3Epochs_CNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6cb26260",
   "metadata": {},
   "source": [
    "## Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf3aa395",
   "metadata": {
    "colab": {
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from collections import defaultdict\n",
    "import re\n",
    "import keras\n",
    "from bs4 import BeautifulSoup\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "import sys\n",
    "import os\n",
    "# Train test validation Split\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "from keras.utils.np_utils import to_categorical\n",
    "from wandb.keras import WandbCallback\n",
    "from keras.layers import Embedding\n",
    "from keras.layers import Dense, Input, Flatten\n",
    "from keras.layers import Conv1D, MaxPooling1D, Embedding, Dropout\n",
    "from keras.models import Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78e97abc",
   "metadata": {},
   "source": [
    "## Set initial parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0dc3fac3",
   "metadata": {
    "colab": {
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     "height": 1000,
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      "6bc46120667b4a4cae58dd961092bdb0"
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   "outputs": [],
   "source": [
    "MAX_SEQUENCE_LENGTH = 1000\n",
    "EMBEDDING_DIM = 100\n",
    "MAX_NB_WORDS = 200000\n",
    "VALIDATION_SPLIT = 0.2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e558c9ff",
   "metadata": {},
   "source": [
    "## Load the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a18493a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
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      "6bc46120667b4a4cae58dd961092bdb0"
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   "outputs": [],
   "source": [
    "    \n",
    "#clean the dataset\n",
    "def clean_str(string):\n",
    "    string = re.sub(r\"\\\\\", \"\", string)    \n",
    "    string = re.sub(r\"\\'\", \"\", string)    \n",
    "    string = re.sub(r\"\\\"\", \"\", string)    \n",
    "    return string.strip().lower()\n",
    "\n",
    "\n",
    "#read the training data\n",
    "train_data = pd.read_csv('train.csv')\n",
    "train_data.text[1]\n",
    "\n",
    "train_data\n",
    "\n",
    "train_data['label'].value_counts()\n",
    "\n",
    "\n",
    "print(train_data.columns)\n",
    "print('The raw input data looks like: ')\n",
    "print(train_data[0:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6476776b",
   "metadata": {},
   "source": [
    "## Vectorize the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c56ba2d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
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   "outputs": [],
   "source": [
    "labels =[]\n",
    "train_data_text =[] \n",
    "\n",
    "for index in range(train_data.text.shape[0]):\n",
    "    text1 = train_data.title[index]\n",
    "    text2 = train_data.text[index]\n",
    "    combined_text = str(text1) +\"\"+ str(text2)\n",
    "    train_data_text.append(combined_text)\n",
    "    labels.append(train_data.label[index])\n",
    "    \n",
    "tokenizer = Tokenizer(num_words=MAX_NB_WORDS)\n",
    "tokenizer.fit_on_texts(train_data_text)\n",
    "sequences = tokenizer.texts_to_sequences(train_data_text)\n",
    "\n",
    "word_index = tokenizer.word_index\n",
    "print('There are %s unique tokens.' % len(word_index))\n",
    "\n",
    "\n",
    "\n",
    "# Pad the input sequences\n",
    "data = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)\n",
    "labels = to_categorical(np.asarray(labels),num_classes = 2)\n",
    "print('Shape of data tensor:', data.shape)\n",
    "print('Shape of label tensor:', labels.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26deae01",
   "metadata": {},
   "source": [
    "## Split the data and apply embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1df6c2fb",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
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   "outputs": [],
   "source": [
    "indices = np.arange(data.shape[0])\n",
    "np.random.shuffle(indices)\n",
    "data = data[indices]\n",
    "labels = labels[indices]\n",
    "x_train, x_test, y_train, y_test = train_test_split( data, labels, test_size=0.20, random_state=42)\n",
    "x_test, x_val, y_test, y_val = train_test_split( data, labels, test_size=0.50, random_state=42)\n",
    "print('Size of train, validation, test:', len(y_train), len(y_val), len(y_test))\n",
    "\n",
    "print('Real and fake news in train,validation and test:')\n",
    "print(y_train.sum(axis=0))\n",
    "print(y_val.sum(axis=0))\n",
    "print(y_test.sum(axis=0))\n",
    "\n",
    "#Used Pre-trained word Glove embeddings\n",
    "GLOVE_DIR = \"data\" \n",
    "embeddings_index = {}\n",
    "f = open('glove.6B.100d.txt', encoding=\"utf8\")\n",
    "for line in f:\n",
    "    values = line.split()\n",
    "    word = values[0]\n",
    "    coefs = np.asarray(values[1:], dtype='float32')\n",
    "    embeddings_index[word] = coefs\n",
    "f.close()\n",
    "\n",
    "print('Total %s word vectors in Glove.' % len(embeddings_index))\n",
