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
+      "File \u001b[1;32mc:\\Users\\prajw\\anaconda3\\envs\\TestNullClass\\lib\\site-packages\\tensorflow\\python\\saved_model\\loader_impl.py:115\u001b[0m, in \u001b[0;36mparse_saved_model\u001b[1;34m(export_dir)\u001b[0m\n\u001b[0;32m    113\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIOError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot parse file \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath_to_pbtxt\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mstr\u001b[39m(e)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    114\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 115\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIOError\u001b[39;00m(\n\u001b[0;32m    116\u001b[0m       \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSavedModel file does not exist at: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mexport_dir\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mos\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39msep\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    117\u001b[0m       \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m{{\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mconstants\u001b[38;5;241m.\u001b[39mSAVED_MODEL_FILENAME_PBTXT\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m|\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    118\u001b[0m       \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconstants\u001b[38;5;241m.\u001b[39mSAVED_MODEL_FILENAME_PB\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m}}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
+      "\u001b[1;31mOSError\u001b[0m: SavedModel file does not exist at: C:\\Users\\prajw\\.cache\\huggingface\\hub\\models--prajwath--NullClass_Task-1\\snapshots\\db56a5e525e47ee9bde30fd099bea336bba3908f\\{saved_model.pbtxt|saved_model.pb}"
+     ]
+    }
+   ],
+   "source": [
+    "from huggingface_hub import from_pretrained_keras\n",
+    "\n",
+    "model = from_pretrained_keras(\"prajwath/NullClass_Task-1\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting huggingface_hub\n",
+      "  Using cached huggingface_hub-0.23.4-py3-none-any.whl.metadata (12 kB)\n",
+      "Collecting filelock (from huggingface_hub)\n",
+      "  Using cached filelock-3.15.4-py3-none-any.whl.metadata (2.9 kB)\n",
+      "Collecting fsspec>=2023.5.0 (from huggingface_hub)\n",
+      "  Using cached fsspec-2024.6.1-py3-none-any.whl.metadata (11 kB)\n",
+      "Requirement already satisfied: packaging>=20.9 in c:\\users\\prajw\\anaconda3\\envs\\testnullclass\\lib\\site-packages (from huggingface_hub) (24.0)\n",
+      "Collecting pyyaml>=5.1 (from huggingface_hub)\n",
+      "  Using cached PyYAML-6.0.1-cp39-cp39-win_amd64.whl.metadata (2.1 kB)\n",
+      "Requirement already satisfied: requests in c:\\users\\prajw\\anaconda3\\envs\\testnullclass\\lib\\site-packages (from huggingface_hub) (2.32.3)\n",
+      "Collecting tqdm>=4.42.1 (from huggingface_hub)\n",
+      "  Using cached tqdm-4.66.4-py3-none-any.whl.metadata (57 kB)\n",
+      "Requirement already satisfied: typing-extensions>=3.7.4.3 in c:\\users\\prajw\\anaconda3\\envs\\testnullclass\\lib\\site-packages (from huggingface_hub) (4.12.2)\n",
+      "Requirement already satisfied: colorama in c:\\users\\prajw\\anaconda3\\envs\\testnullclass\\lib\\site-packages (from tqdm>=4.42.1->huggingface_hub) (0.4.6)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\prajw\\anaconda3\\envs\\testnullclass\\lib\\site-packages (from requests->huggingface_hub) (3.3.2)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\prajw\\anaconda3\\envs\\testnullclass\\lib\\site-packages (from requests->huggingface_hub) (2.10)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\prajw\\anaconda3\\envs\\testnullclass\\lib\\site-packages (from requests->huggingface_hub) (2.2.1)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\prajw\\anaconda3\\envs\\testnullclass\\lib\\site-packages (from requests->huggingface_hub) (2024.6.2)\n",
+      "Downloading huggingface_hub-0.23.4-py3-none-any.whl (402 kB)\n",
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+      "Downloading tqdm-4.66.4-py3-none-any.whl (78 kB)\n",
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+      "Downloading filelock-3.15.4-py3-none-any.whl (16 kB)\n",
+      "Installing collected packages: tqdm, pyyaml, fsspec, filelock, huggingface_hub\n",
+      "Successfully installed filelock-3.15.4 fsspec-2024.6.1 huggingface_hub-0.23.4 pyyaml-6.0.1 tqdm-4.66.4\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install huggingface_hub\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import collections\n",
+    "import numpy as np\n",
+    "import json\n",
+    "\n",
+    "from keras.preprocessing.text import Tokenizer\n",
+    "from keras.utils import pad_sequences\n",
+    "from keras.models import Model, Sequential\n",
+    "from keras.layers import Input, Dense, Embedding, GRU, LSTM, Bidirectional, Dropout, Activation, TimeDistributed, RepeatVector\n",
+    "from keras.optimizers import Adam\n",
+    "from keras.losses import sparse_categorical_crossentropy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Verify access to the GPU"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[name: \"/device:CPU:0\"\n",
+      "device_type: \"CPU\"\n",
+      "memory_limit: 268435456\n",
+      "locality {\n",
+      "}\n",
+      "incarnation: 753238729468120299\n",
+      "xla_global_id: -1\n",
+      ", name: \"/device:GPU:0\"\n",
+      "device_type: \"GPU\"\n",
+      "memory_limit: 1733715559\n",
+      "locality {\n",
+      "  bus_id: 1\n",
+      "  links {\n",
+      "  }\n",
+      "}\n",
+      "incarnation: 17911561745832575813\n",
+      "physical_device_desc: \"device: 0, name: NVIDIA GeForce RTX 2050, pci bus id: 0000:01:00.0, compute capability: 8.6\"\n",
+      "xla_global_id: 416903419\n",
+      "]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tensorflow.python.client import device_lib\n",
+    "print(device_lib.list_local_devices())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Dataset\n",
+    "For our machine translation project, we opt for a dataset featuring a limited vocabulary, specifically designed to facilitate a more manageable and efficient training process. Unlike the extensive [WMT](http://www.statmt.org/) datasets, our chosen dataset ensures a quicker training time and demands fewer computational resources. This strategic decision aims to balance the learning experience while still achieving meaningful results within practical time constraints.\n",
+    "### Load Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_data(path):\n",
+    "    input_file = path\n",
+    "    with open(input_file, \"r\") as f:\n",
+    "        data = f.read()\n",
+    "    return data.split('\\n')\n",
+    "\n",
+    "english_sentences = load_data('data/english')\n",
+    "french_sentences = load_data('data/french')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sample Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['new jersey is sometimes quiet during autumn , and it is snowy in april .',\n",
+       " 'the united states is usually chilly during july , and it is usually freezing in november .',\n",
+       " 'california is usually quiet during march , and it is usually hot in june .',\n",
+       " 'the united states is sometimes mild during june , and it is cold in september .',\n",
+       " 'your least liked fruit is the grape , but my least liked is the apple .']"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "english_sentences[:5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By examining the sentences, it's apparent that they have undergone preprocessing: punctuation has been delimited with spaces, and all the text has been converted to lowercase. This preprocessing serves a crucial purpose in text preparation. Firstly, delimiting punctuation with spaces ensures that each punctuation mark is treated as a separate token, aiding the model in understanding sentence structure. Secondly, converting the entire text to lowercase standardizes the input, preventing the model from distinguishing between words solely based on their casing. This uniformity facilitates more effective training and generalization, enhancing the model's ability to grasp patterns and generate accurate translations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Structure of the Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1823250 English words.\n",
+      "227 unique English words.\n",
+      "10 Most common words in the English dataset:\n",
+      "\"is\" \",\" \".\" \"in\" \"it\" \"during\" \"the\" \"but\" \"and\" \"sometimes\"\n",
+      "\n",
+      "1961295 French words.\n",
+      "355 unique French words.\n",
+      "10 Most common words in the French dataset:\n",
+      "\"est\" \".\" \",\" \"en\" \"il\" \"les\" \"mais\" \"et\" \"la\" \"parfois\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "english_words_counter = collections.Counter([word for sentence in english_sentences for word in sentence.split()])\n",
+    "french_words_counter = collections.Counter([word for sentence in french_sentences for word in sentence.split()])\n",
+    "\n",
+    "print('{} English words.'.format(len([word for sentence in english_sentences for word in sentence.split()])))\n",
+    "print('{} unique English words.'.format(len(english_words_counter)))\n",
+    "print('10 Most common words in the English dataset:')\n",
+    "print('\"' + '\" \"'.join(list(zip(*english_words_counter.most_common(10)))[0]) + '\"')\n",
+    "\n",
+    "print()\n",
+    "print('{} French words.'.format(len([word for sentence in french_sentences for word in sentence.split()])))\n",
+    "print('{} unique French words.'.format(len(french_words_counter)))\n",
+    "print('10 Most common words in the French dataset:')\n",
+    "print('\"' + '\" \"'.join(list(zip(*french_words_counter.most_common(10)))[0]) + '\"')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Preprocess\n",
+    "1. Tokenize the words into ids\n",
+    "2. Add padding to make all the sequences the same length."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'the': 1, 'quick': 2, 'a': 3, 'brown': 4, 'fox': 5, 'jumps': 6, 'over': 7, 'lazy': 8, 'dog': 9, 'by': 10, 'jove': 11, 'my': 12, 'study': 13, 'of': 14, 'lexicography': 15, 'won': 16, 'prize': 17, 'this': 18, 'is': 19, 'short': 20, 'sentence': 21}\n",
+      "\n",
+      "Sequence 1 in x\n",
+      "  Input:  The quick brown fox jumps over the lazy dog .\n",
+      "  Output: [1, 2, 4, 5, 6, 7, 1, 8, 9]\n",
+      "Sequence 2 in x\n",
+      "  Input:  By Jove , my quick study of lexicography won a prize .\n",
