Street Fighter Move Recognizer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a38a8be9-9f57-4d4e-b101-704e636db4fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install tensorflow scikit-learn pandas\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0b044499-8bd6-4e40-84b9-2d7f22c180b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = {\n",
    "    \"sequence\": [\n",
    "        \"DOWN,RIGHT,PUNCH\",\n",
    "        \"RIGHT,DOWN,RIGHT,KICK\",\n",
    "        \"LEFT,LEFT,PUNCH\",\n",
    "        \"DOWN,KICK\",\n",
    "        \"UP,PUNCH\",\n",
    "        \"RIGHT,RIGHT,KICK\",\n",
    "        \"DOWN,DOWN,RIGHT,PUNCH\",\n",
    "        \"LEFT,DOWN,RIGHT,PUNCH\"\n",
    "    ],\n",
    "    \"move\": [\n",
    "        \"Hadouken\",\n",
    "        \"Shoryuken\",\n",
    "        \"Dash Punch\",\n",
    "        \"Low Kick\",\n",
    "        \"Jump Punch\",\n",
    "        \"Double Kick\",\n",
    "        \"Super Hadouken\",\n",
    "        \"Combo Strike\"\n",
    "    ]\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "606f2fd1-42b8-49f0-87b7-cc2f5c450662",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tokenizer ve Label Encoding\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
    "\n",
    "# Joystick hareketlerini tokenize et\n",
    "tokenizer = Tokenizer(filters='', lower=False, split=',')\n",
    "tokenizer.fit_on_texts(df['sequence'])\n",
    "\n",
    "X_seq = tokenizer.texts_to_sequences(df['sequence'])\n",
    "X_pad = pad_sequences(X_seq, padding='post')  # sekansları eşitle\n",
    "\n",
    "# Etiketleri sayısallaştır\n",
    "le = LabelEncoder()\n",
    "y_encoded = le.fit_transform(df['move'])\n",
    "\n",
    "# Bilgiler\n",
    "vocab_size = len(tokenizer.word_index) + 1\n",
    "num_classes = len(le.classes_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e654d9b3-faea-4888-a4a3-e3cc6b762940",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\core\\embedding.py:97: UserWarning: Argument `input_length` is deprecated. Just remove it.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step - accuracy: 0.2500 - loss: 2.0767\n",
      "Epoch 2/100\n",
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      "Epoch 3/100\n",
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      "Epoch 4/100\n",
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      "Epoch 5/100\n",
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      "Epoch 6/100\n",
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      "Epoch 7/100\n",
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      "Epoch 8/100\n",
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      "Epoch 9/100\n",
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      "Epoch 10/100\n",
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      "Epoch 11/100\n",
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      "Epoch 12/100\n",
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      "Epoch 13/100\n",
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      "Epoch 14/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.6250 - loss: 2.0626\n",
      "Epoch 15/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.6250 - loss: 2.0612\n",
      "Epoch 16/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.6250 - loss: 2.0597\n",
      "Epoch 17/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.6250 - loss: 2.0581\n",
      "Epoch 18/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.6250 - loss: 2.0564\n",
      "Epoch 19/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.6250 - loss: 2.0546\n",
      "Epoch 20/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.6250 - loss: 2.0527\n",
      "Epoch 21/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - accuracy: 0.6250 - loss: 2.0508\n",
      "Epoch 22/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.6250 - loss: 2.0487\n",
      "Epoch 23/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.6250 - loss: 2.0464\n",
      "Epoch 24/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.6250 - loss: 2.0441\n",
      "Epoch 25/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.6250 - loss: 2.0415\n",
      "Epoch 26/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - accuracy: 0.6250 - loss: 2.0389\n",
      "Epoch 27/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.6250 - loss: 2.0361\n",
      "Epoch 28/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.6250 - loss: 2.0331\n",
      "Epoch 29/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.6250 - loss: 2.0299\n",
      "Epoch 30/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - accuracy: 0.6250 - loss: 2.0265\n",
      "Epoch 31/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - accuracy: 0.6250 - loss: 2.0229\n",
      "Epoch 32/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - accuracy: 0.6250 - loss: 2.0191\n",
      "Epoch 33/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - accuracy: 0.6250 - loss: 2.0151\n",
      "Epoch 34/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - accuracy: 0.6250 - loss: 2.0108\n",
      "Epoch 35/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.6250 - loss: 2.0062\n",
      "Epoch 36/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.6250 - loss: 2.0013\n",
      "Epoch 37/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.6250 - loss: 1.9961\n",
      "Epoch 38/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.6250 - loss: 1.9906\n",
      "Epoch 39/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - accuracy: 0.6250 - loss: 1.9848\n",
      "Epoch 40/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - accuracy: 0.7500 - loss: 1.9785\n",
      "Epoch 41/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.8750 - loss: 1.9719\n",
      "Epoch 42/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - accuracy: 0.8750 - loss: 1.9649\n",
      "Epoch 43/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 0.8750 - loss: 1.9574\n",
      "Epoch 44/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - accuracy: 0.8750 - loss: 1.9494\n",
      "Epoch 45/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.8750 - loss: 1.9410\n",
      "Epoch 46/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 0.8750 - loss: 1.9320\n",
      "Epoch 47/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - accuracy: 0.8750 - loss: 1.9225\n",
      "Epoch 48/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - accuracy: 0.8750 - loss: 1.9123\n",
      "Epoch 49/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - accuracy: 0.8750 - loss: 1.9016\n",
      "Epoch 50/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - accuracy: 0.8750 - loss: 1.8902\n",
      "Epoch 51/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - accuracy: 0.8750 - loss: 1.8781\n",