    "\n",
    "embedding_matrix = np.random.random((len(word_index) + 1, EMBEDDING_DIM))\n",
    "for word, index in word_index.items():\n",
    "    embedding_vector = embeddings_index.get(word)\n",
    "    if embedding_vector is not None:\n",
    "        # words not found in embedding index will be all-zeros.\n",
    "        embedding_matrix[index] = embedding_vector\n",
    "        \n",
    "embedding_layer = Embedding(len(word_index) + 1,\n",
    "                            EMBEDDING_DIM,\n",
    "                            weights=[embedding_matrix],\n",
    "                            input_length=MAX_SEQUENCE_LENGTH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0cad6b47",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
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    "outputId": "19556a7d-b511-4583-a7f5-69bc6198424f"
   },
   "outputs": [],
   "source": [
    "!pip install wandb\n",
    "\n",
    "!wandb login"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6ac5bb0",
   "metadata": {},
   "source": [
    "## Build the CNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76a754ce",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
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      "dc2b0b32436041638c74a088de23da37",
      "6bc46120667b4a4cae58dd961092bdb0"
     ]
    },
    "id": "373ef2fe",
    "outputId": "19556a7d-b511-4583-a7f5-69bc6198424f"
   },
   "outputs": [],
   "source": [
    "# custom CNN model\n",
    "import wandb\n",
    "from keras.layers import Dropout\n",
    "input_sequence = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')\n",
    "embedded_sequences = embedding_layer(input_sequence)\n",
    "\n",
    "#Add first convolutional layer\n",
    "covolution1= Conv1D(128, 5, activation='relu')(embedded_sequences)\n",
    "pool1 = MaxPooling1D(5)(covolution1)\n",
    "\n",
    "#Add dropout layer\n",
    "dropout1 = Dropout(0.2)(pool1)\n",
    "\n",
    "#Add second convolutional layer\n",
    "covolution2 = Conv1D(128, 5, activation='relu')(pool1)\n",
    "pool2 = MaxPooling1D(5)(covolution2)\n",
    "\n",
    "#Add dropout layer\n",
    "dropout2 = Dropout(0.2)(pool2)\n",
    "\n",
    "#Add third convolutional layer\n",
    "covolution3 = Conv1D(128, 5, activation='relu')(pool2)\n",
    "pool3 = MaxPooling1D(35)(covolution3)  # global max pooling\n",
    "\n",
    "#Add fourth layer\n",
    "l_flat = Flatten()(pool3)\n",
    "\n",
    "#Add two dense layers\n",
    "l_dense = Dense(128, activation='relu')(l_flat)\n",
    "dropout3 = Dropout(0.2)(l_dense)\n",
    "predictions = Dense(2, activation='softmax')(l_dense)\n",
    "\n",
    "#Use wandb to display training accuracy and loss graphs\n",
    "wandb.init(project=\"FakeNews\")\n",
    "config = wandb.config\n",
    "config.learning_rate = 0.01\n",
    "\n",
    "model = Model(input_sequence, predictions)\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer='adadelta',\n",
    "              metrics=['acc'])\n",
    "\n",
    "print(\"Fitting the convolutional neural network model\")\n",
    "model.summary()\n",
    "\n",
    "history = model.fit(x_train, y_train, validation_data=(x_val, y_val),\n",
    "          epochs=3, batch_size=128,callbacks=[WandbCallback()])\n",
    "\n",
    "#!wandb login --relogin"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a61aac25",
   "metadata": {},
   "source": [
    "## Save the model and plot its metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14c84fde",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
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    "id": "373ef2fe",
    "outputId": "19556a7d-b511-4583-a7f5-69bc6198424f"
   },
   "outputs": [],
   "source": [
    "import joblib\n",
    "model_filename = \"cnnepochs3.pkl\"\n",
    "joblib.dump(history.history, model_filename)\n",
    "\n",
    "# list all data in history\n",
    "print(history.history.keys())\n",
    "\n",
    "# summarize history for accuracy\n",
    "plt.plot(history.history['acc'])\n",
    "plt.plot(history.history['val_acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('accuracy')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "# summarize history for loss\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('loss')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf1f40c6",
   "metadata": {},
   "source": [
    "## Test the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1929961b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
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      "6bc46120667b4a4cae58dd961092bdb0"
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    },
    "id": "373ef2fe",
    "outputId": "19556a7d-b511-4583-a7f5-69bc6198424f"
   },
   "outputs": [],
   "source": [
    "# Test the model\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Test the model\n",
    "y_pred = model.predict(x_test)\n",
    "y_pred = np.round(y_pred).flatten()\n",
    "accuracy = accuracy_score(y_test.flatten(), y_pred)\n",
    "print(f\"Correct number of predictions obtained: {accuracy * len(y_test)}\")\n",
    "print(f\"Total number of test examples: {len(y_test)}\")\n",