+      "  Output: [10, 11, 12, 2, 13, 14, 15, 16, 3, 17]\n",
+      "Sequence 3 in x\n",
+      "  Input:  This is a short sentence .\n",
+      "  Output: [18, 19, 3, 20, 21]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def tokenize(x):\n",
+    "    tokenizer = Tokenizer()\n",
+    "    tokenizer.fit_on_texts(x)\n",
+    "    return tokenizer.texts_to_sequences(x), tokenizer\n",
+    "\n",
+    "text_sentences = [\n",
+    "    'The quick brown fox jumps over the lazy dog .',\n",
+    "    'By Jove , my quick study of lexicography won a prize .',\n",
+    "    'This is a short sentence .']\n",
+    "\n",
+    "text_tokenized, text_tokenizer = tokenize(text_sentences)\n",
+    "print(text_tokenizer.word_index)\n",
+    "print()\n",
+    "for sample_i, (sent, token_sent) in enumerate(zip(text_sentences, text_tokenized)):\n",
+    "    print('Sequence {} in x'.format(sample_i + 1))\n",
+    "    print('  Input:  {}'.format(sent))\n",
+    "    print('  Output: {}'.format(token_sent))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sequence 1 in x\n",
+      "  Input:  [1 2 4 5 6 7 1 8 9]\n",
+      "  Output: [1 2 4 5 6 7 1 8 9 0]\n",
+      "Sequence 2 in x\n",
+      "  Input:  [10 11 12  2 13 14 15 16  3 17]\n",
+      "  Output: [10 11 12  2 13 14 15 16  3 17]\n",
+      "Sequence 3 in x\n",
+      "  Input:  [18 19  3 20 21]\n",
+      "  Output: [18 19  3 20 21  0  0  0  0  0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def pad(x, length=None):\n",
+    "    if length is None:\n",
+    "        length = max([len(sentence) for sentence in x])\n",
+    "    return pad_sequences(x, maxlen=length, padding='post')\n",
+    "\n",
+    "test_pad = pad(text_tokenized)\n",
+    "for sample_i, (token_sent, pad_sent) in enumerate(zip(text_tokenized, test_pad)):\n",
+    "    print('Sequence {} in x'.format(sample_i + 1))\n",
+    "    print('  Input:  {}'.format(np.array(token_sent)))\n",
+    "    print('  Output: {}'.format(pad_sent))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data Preprocessed\n",
+      "Max English sentence length: 15\n",
+      "Max French sentence length: 21\n",
+      "English vocabulary size: 199\n",
+      "French vocabulary size: 344\n"
+     ]
+    }
+   ],
+   "source": [
+    "def preprocess(x,y):\n",
+    "    preprocess_x, x_tk = tokenize(x)\n",
+    "    preprocess_y, y_tk = tokenize(y)\n",
+    "    \n",
+    "    preprocess_x = pad(preprocess_x)\n",
+    "    preprocess_y = pad(preprocess_y)\n",
+    "    \n",
+    "    preprocess_y = preprocess_y.reshape(*preprocess_y.shape, 1)\n",
+    "    \n",
+    "    return preprocess_x, preprocess_y, x_tk, y_tk\n",
+    "\n",
+    "preproc_english_sentences, preproc_french_sentences, english_tokenizer, french_tokenizer = preprocess(english_sentences, french_sentences)\n",
+    "\n",
+    "max_english_sequence_length = preproc_english_sentences.shape[1]\n",
+    "max_french_sequence_length = preproc_french_sentences.shape[1]\n",
+    "english_vocab_size = len(english_tokenizer.word_index)\n",
+    "french_vocab_size = len(french_tokenizer.word_index)\n",
+    "\n",
+    "print('Data Preprocessed')\n",
+    "print(\"Max English sentence length:\", max_english_sequence_length)\n",
+    "print(\"Max French sentence length:\", max_french_sequence_length)\n",
+    "print(\"English vocabulary size:\", english_vocab_size)\n",
+    "print(\"French vocabulary size:\", french_vocab_size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Models\n",
+    "- Model 1 is a simple RNN\n",
+    "- Model 2 is a Bidirectional RNN\n",
+    "- Model 3 is an Embedding RNN\n",
+    "\n",
+    "### Ids Back to Text\n",
+    "The neural network will be translating the input to words ids, which isn't the final form we want.  We want the French translation.  The function `logits_to_text` will bridge the gab between the logits from the neural network to the French translation.  You'll be using this function to better understand the output of the neural network."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def logits_to_text(logits, tokenizer):\n",
+    "    index_to_words = {id: word for word, id in tokenizer.word_index.items()}\n",
+    "    index_to_words[0] = '<PAD>'\n",
+    "    \n",
+    "    return ' '.join([index_to_words[prediction] for prediction in np.argmax(logits, 1)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model 1: RNN\n",
+    "![RNN](images/rnn.png)\n",
+    "A basic RNN model is a good baseline for sequence data.  In this model, you'll build a RNN that translates English to French."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "108/108 [==============================] - 21s 132ms/step - loss: 1.9204 - accuracy: 0.5445 - val_loss: nan - val_accuracy: 0.6271\n",
+      "Epoch 2/10\n",
+      "108/108 [==============================] - 13s 119ms/step - loss: 1.2280 - accuracy: 0.6415 - val_loss: nan - val_accuracy: 0.6736\n",
+      "Epoch 3/10\n",
+      "108/108 [==============================] - 13s 119ms/step - loss: 1.0781 - accuracy: 0.6701 - val_loss: nan - val_accuracy: 0.7027\n",
+      "Epoch 4/10\n",
+      "108/108 [==============================] - 13s 122ms/step - loss: 0.9893 - accuracy: 0.6861 - val_loss: nan - val_accuracy: 0.7056\n",
+      "Epoch 5/10\n",
+      "108/108 [==============================] - 13s 122ms/step - loss: 0.9328 - accuracy: 0.6960 - val_loss: nan - val_accuracy: 0.7206\n",
+      "Epoch 6/10\n",
+      "108/108 [==============================] - 12s 113ms/step - loss: 0.8917 - accuracy: 0.7037 - val_loss: nan - val_accuracy: 0.7074\n",
+      "Epoch 7/10\n",
+      "108/108 [==============================] - 12s 109ms/step - loss: 0.8539 - accuracy: 0.7123 - val_loss: nan - val_accuracy: 0.7419\n",
+      "Epoch 8/10\n",
+      "108/108 [==============================] - 12s 114ms/step - loss: 0.8136 - accuracy: 0.7258 - val_loss: nan - val_accuracy: 0.7366\n",
+      "Epoch 9/10\n",
+      "108/108 [==============================] - 13s 117ms/step - loss: 0.7947 - accuracy: 0.7312 - val_loss: nan - val_accuracy: 0.7469\n",
+      "Epoch 10/10\n",
+      "108/108 [==============================] - 13s 117ms/step - loss: 0.7671 - accuracy: 0.7396 - val_loss: nan - val_accuracy: 0.7694\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x1d1836779d0>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def simple_model(input_shape, output_sequence_length, english_vocab_size, french_vocab_size):\n",
+    "    \n",
+    "    #Hyperparameters\n",
+    "    learning_rate = 0.005\n",
+    "    \n",
+    "    # Build the layers\n",
+    "    model = Sequential()\n",
+    "    model.add(GRU(256, input_shape=input_shape[1:], return_sequences=True))\n",
+    "    model.add(TimeDistributed(Dense(1024, activation='relu')))\n",
+    "    model.add(Dropout(0.5))\n",
+    "    model.add(TimeDistributed(Dense(french_vocab_size, activation='softmax')))\n",
+    "    \n",
+    "    # Compile model\n",
+    "    model.compile(loss = sparse_categorical_crossentropy,\n",
+    "                  optimizer = Adam(learning_rate),\n",
+    "                  metrics = ['accuracy'])\n",
+    "    \n",
+    "    return model\n",
+    "\n",
+    "tmp_x = pad(preproc_english_sentences, max_french_sequence_length)\n",
+    "tmp_x = tmp_x.reshape((-1, preproc_french_sentences.shape[-2], 1))\n",
+    "\n",
+    "#Train the neural network\n",
+    "simple_rnn_model = simple_model(\n",
+    "    tmp_x.shape,\n",
+    "    max_french_sequence_length,\n",
+    "    english_vocab_size,\n",
+    "    french_vocab_size)\n",
+    "\n",
+    "simple_rnn_model.fit(tmp_x, preproc_french_sentences, batch_size=1024, epochs=10, validation_split=0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prediciton:\n",
+      "1/1 [==============================] - 0s 278ms/step\n",
+      "new jersey est parfois calme en mois de il et il est en en <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD>\n",
+      "\n",
+      "Correct Translation:\n",
+      "[\"new jersey est parfois calme pendant l' automne , et il est neigeux en avril .\"]\n",
+      "\n",
+      "Original text:\n",
+      "['new jersey is sometimes quiet during autumn , and it is snowy in april .']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print prediction(s)\n",
+    "print(\"Prediciton:\")\n",
+    "print(logits_to_text(simple_rnn_model.predict(tmp_x[:1])[0], french_tokenizer))\n",
+    "\n",
+    "print(\"\\nCorrect Translation:\")\n",
+    "print(french_sentences[:1])\n",
+    "\n",
+    "print('\\nOriginal text:')\n",
+    "print(english_sentences[:1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model 2: Bidirectional RNNs\n",
+    "![RNN](images/bidirectional.png)\n",
+    "One restriction of a RNN is that it can't see the future input, only the past.  This is where bidirectional recurrent neural networks come in.  They are able to see the future data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"sequential_1\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " bidirectional (Bidirectiona  (None, 21, 256)          100608    \n",
+      " l)                                                              \n",
+      "                                                                 \n",
+      " time_distributed_2 (TimeDis  (None, 21, 1024)         263168    \n",
+      " tributed)                                                       \n",
+      "                                                                 \n",
+      " dropout_1 (Dropout)         (None, 21, 1024)          0         \n",
+      "                                                                 \n",
+      " time_distributed_3 (TimeDis  (None, 21, 344)          352600    \n",
+      " tributed)                                                       \n",
+      "                                                                 \n",
+      "=================================================================\n",
+      "Total params: 716,376\n",
+      "Trainable params: 716,376\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n",
+      "None\n",
+      "Epoch 1/10\n",
+      "108/108 [==============================] - 15s 121ms/step - loss: 1.7581 - accuracy: 0.5748 - val_loss: nan - val_accuracy: 0.6500\n",
+      "Epoch 2/10\n",
+      "108/108 [==============================] - 13s 117ms/step - loss: 1.1684 - accuracy: 0.6552 - val_loss: nan - val_accuracy: 0.6823\n",
+      "Epoch 3/10\n",
+      "108/108 [==============================] - 13s 118ms/step - loss: 1.0394 - accuracy: 0.6766 - val_loss: nan - val_accuracy: 0.6961\n",
+      "Epoch 4/10\n",
+      "108/108 [==============================] - 13s 118ms/step - loss: 0.9543 - accuracy: 0.6900 - val_loss: nan - val_accuracy: 0.7073\n",
