3. Text Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width: 100%; overflow: hidden;\">\n",
    "    <div style=\"width: 150px; float: left;\"> <img src=\"data/D4Sci_logo_ball.png\" alt=\"Data For Science, Inc\" align=\"left\" border=\"0\"> </div>\n",
    "    <div style=\"float: left; margin-left: 10px;\"> <h1>NLP with Deep Learning for Everyone</h1>\n",
    "<h1>Text Classification</h1>\n",
    "        <p>Bruno Gonçalves<br/>\n",
    "        <a href=\"http://www.data4sci.com/\">www.data4sci.com</a><br/>\n",
    "            @bgoncalves, @data4sci</p></div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import gzip\n",
    "from collections import Counter\n",
    "from pprint import pprint\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "np.random.seed(42)\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "from tqdm import tqdm\n",
    "\n",
    "import keras\n",
    "import tensorflow as tf\n",
    "from keras.datasets import imdb\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Flatten\n",
    "from keras.layers import Conv1D\n",
    "from keras.layers import MaxPooling1D\n",
    "from keras.layers import Embedding\n",
    "from keras_preprocessing import sequence\n",
    "\n",
    "import watermark\n",
    "\n",
    "%load_ext watermark\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by print out the versions of the libraries we're using for future reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python implementation: CPython\n",
      "Python version       : 3.11.5\n",
      "IPython version      : 8.12.3\n",
      "\n",
      "Compiler    : Clang 14.0.6 \n",
      "OS          : Darwin\n",
      "Release     : 23.3.0\n",
      "Machine     : arm64\n",
      "Processor   : arm\n",
      "CPU cores   : 16\n",
      "Architecture: 64bit\n",
      "\n",
      "Git hash: 3022f3d8aeb2c24ebf2d47d4e9999181a7ad777d\n",
      "\n",
      "numpy              : 1.23.5\n",
      "pandas             : 2.0.3\n",
      "json               : 2.0.9\n",
      "keras              : 2.12.0\n",
      "watermark          : 2.4.3\n",
      "keras_preprocessing: 1.1.2\n",
      "matplotlib         : 3.7.2\n",
      "tensorflow         : 2.12.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%watermark -n -v -m -g -iv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load default figure style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('d4sci.mplstyle')\n",
    "colors = plt.rcParams['axes.prop_cycle'].by_key()['color']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# IMDB Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_words = 10_000\n",
    "(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=top_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each word is associated with a unique ID. We can easily obtain the dictionary mapping words to IDs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "word_dict = imdb.get_word_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we can check what the id of each word is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "970"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_dict['casting']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "164"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_dict['director']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also invert the index so that we can recover the original text. A few special tokens are reserved at the beginning of the id range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "word_list = [\"<PAD>\", \"<START>\", \"<UNK>\", \"<UNUSED>\"]\n",
    "word_list.extend([k for k, v in sorted(word_dict.items(), key = lambda x: x[1])])\n",
    "word_list = np.array(word_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       " 32]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"<START> this film was just brilliant casting location scenery story direction everyone's really suited the part they played and you could just imagine being there robert <UNK> is an amazing actor and now the same being director <UNK> father came from the same scottish island as myself so i loved the fact there was a real connection with this film the witty remarks throughout the film were great it was just brilliant so much that i bought the film as soon as it was released for <UNK> and would recommend it to everyone to watch and the fly fishing was amazing really cried at the end it was so sad and you know what they say if you cry at a film it must have been good and this definitely was also <UNK> to the two little boy's that played the <UNK> of norman and paul they were just brilliant children are often left out of the <UNK> list i think because the stars that play them all grown up are such a big profile for the whole film but these children are amazing and should be praised for what they have done don't you think the whole story was so lovely because it was true and was someone's life after all that was shared with us all\""
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\" \".join(word_list[X_train[0]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each review has associated with it a value of 0 or 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0, 1}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So the corresponding rating for review is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1 means positive, while 0 means negative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"<START> worst mistake of my life br br i picked this movie up at target for 5 because i figured hey it's sandler i can get some cheap laughs i was wrong completely wrong mid way through the film all three of my friends were asleep and i was still suffering worst plot worst script worst movie i have ever seen i wanted to hit my head up against a wall for an hour then i'd stop and you know why because it felt damn good upon bashing my head in i stuck that damn movie in the <UNK> and watched it burn and that felt better than anything else i've ever done it took american psycho army of darkness and kill bill just to get over that crap i hate you sandler for actually going through with this and ruining a whole day of my life\""