    "print(f\"Accuracy of model: {accuracy:.2%}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ca27cdf",
   "metadata": {},
   "source": [
    "## Obtain Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "373ef2fe",
   "metadata": {
    "colab": {
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    "id": "373ef2fe",
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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      "Index(['id', 'title', 'author', 'text', 'label'], dtype='object')\n",
      "The raw input data looks like: \n",
      "   id                                              title              author  \\\n",
      "0   0  House Dem Aide: We Didn’t Even See Comey’s Let...       Darrell Lucus   \n",
      "1   1  FLYNN: Hillary Clinton, Big Woman on Campus - ...     Daniel J. Flynn   \n",
      "2   2                  Why the Truth Might Get You Fired  Consortiumnews.com   \n",
      "3   3  15 Civilians Killed In Single US Airstrike Hav...     Jessica Purkiss   \n",
      "4   4  Iranian woman jailed for fictional unpublished...      Howard Portnoy   \n",
      "\n",
      "                                                text  label  \n",
      "0  House Dem Aide: We Didn’t Even See Comey’s Let...      1  \n",
      "1  Ever get the feeling your life circles the rou...      0  \n",
      "2  Why the Truth Might Get You Fired October 29, ...      1  \n",
      "3  Videos 15 Civilians Killed In Single US Airstr...      1  \n",
      "4  Print \\nAn Iranian woman has been sentenced to...      1  \n",
      "There are 251876 unique tokens.\n",
      "Shape of data tensor: (20800, 1000)\n",
      "Shape of label tensor: (20800, 2)\n",
      "Size of train, validation, test: 16640 10400 10400\n",
      "Real and fake news in train,validation and test:\n",
      "[8287. 8353.]\n",
      "[5262. 5138.]\n",
      "[5125. 5275.]\n",
      "Total 400000 word vectors in Glove.\n",
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.0.0->wandb) (3.4)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.0.0->wandb) (1.26.15)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.0.0->wandb) (2022.12.7)\n",
      "Requirement already satisfied: smmap<6,>=3.0.1 in /usr/local/lib/python3.10/dist-packages (from gitdb<5,>=4.0.1->GitPython!=3.1.29,>=1.0.0->wandb) (5.0.0)\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mankithas\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
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      "text/html": [
       "Finishing last run (ID:8dcawhiq) before initializing another..."
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       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>acc</td><td>▁▇█</td></tr><tr><td>epoch</td><td>▁▅█</td></tr><tr><td>loss</td><td>█▄▁</td></tr><tr><td>val_acc</td><td>▁▇█</td></tr><tr><td>val_loss</td><td>█▄▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>acc</td><td>0.63185</td></tr><tr><td>best_epoch</td><td>2</td></tr><tr><td>best_val_loss</td><td>0.64234</td></tr><tr><td>epoch</td><td>2</td></tr><tr><td>loss</td><td>0.64942</td></tr><tr><td>val_acc</td><td>0.64115</td></tr><tr><td>val_loss</td><td>0.64234</td></tr></table><br/></div></div>"
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       " View run <strong style=\"color:#cdcd00\">solar-shape-11</strong> at: <a href='https://wandb.ai/ankithas/FakeNews/runs/8dcawhiq' target=\"_blank\">https://wandb.ai/ankithas/FakeNews/runs/8dcawhiq</a><br/>Synced 5 W&B file(s), 1 media file(s), 15 artifact file(s) and 1 other file(s)"
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       "Syncing run <strong><a href='https://wandb.ai/ankithas/FakeNews/runs/jyxf7bf8' target=\"_blank\">splendid-butterfly-12</a></strong> to <a href='https://wandb.ai/ankithas/FakeNews' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
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     "text": [
      "Fitting the convolutional neural network model\n",
      "Model: \"model_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_4 (InputLayer)        [(None, 1000)]            0         \n",
      "                                                                 \n",
      " embedding_3 (Embedding)     (None, 1000, 100)         25187700  \n",
      "                                                                 \n",
      " conv1d_9 (Conv1D)           (None, 996, 128)          64128     \n",
      "                                                                 \n",
      " max_pooling1d_9 (MaxPooling  (None, 199, 128)         0         \n",
      " 1D)                                                             \n",
      "                                                                 \n",
      " conv1d_10 (Conv1D)          (None, 195, 128)          82048     \n",
      "                                                                 \n",
      " max_pooling1d_10 (MaxPoolin  (None, 39, 128)          0         \n",
      " g1D)                                                            \n",
      "                                                                 \n",
      " conv1d_11 (Conv1D)          (None, 35, 128)           82048     \n",
      "                                                                 \n",