+      "Epoch 5/10\n",
+      "108/108 [==============================] - 13s 118ms/step - loss: 0.8969 - accuracy: 0.6993 - val_loss: nan - val_accuracy: 0.7155\n",
+      "Epoch 6/10\n",
+      "108/108 [==============================] - 13s 118ms/step - loss: 0.8589 - accuracy: 0.7062 - val_loss: nan - val_accuracy: 0.7109\n",
+      "Epoch 7/10\n",
+      "108/108 [==============================] - 13s 118ms/step - loss: 0.8371 - accuracy: 0.7107 - val_loss: nan - val_accuracy: 0.7233\n",
+      "Epoch 8/10\n",
+      "108/108 [==============================] - 13s 118ms/step - loss: 0.7975 - accuracy: 0.7195 - val_loss: nan - val_accuracy: 0.7446\n",
+      "Epoch 9/10\n",
+      "108/108 [==============================] - 13s 118ms/step - loss: 0.7670 - accuracy: 0.7282 - val_loss: nan - val_accuracy: 0.7498\n",
+      "Epoch 10/10\n",
+      "108/108 [==============================] - 13s 117ms/step - loss: 0.7318 - accuracy: 0.7401 - val_loss: nan - val_accuracy: 0.7604\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x1d184e5adf0>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def bd_model(input_shape, output_sequence_length, english_vocab_size, french_vocab_size):\n",
+    "    \n",
+    "    #Hyperparameters\n",
+    "    learning_rate = 0.005\n",
+    "    \n",
+    "    # Build the layers\n",
+    "    model = Sequential()\n",
+    "    model.add(Bidirectional(GRU(128, return_sequences=True), input_shape=input_shape[1:]))\n",
+    "    model.add(TimeDistributed(Dense(1024, activation='relu')))\n",
+    "    model.add(Dropout(0.5))\n",
+    "    model.add(TimeDistributed(Dense(french_vocab_size, activation='softmax')))\n",
+    "    \n",
+    "    # Compile model\n",
+    "    model.compile(loss = sparse_categorical_crossentropy,\n",
+    "                  optimizer = Adam(learning_rate),\n",
+    "                  metrics = ['accuracy'])\n",
+    "    \n",
+    "    return model\n",
+    "\n",
+    "tmp_x = pad(preproc_english_sentences, max_french_sequence_length)\n",
+    "tmp_x = tmp_x.reshape((-1, preproc_french_sentences.shape[-2], 1))\n",
+    "\n",
+    "# Train the neural network\n",
+    "bd_rnn_model = bd_model(\n",
+    "    tmp_x.shape,\n",
+    "    max_french_sequence_length,\n",
+    "    english_vocab_size,\n",
+    "    french_vocab_size)\n",
+    "\n",
+    "print(bd_rnn_model.summary())\n",
+    "\n",
+    "bd_rnn_model.fit(tmp_x, preproc_french_sentences, batch_size=1024, epochs=10, validation_split=0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prediciton:\n",
+      "1/1 [==============================] - 1s 569ms/step\n",
+      "new jersey est parfois chaud en mois de il et est est en en <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD>\n",
+      "\n",
+      "Correct Translation:\n",
+      "[\"new jersey est parfois calme pendant l' automne , et il est neigeux en avril .\"]\n",
+      "\n",
+      "Original text:\n",
+      "['new jersey is sometimes quiet during autumn , and it is snowy in april .']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print prediction(s)\n",
+    "print(\"Prediciton:\")\n",
+    "print(logits_to_text(bd_rnn_model.predict(tmp_x[:1])[0], french_tokenizer))\n",
+    "\n",
+    "print(\"\\nCorrect Translation:\")\n",
+    "print(french_sentences[:1])\n",
+    "\n",
+    "print('\\nOriginal text:')\n",
+    "print(english_sentences[:1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model 3: Embedding\n",
+    "![RNN](images/embedding-words.png)\n",
+    "You've turned the words into ids, but there's a better representation of a word.  This is called word embeddings.  An embedding is a vector representation of the word that is close to similar words in n-dimensional space, where the n represents the size of the embedding vectors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"sequential_2\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " embedding (Embedding)       (None, 21, 256)           50944     \n",
+      "                                                                 \n",
+      " bidirectional_1 (Bidirectio  (None, 21, 512)          789504    \n",
+      " nal)                                                            \n",
+      "                                                                 \n",
+      " time_distributed_4 (TimeDis  (None, 21, 1024)         525312    \n",
+      " tributed)                                                       \n",
+      "                                                                 \n",
+      " dropout_2 (Dropout)         (None, 21, 1024)          0         \n",
+      "                                                                 \n",
+      " time_distributed_5 (TimeDis  (None, 21, 344)          352600    \n",
+      " tributed)                                                       \n",
+      "                                                                 \n",
+      "=================================================================\n",
+      "Total params: 1,718,360\n",
+      "Trainable params: 1,718,360\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n",
+      "None\n",
+      "Epoch 1/10\n",
+      "108/108 [==============================] - 21s 168ms/step - loss: 1.3566 - accuracy: 0.6890 - val_loss: nan - val_accuracy: 0.8730\n",
+      "Epoch 2/10\n",
+      "108/108 [==============================] - 16s 151ms/step - loss: 0.3153 - accuracy: 0.9007 - val_loss: nan - val_accuracy: 0.9377\n",
+      "Epoch 3/10\n",
+      "108/108 [==============================] - 17s 154ms/step - loss: 0.1827 - accuracy: 0.9428 - val_loss: nan - val_accuracy: 0.9572\n",
+      "Epoch 4/10\n",
+      "108/108 [==============================] - 17s 154ms/step - loss: 0.1322 - accuracy: 0.9589 - val_loss: nan - val_accuracy: 0.9685\n",
+      "Epoch 5/10\n",
+      "108/108 [==============================] - 17s 154ms/step - loss: 0.1035 - accuracy: 0.9680 - val_loss: nan - val_accuracy: 0.9734\n",
+      "Epoch 6/10\n",
+      "108/108 [==============================] - 17s 156ms/step - loss: 0.0864 - accuracy: 0.9734 - val_loss: nan - val_accuracy: 0.9764\n",
+      "Epoch 7/10\n",
+      "108/108 [==============================] - 17s 156ms/step - loss: 0.0755 - accuracy: 0.9767 - val_loss: nan - val_accuracy: 0.9774\n",
+      "Epoch 8/10\n",
+      "108/108 [==============================] - 17s 157ms/step - loss: 0.0659 - accuracy: 0.9795 - val_loss: nan - val_accuracy: 0.9805\n",
+      "Epoch 9/10\n",
+      "108/108 [==============================] - 17s 159ms/step - loss: 0.0604 - accuracy: 0.9812 - val_loss: nan - val_accuracy: 0.9813\n",
+      "Epoch 10/10\n",
+      "108/108 [==============================] - 17s 158ms/step - loss: 0.0559 - accuracy: 0.9827 - val_loss: nan - val_accuracy: 0.9825\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x1d183c75460>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def bidirectional_embed_model(input_shape, output_sequence_length, english_vocab_size, french_vocab_size):\n",
+    "    \n",
+    "    # Hyperparameters\n",
+    "    learning_rate = 0.005\n",
+    "    \n",
+    "    # Build the layers\n",
+    "    model = Sequential()\n",
+    "    model.add(Embedding(english_vocab_size, 256, input_length=input_shape[1], input_shape=input_shape[1:]))\n",
+    "    model.add(Bidirectional(GRU(256, return_sequences=True)))\n",
+    "    model.add(TimeDistributed(Dense(1024, activation='relu')))\n",
+    "    model.add(Dropout(0.5))\n",
+    "    model.add(TimeDistributed(Dense(french_vocab_size, activation='softmax')))\n",
+    "    \n",
+    "    # Compile model\n",
+    "    model.compile(loss = sparse_categorical_crossentropy,\n",
+    "                  optimizer = Adam(learning_rate),\n",
+    "                  metrics = ['accuracy'])\n",
+    "    \n",
+    "    return model\n",
+    "\n",
+    "tmp_x = pad(preproc_english_sentences, max_french_sequence_length)\n",
+    "tmp_x = tmp_x.reshape((-1, preproc_french_sentences.shape[-2]))\n",
+    "\n",
+    "# Build the model\n",
+    "embed_rnn_model = bidirectional_embed_model(\n",
+    "    tmp_x.shape,\n",
+    "    max_french_sequence_length,\n",
+    "    english_vocab_size,\n",
+    "    french_vocab_size)\n",
+    "\n",
+    "print(embed_rnn_model.summary())\n",
+    "\n",
+    "embed_rnn_model.fit(tmp_x, preproc_french_sentences, batch_size=1024, epochs=10, validation_split=0.2)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prediciton:\n",
+      "1/1 [==============================] - 0s 479ms/step\n",
+      "new jersey est parfois calme pendant l' automne et il est neigeux en avril <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD>\n",
+      "\n",
+      "Correct Translation:\n",
+      "[\"new jersey est parfois calme pendant l' automne , et il est neigeux en avril .\"]\n",
+      "\n",
+      "Original text:\n",
+      "['new jersey is sometimes quiet during autumn , and it is snowy in april .']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print prediction(s)\n",
+    "print(\"Prediciton:\")\n",
+    "print(logits_to_text(embed_rnn_model.predict(tmp_x[:1])[0], french_tokenizer))\n",
+    "\n",
+    "print(\"\\nCorrect Translation:\")\n",
+    "print(french_sentences[:1])\n",
+    "\n",
+    "print('\\nOriginal text:')\n",
+    "print(english_sentences[:1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:Found untraced functions such as gru_cell_5_layer_call_fn, gru_cell_5_layer_call_and_return_conditional_losses, gru_cell_6_layer_call_fn, gru_cell_6_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Assets written to: english_to_french_model\\assets\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Assets written to: english_to_french_model\\assets\n"
+     ]
+    }
+   ],
+   "source": [
+    "embed_rnn_model.save('english_to_french_model')\n",
+    "# Serialize English Tokenizer to JSON\n",
+    "with open('english_tokenizer.json', 'w', encoding='utf8') as f:\n",
+    "    f.write(json.dumps(english_tokenizer.to_json(), ensure_ascii=False))\n",
+    "    \n",
+    "# Serialize French Tokenizer to JSON\n",
+    "with open('french_tokenizer.json', 'w', encoding='utf8') as f:\n",
+    "    f.write(json.dumps(french_tokenizer.to_json(), ensure_ascii=False))\n",
+    "    \n",
+    "# Save max lengths\n",
+    "max_french_sequence_length_json = max_french_sequence_length\n",
+    "with open('sequence_length.json', 'w', encoding='utf8') as f:\n",
+    "    f.write(json.dumps(max_french_sequence_length_json, ensure_ascii=False))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "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": "3.9.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

