Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "7eJBVm9VufWT",
    "outputId": "c03e24b1-b173-41b1-91b8-edce76c7d571"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "import os\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import keras\n",
    "from keras import backend as K\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Input, Dense, Conv1D, Dropout, MaxPool1D, Flatten\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-rKO7eunufWd"
   },
   "outputs": [],
   "source": [
    "# This is not a mandatory step\n",
    "# This is used to set the figure size so that the plots appear to be of that size\n",
    "# If this parameter is not set, then the default plot size is used\n",
    "plt.rcParams['figure.figsize'] = 30, 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aKnMvN-1ufWh"
   },
   "outputs": [],
   "source": [
    "# Loading all the preformatted datasets fromm the saved location\n",
    "X_train = np.load('X_train.npy')\n",
    "y_train = np.load('y_train.npy')\n",
    "X_test = np.load('X_test.npy')\n",
    "y_test = np.load('y_test.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CRZQCrAFufWl"
   },
   "outputs": [],
   "source": [
    "# This step is actually supposed to be used to randomly split dataset into training and testing set as well as to shuffle them\n",
    "# However, here I am using it just to shuffle the dataset and therefore the test_size is equated to 0\n",
    "X_train, _, y_train, _ = train_test_split(X_train, y_train, test_size=0, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "Z6YYmeXXufWo",
    "outputId": "daafb9f2-f16b-4f89-bfcc-487d5d7080ce"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1200, 256, 2) (1200, 1)\n",
      "(180, 256, 2) (180, 1)\n"
     ]
    }
   ],
   "source": [
    "# Just printing out the dimentions to verify the data\n",
    "print(X_train.shape, y_train.shape)\n",
    "print(X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 646
    },
    "colab_type": "code",
    "id": "lzEE6ykmufWt",
    "outputId": "339aae55-e38e-46a3-d18b-bf7c4ca42521"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1d_1 (Conv1D)            (None, 256, 512)          33280     \n",
      "_________________________________________________________________\n",
      "conv1d_2 (Conv1D)            (None, 256, 512)          8389120   \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 256, 512)          0         \n",
      "_________________________________________________________________\n",
      "conv1d_3 (Conv1D)            (None, 256, 256)          4194560   \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 256, 256)          0         \n",
      "_________________________________________________________________\n",
      "conv1d_4 (Conv1D)            (None, 256, 256)          2097408   \n",
      "_________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1 (None, 2, 256)            0         \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 2, 256)            0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 128)               65664     \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 129       \n",
      "=================================================================\n",
      "Total params: 14,780,161\n",
      "Trainable params: 14,780,161\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Build the model\n",
    "\n",
    "# The model architecture type is sequential hence that is used\n",
    "model = Sequential()\n",
    "\n",
    "# We are using 4 convolution layers for feature extraction\n",
    "model.add(Conv1D(filters=512, kernel_size=32, padding='same', kernel_initializer='normal', activation='relu', input_shape=(256, 2)))\n",
    "model.add(Conv1D(filters=512, kernel_size=32, padding='same', kernel_initializer='normal', activation='relu'))\n",
    "model.add(Dropout(0.2)) # This is the dropout layer. It's main function is to inactivate 20% of neurons in order to prevent overfitting\n",
    "model.add(Conv1D(filters=256, kernel_size=32, padding='same', kernel_initializer='normal', activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Conv1D(filters=256, kernel_size=32, padding='same', kernel_initializer='normal', activation='relu'))\n",
    "model.add(MaxPool1D(pool_size=128)) # We use MaxPooling with a filter size of 128. This also contributes to generalization\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "# The prevous step gices an output of multi dimentional data, which cannot be fead directly into the feed forward neural network. Hence, the model is flattened\n",
    "model.add(Flatten())\n",
    "# One hidden layer of 128 neurons have been used in order to have better classification results\n",
    "model.add(Dense(units=128, kernel_initializer='normal', activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "# The final neuron HAS to be 1 in number and cannot be more than that. This is because this is a binary classification problem and only 1 neuron is enough to denote the class '1' or '0'\n",
    "model.add(Dense(units=1, activation='sigmoid'))\n",
    "\n",
    "# Print the summary of the model\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 255
    },
    "colab_type": "code",
    "id": "MN5v8a7sufWx",
    "outputId": "d91f835f-0cd5-45c4-e013-e95132a83ebd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Train on 1200 samples, validate on 180 samples\n",
      "Epoch 1/5\n",
      "1200/1200 [==============================] - 14s 12ms/step - loss: 4.0444 - acc: 0.5750 - val_loss: 0.7130 - val_acc: 0.6222\n",
      "Epoch 2/5\n",
      "1200/1200 [==============================] - 7s 6ms/step - loss: 1.5851 - acc: 0.7725 - val_loss: 0.2993 - val_acc: 0.9333\n",
      "Epoch 3/5\n",
      "1200/1200 [==============================] - 7s 6ms/step - loss: 0.8153 - acc: 0.8375 - val_loss: 0.2989 - val_acc: 0.9111\n",
      "Epoch 4/5\n",
      "1200/1200 [==============================] - 7s 6ms/step - loss: 0.5098 - acc: 0.8717 - val_loss: 0.2252 - val_acc: 0.9111\n",
      "Epoch 5/5\n",
      "1200/1200 [==============================] - 7s 6ms/step - loss: 0.4888 - acc: 0.8842 - val_loss: 0.2281 - val_acc: 0.9111\n"
     ]
    }
   ],
   "source": [
    "# Train the mode\n",
    "# To my experience, the Stocastic Gradient Descent Optimizer works the best. Adam optimizer also works but not as good as SGD\n",
    "optimizer = keras.optimizers.SGD(lr=0.001, momentum=0.5)\n",
    "model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n",
    "history = model.fit(X_train, y_train,  batch_size=64, epochs=5, validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 917
    },
    "colab_type": "code",
    "id": "HUmOUcbOufW5",
    "outputId": "563017d9-843e-4a44-95ad-aae708998f9f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Accuracy Curves')"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
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Jks444wx9/etfL3RsAECOYrHUbrH9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      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the model Accuracy graph (Ideally, it should be Logarithmic shape)\n",
    "plt.plot(history.history['acc'],'r',linewidth=3.0, label='Training Accuracy')\n",
    "plt.plot(history.history['val_acc'],'b',linewidth=3.0, label='Testing Accuracy')\n",
    "plt.legend(fontsize=18)\n",
    "plt.xlabel('Epochs ', fontsize=16)\n",
    "plt.ylabel('Accuracy', fontsize=16)\n",
    "plt.title('Accuracy Curves', fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 917
    },
    "colab_type": "code",
    "id": "hFXhiy5uufW9",
    "outputId": "364c95ef-7159-49a3-b66c-7eee82c8f76f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Loss Curves')"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAFCz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\nA9cC8x6ui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      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the model Loss graph (Ideally it should be Exponentially decreasing shape)\n",
    "plt.plot(history.history['loss'], 'g', linewidth=3.0, label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], 'y', linewidth=3.0, label='Testing Loss')\n",
    "plt.legend(fontsize=18)\n",
    "plt.xlabel('Epochs ', fontsize=16)\n",
    "plt.ylabel('Loss', fontsize=16)\n",
    "plt.title('Loss Curves', fontsize=16)"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "name": "Model.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}