      " max_pooling1d_11 (MaxPoolin  (None, 1, 128)           0         \n",
      " g1D)                                                            \n",
      "                                                                 \n",
      " flatten_3 (Flatten)         (None, 128)               0         \n",
      "                                                                 \n",
      " dense_6 (Dense)             (None, 128)               16512     \n",
      "                                                                 \n",
      " dense_7 (Dense)             (None, 2)                 258       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 25,432,694\n",
      "Trainable params: 25,432,694\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/3\n",
      "130/130 [==============================] - ETA: 0s - loss: 0.6708 - acc: 0.6000"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla while saving (showing 4 of 4). These functions will not be directly callable after loading.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Adding directory to artifact (/content/wandb/run-20230503_021659-jyxf7bf8/files/model-best)... Done. 1.5s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "130/130 [==============================] - 202s 2s/step - loss: 0.6708 - acc: 0.6000 - val_loss: 0.6617 - val_acc: 0.6287\n",
      "Epoch 2/3\n",
      "130/130 [==============================] - ETA: 0s - loss: 0.6587 - acc: 0.6209"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla while saving (showing 4 of 4). These functions will not be directly callable after loading.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Adding directory to artifact (/content/wandb/run-20230503_021659-jyxf7bf8/files/model-best)... Done. 1.6s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "130/130 [==============================] - 190s 1s/step - loss: 0.6587 - acc: 0.6209 - val_loss: 0.6508 - val_acc: 0.6382\n",
      "Epoch 3/3\n",
      "130/130 [==============================] - ETA: 0s - loss: 0.6499 - acc: 0.6329"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla while saving (showing 4 of 4). These functions will not be directly callable after loading.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Adding directory to artifact (/content/wandb/run-20230503_021659-jyxf7bf8/files/model-best)... Done. 1.6s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "130/130 [==============================] - 210s 2s/step - loss: 0.6499 - acc: 0.6329 - val_loss: 0.6430 - val_acc: 0.6463\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Paste an API key from your profile and hit enter, or press ctrl+c to quit: \n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n",
      "dict_keys(['loss', 'acc', 'val_loss', 'val_acc'])\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "325/325 [==============================] - 21s 63ms/step\n",
      "Correct number of predictions obtained: 6602.0\n",
      "Total number of test examples: 10400\n",
      "Accuracy of model: 63.48%\n",
      "325/325 [==============================] - 21s 63ms/step\n",
      "Test accuracy of the model: 0.6348077058792114\n",
      "Test loss of the model: 0.6468451023101807\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.53      0.50      0.51      5454\n",
      "           1       0.48      0.51      0.49      4946\n",
      "\n",
      "   micro avg       0.50      0.50      0.50     10400\n",
      "   macro avg       0.50      0.50      0.50     10400\n",
      "weighted avg       0.50      0.50      0.50     10400\n",
      " samples avg       0.50      0.50      0.50     10400\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating the Confusion Matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "\n",
    "# Predict on test set and get the class with highest probability\n",
    "y_pred = np.argmax(model.predict(x_test), axis=1)\n",
    "\n",
    "# Compute confusion matrix\n",
    "cm = confusion_matrix(y_test.argmax(axis=1), y_pred)\n",
    "\n",
    "# Plot confusion matrix using seaborn heatmap\n",
    "sns.heatmap(cm, annot=True, cmap='Blues', fmt='d')\n",
    "plt.title('Confusion matrix - model')\n",
    "plt.xlabel('Predicted label')\n",
    "plt.ylabel('True label')\n",
    "\n",
    "#Evaluate the model\n",
    "score = model.evaluate(x_test, y_test, verbose=0)\n",
    "\n",
    "#Display test accuracy and loss\n",
    "print('Test accuracy of the model:', score[1])\n",
    "print('Test loss of the model:', score[0])\n",
    "\n",
    "#Display the classification report\n",
    "from sklearn.metrics import classification_report,confusion_matrix,accuracy_score\n",
    "print(classification_report(test_predictions,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bCzIuqSJlOP0",
   "metadata": {
    "id": "bCzIuqSJlOP0"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "IOWgnL4PlOWZ",
   "metadata": {
    "id": "IOWgnL4PlOWZ"
   },
   "outputs": [],
   "source": [
    "#### 5 epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "RGEJNxYLlQrv",
   "metadata": {
    "id": "RGEJNxYLlQrv"
   },
   "outputs": [],
   "source": []
  }
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