Task 2/Eng_Spanish.ipynb

@@ -0,0 +1,726 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Machine Translation Project (English to Spanish)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pathlib\n",
+    "import random\n",
+    "import string\n",
+    "import tensorflow.strings as tf_strings\n",
+    "import tensorflow.data as tf_data\n",
+    "import re\n",
+    "from keras.layers import TextVectorization\n",
+    "import keras\n",
+    "import tensorflow as tf\n",
+    "from keras import layers\n",
+    "import json"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Verify access to the GPU"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[name: \"/device:CPU:0\"\n",
+      "device_type: \"CPU\"\n",
+      "memory_limit: 268435456\n",
+      "locality {\n",
+      "}\n",
+      "incarnation: 11075625745611853481\n",
+      "xla_global_id: -1\n",
+      ", name: \"/device:GPU:0\"\n",
+      "device_type: \"GPU\"\n",
+      "memory_limit: 1733715559\n",
+      "locality {\n",
+      "  bus_id: 1\n",
+      "  links {\n",
+      "  }\n",
+      "}\n",
+      "incarnation: 17906485139926134931\n",
+      "physical_device_desc: \"device: 0, name: NVIDIA GeForce RTX 2050, pci bus id: 0000:01:00.0, compute capability: 8.6\"\n",
+      "xla_global_id: 416903419\n",
+      "]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tensorflow.python.client import device_lib\n",
+    "print(device_lib.list_local_devices())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Download and prepare the data\n",
+    "source :\"http://storage.googleapis.com/download.tensorflow.org/data/spa-eng.zip\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text_file =  keras.utils.get_file(\n",
+    "    fname = \"spa-eng.zip\",\n",
+    "    origin = \"http://storage.googleapis.com/download.tensorflow.org/data/spa-eng.zip\",\n",
+    "    extract = True,\n",
+    ")\n",
+    "\n",
+    "text_file = pathlib.Path(text_file).parent / \"spa-eng\" / \"spa.txt\"\n",
+    "\n",
+    "with open(text_file, \"r\") as f:\n",
+    "    lines = f.read().split(\"\\n\")[:-1]\n",
+    "    \n",
+    "text_pairs = []\n",
+    "\n",
+    "for line in lines:\n",
+    "    eng, spa = line.split(\"\\t\")\n",
+    "    spa = \"[start] \" + spa + \" [end]\"\n",
+    "    text_pairs.append((eng, spa))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "random.shuffle(text_pairs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('Tom came here on his own.', '[start] Tom vino aquí por sí solo. [end]')\n",
+      "(\"I don't want Tom to see me naked.\", '[start] No quiero que Tom me vea desnuda. [end]')\n",
+      "('Stand back!', '[start] ¡Retrocede! [end]')\n",
+      "(\"He's quite formal when he meets a stranger.\", '[start] Es muy ceremonioso cuando le presentan una persona desconocida. [end]')\n",
+      "(\"I'm stuck in a traffic jam.\", '[start] Estoy atrapado en un atasco. [end]')\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in range(5):\n",
+    "    print(text_pairs[i])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Structure of the Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "118964 total pairs\n",
+      "83276 training pairs\n",
+      "17844 validation pairs\n",
+      "17844 test pairs\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_val_samples = int(0.15 * len(text_pairs))\n",
+    "num_train_samples = len(text_pairs) - 2 * num_val_samples\n",
+    "train_pairs = text_pairs[:num_train_samples]\n",
+    "val_pairs = text_pairs[num_train_samples:num_train_samples + num_val_samples]\n",
+    "test_pairs = text_pairs[num_train_samples + num_val_samples:]\n",
+    "\n",
+    "print(f\"{len(text_pairs)} total pairs\")\n",
+    "print(f\"{len(train_pairs)} training pairs\")\n",
+    "print(f\"{len(val_pairs)} validation pairs\")\n",
+    "print(f\"{len(test_pairs)} test pairs\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# parameters\n",
+    "strip_chars = string.punctuation + \"¿\"\n",
+    "strip_chars = strip_chars.replace(\"[\", \"\")\n",
+    "strip_chars = strip_chars.replace(\"]\", \"\")\n",
+    "\n",
+    "vocab_size = 15000\n",
+    "sequence_length = 20\n",
+    "batch_size = 64"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Vectorize the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def custom_standardization(input_string):\n",
+    "    lowercase = tf_strings.lower(input_string)\n",
+    "    return tf_strings.regex_replace(lowercase, f\"[{re.escape(strip_chars)}]\", \"\")\n",
+    "\n",
+    "# vectorization\n",
+    "eng_vectorization = TextVectorization(\n",
+    "    max_tokens = vocab_size,\n",
+    "    output_mode = \"int\",\n",
+    "    output_sequence_length = sequence_length,\n",
+    ")\n",
+    "\n",
+    "spa_vectorization = TextVectorization(\n",
+    "    max_tokens = vocab_size,\n",
+    "    output_mode = \"int\",\n",
+    "    output_sequence_length = sequence_length + 1,\n",
+    "    standardize = custom_standardization,\n",
+    ")\n",
+    "\n",
+    "train_eng_texts = [pair[0] for pair in train_pairs]\n",
+    "train_spa_texts = [pair[1] for pair in train_pairs]\n",
+    "\n",
+    "eng_vectorization.adapt(train_eng_texts)\n",
+    "spa_vectorization.adapt(train_spa_texts)\n",
+    "\n",
+    "#save the vectorization layers\n",
+    "eng_vectorization_config = eng_vectorization.get_config()\n",
+    "eng_vectorization_config.pop('standardize', None)\n",
+    "eng_vocab = eng_vectorization.get_vocabulary()\n",
+    "with open('eng_vectorization_config.json', 'w', encoding='utf-8') as f:\n",
+    "    json.dump(eng_vectorization_config, f)\n",
+    "    \n",
+    "with open('eng_vocab.json', 'w', encoding='utf-8') as f:\n",
+    "    json.dump(eng_vocab, f)\n",
+    "    \n",
+    "spa_vectorization_config = spa_vectorization.get_config()\n",
+    "spa_vectorization_config.pop('standardize', None)\n",
+    "spa_vocab = spa_vectorization.get_vocabulary()\n",
+    "with open('spa_vectorization_config.json', 'w', encoding='utf-8') as f:\n",
+    "    json.dump(spa_vectorization_config, f)\n",
+    "    \n",
+    "with open('spa_vocab.json', 'w', encoding='utf-8') as f:\n",
+    "    json.dump(spa_vocab, f)\n",
+    "    \n",
+    "\n",
+    "def format_dataset(eng, spa):\n",
+    "    eng = eng_vectorization(eng)\n",
+    "    spa = spa_vectorization(spa)\n",
+    "    return (\n",
+    "        {\n",
+    "            \"encoder_inputs\": eng,\n",
+    "            \"decoder_inputs\": spa[:, :-1],\n",
+    "        },\n",
+    "        spa[:, 1:],\n",
+    "    )\n",
+    "    \n",
+    "def make_dataset(pairs):\n",
+    "    eng_texts, spa_texts = zip(*pairs)\n",
+    "    eng_texts = list(eng_texts)\n",
+    "    spa_texts = list(spa_texts)\n",
+    "    dataset = tf_data.Dataset.from_tensor_slices((eng_texts, spa_texts))\n",
+    "    dataset = dataset.batch(batch_size)\n",
+    "    dataset = dataset.map(format_dataset)\n",
+    "    return dataset.cache().shuffle(2048).prefetch(16)\n",
+    "\n",
+    "train_ds = make_dataset(train_pairs)\n",
+    "val_ds = make_dataset(val_pairs)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(64, 20)\n",
+      "(64, 20)\n"
+     ]
+    }
+   ],
+   "source": [
+    "for inputs,targets in train_ds.take(1):\n",
+    "    print(inputs[\"encoder_inputs\"].shape)\n",
+    "    print(targets.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model Architecture\n",
+    "![Encoder-Decoder](images/encoder-decoder-context.png)\n",
+    "![Encoder-Decoder](images/encoder-decoder-translation.png)\n",
+    "![Attention Mechanism](images/attention.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Creating an Encoder\n",
+    "class TransformerEncoder(layers.Layer):\n",
+    "    def __init__(self, embed_dim, dense_dim, num_heads, **kwargs):\n",
+    "        super().__init__(**kwargs)\n",
+    "        self.embed_dim = embed_dim\n",
+    "        self.dense_dim = dense_dim\n",
+    "        self.num_heads = num_heads\n",
+    "        self.attention = layers.MultiHeadAttention(\n",
+    "            num_heads = num_heads, key_dim = embed_dim\n",
+    "        )\n",
+    "        self.dense_proj = keras.Sequential(\n",
+    "            [\n",
+    "                layers.Dense(dense_dim, activation = \"relu\"),\n",
+    "                layers.Dense(embed_dim),\n",
+    "            ]\n",
+    "        )\n",
+    "        self.layernorm_1 = layers.LayerNormalization()\n",