      "Epoch 52/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - accuracy: 0.8750 - loss: 1.8653\n",
      "Epoch 53/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.8750 - loss: 1.8517\n",
      "Epoch 54/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.8750 - loss: 1.8374\n",
      "Epoch 55/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - accuracy: 0.8750 - loss: 1.8223\n",
      "Epoch 56/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.7500 - loss: 1.8063\n",
      "Epoch 57/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.7500 - loss: 1.7895\n",
      "Epoch 58/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.7500 - loss: 1.7718\n",
      "Epoch 59/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.7500 - loss: 1.7532\n",
      "Epoch 60/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.7500 - loss: 1.7338\n",
      "Epoch 61/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.7500 - loss: 1.7134\n",
      "Epoch 62/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.7500 - loss: 1.6922\n",
      "Epoch 63/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.7500 - loss: 1.6701\n",
      "Epoch 64/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.7500 - loss: 1.6471\n",
      "Epoch 65/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.7500 - loss: 1.6233\n",
      "Epoch 66/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - accuracy: 0.7500 - loss: 1.5988\n",
      "Epoch 67/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - accuracy: 0.7500 - loss: 1.5735\n",
      "Epoch 68/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - accuracy: 0.7500 - loss: 1.5476\n",
      "Epoch 69/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.7500 - loss: 1.5211\n",
      "Epoch 70/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.7500 - loss: 1.4941\n",
      "Epoch 71/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.7500 - loss: 1.4667\n",
      "Epoch 72/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.7500 - loss: 1.4390\n",
      "Epoch 73/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.7500 - loss: 1.4110\n",
      "Epoch 74/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.7500 - loss: 1.3829\n",
      "Epoch 75/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.7500 - loss: 1.3547\n",
      "Epoch 76/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.7500 - loss: 1.3266\n",
      "Epoch 77/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 0.7500 - loss: 1.2986\n",
      "Epoch 78/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.7500 - loss: 1.2707\n",
      "Epoch 79/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - accuracy: 0.7500 - loss: 1.2430\n",
      "Epoch 80/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.7500 - loss: 1.2157\n",
      "Epoch 81/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.8750 - loss: 1.1886\n",
      "Epoch 82/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 1.0000 - loss: 1.1618\n",
      "Epoch 83/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 1.0000 - loss: 1.1354\n",
      "Epoch 84/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 1.0000 - loss: 1.1093\n",
      "Epoch 85/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 1.0000 - loss: 1.0835\n",
      "Epoch 86/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 1.0000 - loss: 1.0581\n",
      "Epoch 87/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - accuracy: 1.0000 - loss: 1.0330\n",
      "Epoch 88/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - accuracy: 1.0000 - loss: 1.0082\n",
      "Epoch 89/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 1.0000 - loss: 0.9838\n",
      "Epoch 90/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 1.0000 - loss: 0.9596\n",
      "Epoch 91/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 1.0000 - loss: 0.9357\n",
      "Epoch 92/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 1.0000 - loss: 0.9121\n",
      "Epoch 93/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - accuracy: 1.0000 - loss: 0.8888\n",
      "Epoch 94/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 1.0000 - loss: 0.8656\n",
      "Epoch 95/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 1.0000 - loss: 0.8427\n",
      "Epoch 96/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 1.0000 - loss: 0.8199\n",
      "Epoch 97/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 1.0000 - loss: 0.7974\n",
      "Epoch 98/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 1.0000 - loss: 0.7750\n",
      "Epoch 99/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 1.0000 - loss: 0.7527\n",
      "Epoch 100/100\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 1.0000 - loss: 0.7307\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x230194c7410>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # LSTM Modeli Oluştur ve Eğit\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Embedding, LSTM, Dense\n",
    "\n",
    "model = Sequential([\n",
    "    Embedding(input_dim=vocab_size, output_dim=16, input_length=X_pad.shape[1]),\n",
    "    LSTM(32),\n",
    "    Dense(num_classes, activation='softmax')\n",
    "])\n",
    "\n",
    "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
    "model.fit(X_pad, y_encoded, epochs=100, verbose=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f5d9bb8c-c4da-45f9-93d6-23f083368c05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 165ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Hadouken'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tahmin Fonksiyonu\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "def predict_move(sequence_text):\n",
    "    seq = tokenizer.texts_to_sequences([sequence_text])\n",
    "    pad = pad_sequences(seq, maxlen=X_pad.shape[1], padding='post')\n",
    "    pred = model.predict(pad)\n",
    "    label = le.inverse_transform([np.argmax(pred)])\n",
    "    return label[0]\n",
    "\n",
    "# Örnek:\n",
    "predict_move(\"DOWN,RIGHT,PUNCH\")  # Hadouken\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "89a797c1-fa12-4552-951a-4dcede799be8",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"joystick_move_model.keras\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "847f55ca-1b05-41f8-a0b8-57af26f1fb90",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "# Tokenizer\n",
    "with open(\"tokenizer.pkl\", \"wb\") as f:\n",
    "    pickle.dump(tokenizer, f)\n",
    "\n",
    "# LabelEncoder\n",
    "with open(\"label_encoder.pkl\", \"wb\") as f:\n",
    "    pickle.dump(le, f)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8343757-6301-49f2-894f-30ce3df0b601",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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