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\" \".join(word_list[X_train[4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our dataset is split into two equal parts, the training and the testing dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of training data: (25000,)\n",
      "Shape of test data: (25000,)\n"
     ]
    }
   ],
   "source": [
    "print('Shape of training data:', X_train.shape)\n",
    "print('Shape of test data:', X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feed Forward Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll start by training a simple FF model without any fancyness. The first step is to padd each instance so that they all have the same size. Here we'll use the first 450 words. If the review is longer, we trucate it, while if its shorter we pad it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_words = 500\n",
    "X_train = sequence.pad_sequences(X_train, maxlen=max_words)\n",
    "X_test = sequence.pad_sequences(X_test, maxlen=max_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"<PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <START> this film was just brilliant casting location scenery story direction everyone's really suited the part they played and you could just imagine being there robert <UNK> is an amazing actor and now the same being director <UNK> father came from the same scottish island as myself so i loved the fact there was a real connection with this film the witty remarks throughout the film were great it was just brilliant so much that i bought the film as soon as it was released for <UNK> and would recommend it to everyone to watch and the fly fishing was amazing really cried at the end it was so sad and you know what they say if you cry at a film it must have been good and this definitely was also <UNK> to the two little boy's that played the <UNK> of norman and paul they were just brilliant children are often left out of the <UNK> list i think because the stars that play them all grown up are such a big profile for the whole film but these children are amazing and should be praised for what they have done don't you think the whole story was so lovely because it was true and was someone's life after all that was shared with us all\""
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\" \".join(word_list[X_train[0]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Structure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our model will have an embedding layer to turn our numerical ids into vectors, a hidden dense layer of 32 neurons and ReLu activation followed by an output layer with just one neuron and a sigmoid activation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential() # []\n",
    "model.add(Embedding(top_words, 50, input_shape=(max_words,))) # [].append()\n",
    "## <==> ##\n",
    "model.add(Flatten())\n",
    "model.add(Dense(32, activation='relu'))\n",
    "model.add(Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In summary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding (Embedding)       (None, 500, 50)           500000    \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 25000)             0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 32)                800032    \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,300,065\n",
      "Trainable params: 1,300,065\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we compile our model using a binary cross entropy loss function and the Adam optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='binary_crossentropy', \n",
    "              optimizer='adam', \n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The final step is to fit the model using the training data. We also supply the test data for validation purposes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-02-26 20:02:30.702146: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "196/196 [==============================] - 2s 10ms/step - loss: 0.4793 - accuracy: 0.7448 - val_loss: 0.2870 - val_accuracy: 0.8798\n",
      "Epoch 2/10\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 0.1644 - accuracy: 0.9413 - val_loss: 0.3320 - val_accuracy: 0.8644\n",
      "Epoch 3/10\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 0.0434 - accuracy: 0.9904 - val_loss: 0.3757 - val_accuracy: 0.8676\n",
      "Epoch 4/10\n",
      "196/196 [==============================] - 2s 9ms/step - loss: 0.0077 - accuracy: 0.9994 - val_loss: 0.4084 - val_accuracy: 0.8730\n",
      "Epoch 5/10\n",
      "196/196 [==============================] - 2s 9ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.4389 - val_accuracy: 0.8753\n",
      "Epoch 6/10\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 9.7629e-04 - accuracy: 1.0000 - val_loss: 0.4653 - val_accuracy: 0.8756\n",
      "Epoch 7/10\n",
      "196/196 [==============================] - 2s 10ms/step - loss: 5.6921e-04 - accuracy: 1.0000 - val_loss: 0.4870 - val_accuracy: 0.8754\n",
      "Epoch 8/10\n",
      "196/196 [==============================] - 2s 9ms/step - loss: 3.7253e-04 - accuracy: 1.0000 - val_loss: 0.5057 - val_accuracy: 0.8751\n",
      "Epoch 9/10\n",
      "196/196 [==============================] - 2s 9ms/step - loss: 2.6300e-04 - accuracy: 1.0000 - val_loss: 0.5218 - val_accuracy: 0.8751\n",
      "Epoch 10/10\n",
      "196/196 [==============================] - 2s 9ms/step - loss: 1.8916e-04 - accuracy: 1.0000 - val_loss: 0.5372 - val_accuracy: 0.8753\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, y_train, # Training data\n",