+    "        self.layernorm_2 = layers.LayerNormalization()\n",
+    "        self.supports_masking = True\n",
+    "        \n",
+    "    def call(self, inputs, mask=None):\n",
+    "        if mask is not None:\n",
+    "            padding_mask = tf.cast(mask[:, None, :], dtype = tf.int32)\n",
+    "        else:\n",
+    "            padding_mask = None\n",
+    "            \n",
+    "        attention_output = self.attention(\n",
+    "            query = inputs,\n",
+    "            value = inputs,\n",
+    "            key = inputs,\n",
+    "            attention_mask = padding_mask,\n",
+    "        )\n",
+    "        proj_input = self.layernorm_1(inputs + attention_output)\n",
+    "        proj_output = self.dense_proj(proj_input)\n",
+    "        return self.layernorm_2(proj_input + proj_output)\n",
+    "    \n",
+    "    def get_config(self):\n",
+    "        config = super().get_config()\n",
+    "        config.update({\n",
+    "            \"embed_dim\": self.embed_dim,\n",
+    "            \"dense_dim\": self.dense_dim,\n",
+    "            \"num_heads\": self.num_heads,\n",
+    "        })\n",
+    "        return config\n",
+    "    \n",
+    "# Creating a Positional Embedding\n",
+    "class PositionalEmbedding(layers.Layer):\n",
+    "    def __init__(self, sequence_length, vocab_size, embed_dim, **kwargs):\n",
+    "        super().__init__(**kwargs)\n",
+    "        self.token_embeddings = layers.Embedding(\n",
+    "            input_dim = vocab_size, output_dim = embed_dim\n",
+    "        )\n",
+    "        self.position_embeddings = layers.Embedding(\n",
+    "            input_dim = sequence_length, output_dim = embed_dim\n",
+    "        )\n",
+    "        self.sequence_length = sequence_length\n",
+    "        self.vocab_size = vocab_size\n",
+    "        self.embed_dim = embed_dim\n",
+    "        \n",
+    "    def call(self, inputs):\n",
+    "        length = tf.shape(inputs)[-1]\n",
+    "        positions = tf.range(start = 0, limit = length, delta = 1)\n",
+    "        embedded_tokens = self.token_embeddings(inputs)\n",
+    "        embedded_positions = self.position_embeddings(positions)\n",
+    "        return embedded_tokens + embedded_positions\n",
+    "    \n",
+    "    def compute_mask(self, inputs, mask=None):\n",
+    "        if mask is not None:\n",
+    "            return tf.not_equal(inputs, 0)\n",
+    "        else:\n",
+    "            return None\n",
+    "        \n",
+    "    def get_config(self):\n",
+    "        config = super().get_config()\n",
+    "        config.update({\n",
+    "            \"vocab_size\": self.vocab_size,\n",
+    "            \"sequence_length\": self.sequence_length,\n",
+    "            \"embed_dim\": self.embed_dim,\n",
+    "        })\n",
+    "        return config\n",
+    "    \n",
+    "# Creating a Decoder\n",
+    "class TransformerDecoder(layers.Layer):\n",
+    "    def __init__(self, embed_dim, latent_dim, num_heads, **kwargs):\n",
+    "        super().__init__(**kwargs)\n",
+    "        self.embed_dim = embed_dim\n",
+    "        self.latent_dim = latent_dim\n",
+    "        self.num_heads = num_heads\n",
+    "        self.attention_1 = layers.MultiHeadAttention(\n",
+    "            num_heads = num_heads, key_dim = embed_dim\n",
+    "        )\n",
+    "        self.attention_2 = layers.MultiHeadAttention(\n",
+    "            num_heads = num_heads, key_dim = embed_dim\n",
+    "        )\n",
+    "        self.dense_proj = keras.Sequential(\n",
+    "            [\n",
+    "                layers.Dense(latent_dim, activation = \"relu\"),\n",
+    "                layers.Dense(embed_dim),\n",
+    "            ]\n",
+    "        )\n",
+    "        self.layernorm_1 = layers.LayerNormalization()\n",
+    "        self.layernorm_2 = layers.LayerNormalization()\n",
+    "        self.layernorm_3 = layers.LayerNormalization()\n",
+    "        self.supports_masking = True\n",
+    "        \n",
+    "    def call(self, inputs, encoder_outputs, mask=None):\n",
+    "        casual_mask = self.get_causal_attention_mask(inputs)\n",
+    "        if mask is not None:\n",
+    "            padding_mask = tf.cast(mask[:, None, :], dtype = tf.int32)\n",
+    "            padding_mask = tf.minimum(padding_mask, casual_mask)\n",
+    "        else:\n",
+    "            padding_mask = None\n",
+    "            \n",
+    "        attention_output_1 = self.attention_1(\n",
+    "            query = inputs,\n",
+    "            value = inputs,\n",
+    "            key = inputs,\n",
+    "            attention_mask = casual_mask,\n",
+    "        )\n",
+    "        out_1 = self.layernorm_1(inputs + attention_output_1)\n",
+    "        \n",
+    "        attention_output_2 = self.attention_2(\n",
+    "            query = out_1,\n",
+    "            value = encoder_outputs,\n",
+    "            key = encoder_outputs,\n",
+    "            attention_mask = padding_mask,\n",
+    "        )\n",
+    "        \n",
+    "        out_2 = self.layernorm_2(out_1 + attention_output_2)\n",
+    "        proj_output = self.dense_proj(out_2)\n",
+    "        \n",
+    "        return self.layernorm_3(out_2 + proj_output)\n",
+    "    \n",
+    "    def get_causal_attention_mask(self, inputs):\n",
+    "        input_shape = tf.shape(inputs)\n",
+    "        batch_size, sequence_length = input_shape[0], input_shape[1]\n",
+    "        i = tf.range(sequence_length)[:, None]\n",
+    "        j = tf.range(sequence_length)\n",
+    "        mask = tf.cast(i >= j, tf.int32)\n",
+    "        mask = tf.reshape(mask,(1, input_shape[1], input_shape[1]))\n",
+    "        mult = tf.concat(\n",
+    "            [\n",
+    "                tf.expand_dims(batch_size, -1),\n",
+    "                tf.convert_to_tensor([1, 1]),\n",
+    "            ],\n",
+    "            axis = 0,\n",
+    "        )\n",
+    "        return tf.tile(mask, mult)\n",
+    "    \n",
+    "    def get_config(self):\n",
+    "        config = super().get_config()\n",
+    "        config.update({\n",
+    "            \"embed_dim\": self.embed_dim,\n",
+    "            \"latent_dim\": self.latent_dim,\n",
+    "            \"num_heads\": self.num_heads,\n",
+    "        })\n",
+    "        return config\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define emmbedding dimensions, latent dimensions, and number of heads\n",
+    "embed_dim = 256\n",
+    "latent_dim = 2048\n",
+    "num_heads = 8\n",
+    "\n",
+    "#Encoder\n",
+    "encoder_inputs = keras.Input(shape = (None,), dtype = \"int64\", name = \"encoder_inputs\")\n",
+    "\n",
+    "x = PositionalEmbedding(sequence_length, vocab_size, embed_dim)(encoder_inputs)\n",
+    "\n",
+    "encoder_outputs = TransformerEncoder(embed_dim, latent_dim, num_heads)(x)\n",
+    "\n",
+    "encoder = keras.Model(encoder_inputs, encoder_outputs, name = \"encoder\")\n",
+    "\n",
+    "#Decoder\n",
+    "decoder_inputs = keras.Input(shape = (None,), dtype = \"int64\", name = \"decoder_inputs\")\n",
+    "encoder_seq_inputs = keras.Input(shape = (None, embed_dim), name = \"encoder_seq_inputs\")\n",
+    "\n",
+    "x = PositionalEmbedding(sequence_length, vocab_size, embed_dim)(decoder_inputs)\n",
+    "\n",
+    "x = TransformerDecoder(embed_dim, latent_dim, num_heads)(x, encoder_seq_inputs)\n",
+    "\n",
+    "x = layers.Dropout(0.5)(x)\n",
+    "\n",
+    "decoder_outputs = layers.Dense(vocab_size, activation = \"softmax\")(x)\n",
+    "\n",
+    "decoder = keras.Model([decoder_inputs, encoder_seq_inputs], decoder_outputs, name = \"decoder\")\n",
+    "\n",
+    "# Define the final model\n",
+    "decoder_outputs = decoder([decoder_inputs, encoder_outputs])\n",
+    "\n",
+    "transformer = keras.Model(\n",
+    "    [encoder_inputs, decoder_inputs], decoder_outputs, name = \"transformer\"\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"transformer\"\n",
+      "__________________________________________________________________________________________________\n",
+      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
+      "==================================================================================================\n",
+      " encoder_inputs (InputLayer)    [(None, None)]       0           []                               \n",
