Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Building Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-12-10 16:22:01.252329: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Embedding, LSTM, Dense\n",
    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#upload dataset\n",
    "csv_path = \"/Users/vivianwang/Desktop/data2.csv\"\n",
    "df = pd.read_csv(csv_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "88/88 [==============================] - 524s 6s/step - loss: 3.3090 - accuracy: 0.7831 - val_loss: 3.0875 - val_accuracy: 0.8806\n",
      "Epoch 2/10\n",
      "88/88 [==============================] - 532s 6s/step - loss: 2.8742 - accuracy: 0.8828 - val_loss: 2.6853 - val_accuracy: 0.8806\n",
      "Epoch 3/10\n",
      "88/88 [==============================] - 637s 7s/step - loss: 2.5288 - accuracy: 0.8828 - val_loss: 2.3798 - val_accuracy: 0.8806\n",
      "Epoch 4/10\n",
      "88/88 [==============================] - 554s 6s/step - loss: 2.2351 - accuracy: 0.8842 - val_loss: 2.0949 - val_accuracy: 0.8895\n",
      "Epoch 5/10\n",
      "88/88 [==============================] - 472s 5s/step - loss: 2.4890 - accuracy: 0.6914 - val_loss: 2.5452 - val_accuracy: 0.8806\n",
      "Epoch 6/10\n",
      "88/88 [==============================] - 471s 5s/step - loss: 2.3329 - accuracy: 0.8828 - val_loss: 2.1432 - val_accuracy: 0.8806\n",
      "Epoch 7/10\n",
      "88/88 [==============================] - 523s 6s/step - loss: 1.9848 - accuracy: 0.8828 - val_loss: 1.8407 - val_accuracy: 0.8806\n",
      "Epoch 8/10\n",
      "88/88 [==============================] - 496s 6s/step - loss: 1.7129 - accuracy: 0.8828 - val_loss: 1.5991 - val_accuracy: 0.8806\n",
      "Epoch 9/10\n",
      "88/88 [==============================] - 461s 5s/step - loss: 1.4953 - accuracy: 0.8828 - val_loss: 1.4062 - val_accuracy: 0.8806\n",
      "Epoch 10/10\n",
      "88/88 [==============================] - 466s 5s/step - loss: 1.3285 - accuracy: 0.8828 - val_loss: 1.2562 - val_accuracy: 0.8806\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "def reverse_sequences(sequences):\n",
    "   reversed_sequences = [sequence[::-1] for sequence in sequences]\n",
    "   return reversed_sequences\n",
    "\n",
    "df2 = df.iloc[:7000]\n",
    "\n",
    "input_sequences = df2['input'].apply(lambda x: ' '.join(x)).values\n",
    "output_sequences = df2['output'].apply(lambda x: ' '.join(x)).values\n",
    "\n",
    "reversed_output_sequences = reverse_sequences(output_sequences)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Embedding, LSTM, Dense, TimeDistributed\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "tokenizer = Tokenizer()\n",
    "tokenizer.fit_on_texts(input_sequences)\n",
    "tokenizer.fit_on_texts(output_sequences)\n",
    "\n",
    "\n",
    "input_sequences = tokenizer.texts_to_sequences(input_sequences)\n",
    "output_sequences = tokenizer.texts_to_sequences(output_sequences)\n",
    "\n",
    "\n",
    "max_len_input = max(len(seq) for seq in input_sequences)\n",
    "max_len_output = max(len(seq) for seq in output_sequences)\n",
    "\n",
    "\n",
    "# padded_input = pad_sequences(input_sequences, maxlen=max_len_input, padding='post')\n",
    "# padded_output = pad_sequences(output_sequences, maxlen=max_len_output, padding='post')\n",
    "max_len = max(max_len_input, max_len_output)\n",
    "\n",
    "\n",
    "padded_input = pad_sequences(input_sequences, maxlen=max_len, padding='post')\n",
    "padded_output = pad_sequences(output_sequences, maxlen=max_len, padding='post')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Split the data into training and validation sets\n",
    "X_train, X_val, y_train, y_val = train_test_split(padded_input, padded_output, test_size=0.2, random_state=42)\n",
    "\n",
    "\n",
    "# Build the model\n",
    "embedding_dim = 100  \n",
    "vocab_size = len(tokenizer.word_index) + 1\n",
    "\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Embedding(input_dim=vocab_size, output_dim=embedding_dim, input_length=max_len_input))\n",
    "model.add(LSTM(100, return_sequences=True))  # Return sequences for TimeDistributed layer\n",
    "model.add(TimeDistributed(Dense(vocab_size, activation='softmax')))\n",
    "model.add(TimeDistributed(Dense(vocab_size, activation='softmax')))\n",
    "\n",
    "\n",
    "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "\n",
    "# Train the model\n",
    "# model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=10, batch_size=64)\n",
    "history = model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=10, batch_size=64)\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding_1 (Embedding)     (None, 3560, 100)         3800      \n",
      "                                                                 \n",
      " lstm_1 (LSTM)               (None, 3560, 100)         80400     \n",
      "                                                                 \n",
      " time_distributed_2 (TimeDi  (None, 3560, 38)          3838      \n",
      " stributed)                                                      \n",
      "                                                                 \n",
      " time_distributed_3 (TimeDi  (None, 3560, 38)          1482      \n",
      " stributed)                                                      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 89520 (349.69 KB)\n",
      "Trainable params: 89520 (349.69 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44/44 [==============================] - 41s 916ms/step - loss: 1.2562 - accuracy: 0.8806\n",
      "Test accuracy: 0.8805778622627258\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model on the test set\n",
    "test_loss, test_acc = model.evaluate(X_val, y_val)\n",
    "print(f'Test accuracy: {test_acc}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extracting training and validation accuracy and loss from the history object\n",
    "training_accuracy = history.history['accuracy']\n",
    "validation_accuracy = history.history['val_accuracy']\n",
    "training_loss = history.history['loss']\n",
    "validation_loss = history.history['val_loss']\n",
    "\n",
    "# Plotting the accuracy\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(training_accuracy, label='Training Accuracy')\n",
    "plt.plot(validation_accuracy, label='Validation Accuracy')\n",
    "plt.title('Accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(['Train', 'Validation'], loc='lower right')\n",
    "\n",
    "# Plotting the loss\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(training_loss, label='Training Loss')\n",
    "plt.plot(validation_loss, label='Validation Loss')\n",
    "plt.title('Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}