    "          validation_data=(X_test, y_test), # The testing data for validation\n",
    "          epochs=10, # How many epochs to train for\n",
    "          batch_size=128, # The batch size. Gradients are updated after each batch \n",
    "          verbose=1) # The level of detail in the output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Small utility function to display the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_history(history):    \n",
    "    fig, ax_lst = plt.subplots(1, 2, sharex=True, sharey=True)\n",
    "\n",
    "    epochs = np.array(history.epoch) + 1\n",
    "    \n",
    "    ax_lst[0].plot(epochs, history.history['loss'], label='Training')\n",
    "    ax_lst[0].plot(epochs, history.history['val_loss'], label='Testing')\n",
    "    ax_lst[0].set_ylabel('Loss')\n",
    "    ax_lst[0].set_xlabel('Epoch')\n",
    "    ax_lst[0].set_xticks(epochs)\n",
    "    \n",
    "    best_epoch = np.argmin(history.history['val_loss']) + 1\n",
    "    ax_lst[0].axvline(x=best_epoch, linestyle=':', color=colors[2])\n",
    "    ax_lst[0].legend()\n",
    "    \n",
    "\n",
    "    ax_lst[1].plot(epochs, history.history['accuracy'], label='Training')\n",
    "    ax_lst[1].plot(epochs, history.history['val_accuracy'], label='Testing')\n",
    "    ax_lst[1].set_ylabel('Accuracy')\n",
    "    ax_lst[1].set_xlabel('Epoch')\n",
    "    ax_lst[1].set_xticks(epochs)\n",
    "    ax_lst[1].axvline(x=best_epoch, linestyle=':', color=colors[2])\n",
    "\n",
    "    fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 3840x2640 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like we are overfitting the data after just a few epochs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolutional Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we add a convolution layer between the embedding layer and the dense layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Embedding(top_words, 50, input_length=max_words))\n",
    "## <==> ##\n",
    "model.add(Conv1D(32, 3, padding='same', activation='relu'))\n",
    "model.add(MaxPooling1D())\n",
    "## <==> ##\n",
    "model.add(Flatten())\n",
    "model.add(Dense(32, activation='relu'))\n",
    "model.add(Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compile and fit the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding_1 (Embedding)     (None, 500, 50)           500000    \n",
      "                                                                 \n",
      " conv1d (Conv1D)             (None, 500, 32)           4832      \n",
      "                                                                 \n",
      " max_pooling1d (MaxPooling1D  (None, 250, 32)          0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " flatten_1 (Flatten)         (None, 8000)              0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 32)                256032    \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 1)                 33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 760,897\n",
      "Trainable params: 760,897\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss='binary_crossentropy', \n",
    "              optimizer='adam', \n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "196/196 [==============================] - 4s 20ms/step - loss: 0.5039 - accuracy: 0.7202 - val_loss: 0.2904 - val_accuracy: 0.8794\n",
      "Epoch 2/10\n",
      "196/196 [==============================] - 4s 22ms/step - loss: 0.2090 - accuracy: 0.9199 - val_loss: 0.2799 - val_accuracy: 0.8829\n",
      "Epoch 3/10\n",
      "196/196 [==============================] - 4s 20ms/step - loss: 0.1396 - accuracy: 0.9510 - val_loss: 0.3077 - val_accuracy: 0.8793\n",
      "Epoch 4/10\n",
      "196/196 [==============================] - 4s 20ms/step - loss: 0.0941 - accuracy: 0.9690 - val_loss: 0.3782 - val_accuracy: 0.8742\n",
      "Epoch 5/10\n",
      "196/196 [==============================] - 4s 20ms/step - loss: 0.0597 - accuracy: 0.9832 - val_loss: 0.4477 - val_accuracy: 0.8684\n",
      "Epoch 6/10\n",
      "196/196 [==============================] - 4s 19ms/step - loss: 0.0345 - accuracy: 0.9922 - val_loss: 0.5196 - val_accuracy: 0.8677\n",
      "Epoch 7/10\n",
      "196/196 [==============================] - 4s 19ms/step - loss: 0.0206 - accuracy: 0.9962 - val_loss: 0.5834 - val_accuracy: 0.8674\n",
      "Epoch 8/10\n",
      "196/196 [==============================] - 4s 19ms/step - loss: 0.0123 - accuracy: 0.9983 - val_loss: 0.6566 - val_accuracy: 0.8653\n",
      "Epoch 9/10\n",
      "196/196 [==============================] - 4s 19ms/step - loss: 0.0081 - accuracy: 0.9988 - val_loss: 0.7058 - val_accuracy: 0.8641\n",
      "Epoch 10/10\n",
      "196/196 [==============================] - 4s 19ms/step - loss: 0.0057 - accuracy: 0.9991 - val_loss: 0.7840 - val_accuracy: 0.8601\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, y_train, \n",
    "          validation_data=(X_test, y_test), # The testing data for validation\n",
    "          epochs=10, # How many epochs to train for\n",
    "          batch_size=128, # The batch size. Gradients are updated after each batch \n",
    "          verbose=1) # The level of detail in the output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model takes significantly longer to train, but produces much better results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3840x2640 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width: 100%; overflow: hidden;\">\n",
    "     <img src=\"data/D4Sci_logo_full.png\" alt=\"Data For Science, Inc\" align=\"center\" border=\"0\" width=300px> \n",
    "</div>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.5"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}