+      "                                                                                                  \n",
+      " positional_embedding (Position  (None, None, 256)   3845120     ['encoder_inputs[0][0]']         \n",
+      " alEmbedding)                                                                                     \n",
+      "                                                                                                  \n",
+      " decoder_inputs (InputLayer)    [(None, None)]       0           []                               \n",
+      "                                                                                                  \n",
+      " transformer_encoder (Transform  (None, None, 256)   3155456     ['positional_embedding[0][0]']   \n",
+      " erEncoder)                                                                                       \n",
+      "                                                                                                  \n",
+      " decoder (Functional)           (None, None, 15000)  12959640    ['decoder_inputs[0][0]',         \n",
+      "                                                                  'transformer_encoder[0][0]']    \n",
+      "                                                                                                  \n",
+      "==================================================================================================\n",
+      "Total params: 19,960,216\n",
+      "Trainable params: 19,960,216\n",
+      "Non-trainable params: 0\n",
+      "__________________________________________________________________________________________________\n",
+      "Epoch 1/20\n",
+      "1302/1302 [==============================] - 186s 138ms/step - loss: 1.9811 - accuracy: 0.7206 - val_loss: 1.6848 - val_accuracy: 0.7416\n",
+      "Epoch 2/20\n",
+      "1302/1302 [==============================] - 179s 137ms/step - loss: 1.7133 - accuracy: 0.7508 - val_loss: 1.5721 - val_accuracy: 0.7638\n",
+      "Epoch 3/20\n",
+      "1302/1302 [==============================] - 178s 136ms/step - loss: 1.5370 - accuracy: 0.7759 - val_loss: 1.3673 - val_accuracy: 0.7908\n",
+      "Epoch 4/20\n",
+      "1302/1302 [==============================] - 178s 137ms/step - loss: 1.3938 - accuracy: 0.7957 - val_loss: 1.2725 - val_accuracy: 0.8077\n",
+      "Epoch 5/20\n",
+      "1302/1302 [==============================] - 177s 136ms/step - loss: 1.2863 - accuracy: 0.8128 - val_loss: 1.2105 - val_accuracy: 0.8216\n",
+      "Epoch 6/20\n",
+      "1302/1302 [==============================] - 178s 137ms/step - loss: 1.2217 - accuracy: 0.8268 - val_loss: 1.1603 - val_accuracy: 0.8325\n",
+      "Epoch 7/20\n",
+      "1302/1302 [==============================] - 178s 137ms/step - loss: 1.1745 - accuracy: 0.8369 - val_loss: 1.1168 - val_accuracy: 0.8407\n",
+      "Epoch 8/20\n",
+      "1302/1302 [==============================] - 178s 136ms/step - loss: 1.1379 - accuracy: 0.8448 - val_loss: 1.1005 - val_accuracy: 0.8429\n",
+      "Epoch 9/20\n",
+      "1302/1302 [==============================] - 177s 136ms/step - loss: 1.1092 - accuracy: 0.8506 - val_loss: 1.0816 - val_accuracy: 0.8485\n",
+      "Epoch 10/20\n",
+      "1302/1302 [==============================] - 178s 136ms/step - loss: 1.0849 - accuracy: 0.8552 - val_loss: 1.0628 - val_accuracy: 0.8515\n",
+      "Epoch 11/20\n",
+      "1302/1302 [==============================] - 177s 136ms/step - loss: 1.0652 - accuracy: 0.8590 - val_loss: 1.0541 - val_accuracy: 0.8510\n",
+      "Epoch 12/20\n",
+      "1302/1302 [==============================] - 178s 136ms/step - loss: 1.0469 - accuracy: 0.8622 - val_loss: 1.0370 - val_accuracy: 0.8556\n",
+      "Epoch 13/20\n",
+      "1302/1302 [==============================] - 178s 136ms/step - loss: 1.0317 - accuracy: 0.8649 - val_loss: 1.0275 - val_accuracy: 0.8577\n",
+      "Epoch 14/20\n",
+      "1302/1302 [==============================] - 178s 136ms/step - loss: 1.0163 - accuracy: 0.8674 - val_loss: 1.0242 - val_accuracy: 0.8584\n",
+      "Epoch 15/20\n",
+      "1302/1302 [==============================] - 178s 136ms/step - loss: 1.0016 - accuracy: 0.8696 - val_loss: 1.0176 - val_accuracy: 0.8602\n",
+      "Epoch 16/20\n",
+      "1302/1302 [==============================] - 178s 136ms/step - loss: 0.9884 - accuracy: 0.8717 - val_loss: 1.0102 - val_accuracy: 0.8606\n",
+      "Epoch 17/20\n",
+      "1302/1302 [==============================] - 178s 136ms/step - loss: 0.9767 - accuracy: 0.8736 - val_loss: 1.0234 - val_accuracy: 0.8591\n",
+      "Epoch 18/20\n",
+      "1302/1302 [==============================] - 178s 136ms/step - loss: 0.9665 - accuracy: 0.8752 - val_loss: 0.9950 - val_accuracy: 0.8634\n",
+      "Epoch 19/20\n",
+      "1302/1302 [==============================] - 178s 136ms/step - loss: 0.9549 - accuracy: 0.8771 - val_loss: 1.0000 - val_accuracy: 0.8621\n",
+      "Epoch 20/20\n",
+      "1302/1302 [==============================] - 178s 136ms/step - loss: 0.9456 - accuracy: 0.8783 - val_loss: 1.0106 - val_accuracy: 0.8617\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x214f97284c0>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "epochs = 20\n",
+    "\n",
+    "transformer.summary()\n",
+    "\n",
+    "transformer.compile(\n",
+    "    \"rmsprop\", loss = \"sparse_categorical_crossentropy\", metrics = [\"accuracy\"]\n",
+    ")\n",
+    "\n",
+    "transformer.fit(train_ds, epochs = epochs, validation_data = val_ds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:Found untraced functions such as embedding_layer_call_fn, embedding_layer_call_and_return_conditional_losses, embedding_1_layer_call_fn, embedding_1_layer_call_and_return_conditional_losses, multi_head_attention_layer_call_fn while saving (showing 5 of 60). These functions will not be directly callable after loading.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Assets written to: transformer_model\\assets\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Assets written to: transformer_model\\assets\n"
+     ]
+    }
+   ],
+   "source": [
+    "transformer.save(\"transformer_model\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "input: her days are numbered\n",
+      "translated: [start] sus días están en el [UNK] [end]\n",
+      "\n",
+      "input: i heard the door close\n",
+      "translated: [start] he oído la puerta [end]\n",
+      "\n",
+      "input: you go first\n",
+      "translated: [start] vas al primero [end]\n",
+      "\n",
+      "input: you used to look up to your father\n",
+      "translated: [start] te [UNK] a tu padre [end]\n",
+      "\n",
+      "input: i am acquainted with the chairman of the committee\n",
+      "translated: [start] estoy en el [UNK] de la [UNK] [end]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "spa_vocab = spa_vectorization.get_vocabulary()\n",
+    "spa_index_lookup = dict(zip(range(len(spa_vocab)), spa_vocab))\n",
+    "max_decoded_sentence_length = sequence_length\n",
+    "\n",
+    "def decode_sentence(input_sentence):\n",
+    "    tokenized_input_sentence = eng_vectorization([input_sentence])\n",
+    "    decoded_sentence = \"[start]\"\n",
+    "    for i in range(max_decoded_sentence_length):\n",
+    "        tokenized_target_sentence = spa_vectorization([decoded_sentence])[:, :-1]\n",
+    "        predictions = transformer([tokenized_input_sentence, tokenized_target_sentence])\n",
+    "        sampled_token_index = tf.argmax(predictions[0, i, :]).numpy().item(0)\n",
+    "        sampled_token = spa_index_lookup[sampled_token_index]\n",
+    "        decoded_sentence += \" \" + sampled_token\n",
+    "        if sampled_token == \"[end]\":\n",
+    "            break\n",
+    "    return decoded_sentence\n",
+    "\n",
+    "test_eng_texts = [pair[0] for pair in test_pairs]\n",
+    "for _ in range(5):\n",
+    "    input_sentence = random.choice(test_eng_texts)\n",
+    "    input_sentence = input_sentence.lower()\n",
+    "    input_sentence = input_sentence.translate(str.maketrans('', '', strip_chars))\n",
+    "    translated = decode_sentence(input_sentence)\n",
+    "    print(f\"input: {input_sentence}\")\n",
+    "    print(f\"translated: {translated}\")\n",
+    "    print()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.9.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

Task 2/LICENSE

@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
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Task 2/README.md

@@ -0,0 +1 @@
+# Machine_Translation

Task 2/eng_vectorization_config.json

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+{"name": "text_vectorization", "trainable": true, "batch_input_shape": [null], "dtype": "string", "max_tokens": 15000, "split": "whitespace", "ngrams": null, "output_mode": "int", "output_sequence_length": 20, "pad_to_max_tokens": false, "sparse": false, "ragged": false, "vocabulary": null, "idf_weights": null}

Task 2/english_to_french_model/keras_metadata.pb

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+oid sha256:18727f19a8f2e298e734019630e718acf71e07ef341ce0398a88208498c47dfd
+size 21276

Task 2/english_to_french_model/saved_model.pb

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5ecd4484fbca10b3a8698ba8bfac97dd428bcd51198b27e3df139be775b3fcb3
+size 2021585

Task 2/english_to_french_model/variables/variables.data-00000-of-00001

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+size 20633894

Task 2/english_tokenizer.json

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Task 2/french_tokenizer.json

@@ -0,0 +1 @@
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\\\"chiens\\\", \\\"259\\\": \\\"l'\\\\u00e9l\\\\u00e9phant\\\", \\\"260\\\": \\\"l'oiseau\\\", \\\"261\\\": \\\"requins\\\", \\\"262\\\": \\\"l'ours\\\", \\\"263\\\": \\\"serpents\\\", \\\"264\\\": \\\"chats\\\", \\\"265\\\": \\\"lapins\\\", \\\"266\\\": \\\"singes\\\", \\\"267\\\": \\\"oiseaux\\\", \\\"268\\\": \\\"lions\\\", \\\"269\\\": \\\"l\\\\u00e9g\\\\u00e8re\\\", \\\"270\\\": \\\"c\\\\u00e9page\\\", \\\"271\\\": \\\"pensez\\\", \\\"272\\\": \\\"tour\\\", \\\"273\\\": \\\"eiffel\\\", \\\"274\\\": \\\"l'\\\\u00e9picerie\\\", \\\"275\\\": \\\"terrain\\\", \\\"276\\\": \\\"football\\\", \\\"277\\\": \\\"lac\\\", \\\"278\\\": \\\"l'\\\\u00e9cole\\\", \\\"279\\\": \\\"l'animal\\\", \\\"280\\\": \\\"n'est\\\", \\\"281\\\": \\\"allons\\\", \\\"282\\\": \\\"allez\\\", \\\"283\\\": \\\"peu\\\", \\\"284\\\": \\\"pousse\\\", \\\"285\\\": \\\"du\\\", \\\"286\\\": \\\"temps\\\", \\\"287\\\": \\\"at\\\", \\\"288\\\": \\\"rouille\\\", \\\"289\\\": \\\"sur\\\", \\\"290\\\": \\\"qu'elle\\\", 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152, \\\"noire\\\": 153, \\\"brillant\\\": 154, \\\"blanche\\\": 155, \\\"redout\\\\u00e9\\\": 156, \\\"pleut\\\": 157, \\\"n'aimait\\\": 158, \\\"pamplemousses\\\": 159, \\\"pense\\\": 160, \\\"entre\\\": 161, \\\"bleue\\\": 162, \\\"nouveau\\\": 163, \\\"traduire\\\": 164, \\\"rouill\\\\u00e9e\\\": 165, \\\"bleu\\\": 166, \\\"se\\\": 167, \\\"grande\\\": 168, \\\"rouill\\\\u00e9\\\": 169, \\\"ses\\\": 170, \\\"qu'il\\\": 171, \\\"blanc\\\": 172, \\\"aux\\\": 173, \\\"brillante\\\": 174, \\\"pr\\\\u00e9f\\\\u00e9r\\\\u00e9s\\\": 175, \\\"noir\\\": 176, \\\"pluies\\\": 177, \\\"envisage\\\": 178, \\\"\\\\u00e9taient\\\": 179, \\\"va\\\": 180, \\\"rendre\\\": 181, \\\"vert\\\": 182, \\\"vieux\\\": 183, \\\"petit\\\": 184, \\\"espagnol\\\": 185, \\\"portugais\\\": 186, \\\"chinois\\\": 187, \\\"anglais\\\": 188, \\\"fran\\\\u00e7ais\\\": 189, \\\"glaciales\\\": 190, \\\"mes\\\": 191, \\\"cet\\\": 192, \\\"automobile\\\": 193, \\\"traduction\\\": 194, \\\"mouill\\\\u00e9\\\": 195, 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Task 2/sequence_length.json

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+21

Task 2/spa_vectorization_config.json

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+{"name": "text_vectorization_1", "trainable": true, "batch_input_shape": [null], "dtype": "string", "max_tokens": 15000, "split": "whitespace", "ngrams": null, "output_mode": "int", "output_sequence_length": 21, "pad_to_max_tokens": false, "sparse": false, "ragged": false, "vocabulary": null, "idf_weights": null}

Task 2/transformer_model/keras_metadata.pb

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+oid sha256:c57e5b1c49a163881bb486d7ab440406af67981fb4d43c1b293d4f3e8be60b6b
+size 58739

Task 2/transformer_model/saved_model.pb

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+version https://git-lfs.github.com/spec/v1
+oid sha256:08e90ad65260d2a5f6b86eb2222070bfff801829a65b959df096fdf5609f4979
+size 1305169

Task 2/transformer_model/variables/variables.data-00000-of-00001

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+version https://git-lfs.github.com/spec/v1
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+size 159724466

Task 2/word detection.py

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+# Import libraries
+import tkinter as tk
+from tkinter import ttk, messagebox
+
+from keras.layers import TextVectorization
+import re
+import tensorflow.strings as tf_strings
+import json
+import string
+from keras.models import load_model
+import tensorflow as tf
+from keras.preprocessing.text import tokenizer_from_json
+from keras.utils import pad_sequences
+import numpy as np
+import difflib
+
+# English to Spanish translation
+strip_chars = string.punctuation + "¿"
+strip_chars = strip_chars.replace("[", "")
+strip_chars = strip_chars.replace("]", "")
+
+def custom_standardization(input_string):
+    lowercase = tf_strings.lower(input_string)
+    return tf_strings.regex_replace(lowercase, f"[{re.escape(strip_chars)}]", "")
+
+# Load the English vectorization layer configuration
+with open('eng_vectorization_config.json') as json_file:
+    eng_vectorization_config = json.load(json_file)
+
+# Recreate the English vectorization layer with basic configuration
+eng_vectorization = TextVectorization(
+    max_tokens=eng_vectorization_config['max_tokens'],
+    output_mode=eng_vectorization_config['output_mode'],
+    output_sequence_length=eng_vectorization_config['output_sequence_length']
+)
+
+# Apply the custom standardization function
+eng_vectorization.standardize = custom_standardization
+
+# Load the Spanish vectorization layer configuration
+with open('spa_vectorization_config.json') as json_file:
+    spa_vectorization_config = json.load(json_file)
+
+# Recreate the Spanish vectorization layer with basic configuration
+spa_vectorization = TextVectorization(
+    max_tokens=spa_vectorization_config['max_tokens'],
+    output_mode=spa_vectorization_config['output_mode'],
+    output_sequence_length=spa_vectorization_config['output_sequence_length'],
+    standardize=custom_standardization
+)
+
+# Load and set the English vocabulary
+with open('eng_vocab.json') as json_file:
+    eng_vocab = json.load(json_file)
+    eng_vectorization.set_vocabulary(eng_vocab)
+
+# Load and set the Spanish vocabulary
+with open('spa_vocab.json') as json_file:
+    spa_vocab = json.load(json_file)
+    spa_vectorization.set_vocabulary(spa_vocab)
+
+# Load the Spanish model
+transformer = load_model('transformer_model')
+
+spa_index_lookup = dict(zip(range(len(spa_vocab)), spa_vocab))
+max_decoded_sentence_length = 20
+
+# Initialize list to track incorrect words
+incorrect_words = []
+
+def beam_search_decode(input_sentence, beam_width=3):
+    tokenized_input_sentence = eng_vectorization([input_sentence])
+    decoded_sentences = [("[start]", 0.0)]
+    
+    for i in range(max_decoded_sentence_length):
+        all_candidates = []
+        for decoded_sentence, score in decoded_sentences:
+            tokenized_target_sentence = spa_vectorization([decoded_sentence])[:, :-1]
+            predictions = transformer([tokenized_input_sentence, tokenized_target_sentence])
+            top_k = tf.math.top_k(predictions[0, i, :], k=beam_width)
+            
+            for j in range(beam_width):
+                predicted_token_index = top_k.indices[j].numpy()
+                predicted_token = spa_index_lookup[predicted_token_index]
+                candidate = (decoded_sentence + " " + predicted_token, score + top_k.values[j].numpy())
+                all_candidates.append(candidate)
+        
+        ordered = sorted(all_candidates, key=lambda x: x[1], reverse=True)
+        decoded_sentences = ordered[:beam_width]
+        
+        if all(sentence[0].endswith("[end]") for sentence in decoded_sentences):
+            break
+    
+    return decoded_sentences[0][0]
+
+# English to French translation
+# Load French model
+model = load_model('english_to_french_model')
+
+# Load Tokenizer
+with open('english_tokenizer.json') as f:
+    data = json.load(f)
+    english_tokenizer = tokenizer_from_json(data)
+    
+with open('french_tokenizer.json') as f:
+    data = json.load(f)
+    french_tokenizer = tokenizer_from_json(data)
+    
+# Load max length
+with open('sequence_length.json') as f:
+    max_length = json.load(f)
+    
+def pad(x, length=None):
+    return pad_sequences(x, maxlen=length, padding='post')
+
+def translate_to_french(english_sentence):
+    english_sentence = english_sentence.lower()
+    english_sentence = re.sub(r'[.?!,]', '', english_sentence)
+    english_sentence = english_tokenizer.texts_to_sequences([english_sentence])
+    english_sentence = pad(english_sentence, max_length)
+    english_sentence = english_sentence.reshape((-1, max_length))
+    
+    french_sentence = model.predict(english_sentence)[0]
+    french_sentence = [np.argmax(word) for word in french_sentence]
+    french_sentence = french_tokenizer.sequences_to_texts([french_sentence])[0]
+    
+    return french_sentence
+
+def get_word_suggestions(word, vocab):
+    return difflib.get_close_matches(word, vocab, n=3, cutoff=0.6)
+
+def check_and_correct_sentence(sentence, vocab):
+    words = sentence.split()
+    incorrect_words.clear()
+    corrected_sentence = []
+    for word in words:
+        if word not in vocab:
+            suggestions = get_word_suggestions(word, vocab)
+            incorrect_words.append((word, suggestions))
+        else:
+            corrected_sentence.append(word)
+    
+    if incorrect_words:
+        message = f"Incorrect word(s) detected: {', '.join([w[0] for w in incorrect_words])}\n"
+        for word, suggestions in incorrect_words:
+            message += f"Suggestions for '{word}': {', '.join(suggestions) if suggestions else 'No suggestions available'}\n"
+        if len(incorrect_words) >= 2:
+            messagebox.showerror("Error", message)
+        return False
+    return True
+
+def translate_to_spanish(english_sentence):
+    if not check_and_correct_sentence(english_sentence, eng_vocab):
+        return ""
+    spanish_sentence = beam_search_decode(english_sentence)
+    return spanish_sentence.replace("[start]", "").replace("[end]", "").strip()
+
+# Function to handle translation request based on selected language
+def handle_translate():
+    selected_language = language_var.get()
+    english_sentence = text_input.get("1.0", "end-1c").strip()
+    
+    if not english_sentence:
+        messagebox.showwarning("Warning", "Please enter a sentence to translate.")
+        return
+    
+    if selected_language == "French":
+        translation = translate_to_french(english_sentence)
+    elif selected_language == "Spanish":
+        translation = translate_to_spanish(english_sentence)
+        
+    translation_output.delete("1.0", "end")
+    translation_output.insert("end", f"{selected_language} translation: {translation}")
+
+# Setting up the main window
+root = tk.Tk()
+root.title("Language Translator")
+root.geometry("550x600")
+
+# Font configuration
+font_style = "Times New Roman"
+font_size = 14
+
+# Frame for input
+input_frame = tk.Frame(root)
+input_frame.pack(pady=10)
+
+# Heading for input
+input_heading = tk.Label(input_frame, text="Enter the text to be translated", font=(font_style, font_size, 'bold'))
+input_heading.pack()
+# Text input for English sentence
+text_input = tk.Text(input_frame, height=5, width=50, font=(font_style, font_size))
+text_input.pack()
+
+# Language selection
+language_var = tk.StringVar()
+language_label = tk.Label(root, text="Select the language to translate to", font=(font_style, font_size, 'bold'))
+language_label.pack()
+language_select = ttk.Combobox(root, textvariable=language_var, values=["French", "Spanish"], font=(font_style, font_size), state="readonly")
+language_select.pack()
+
+# Submit button
+submit_button = ttk.Button(root, text="Translate", command=handle_translate)
+submit_button.pack(pady=10)
+
+# Frame for output
+output_frame = tk.Frame(root)
+output_frame.pack(pady=10)
+# Heading for output
+output_heading = tk.Label(output_frame, text="Translation: ", font=(font_style, font_size, 'bold'))
+output_heading.pack()
+
+# Text output for translations
+translation_output = tk.Text(output_frame, height=10, width=50, font=(font_style, font_size))
+translation_output.pack()
+
+# Running the application
+root.mainloop()