Introduction to Deep Learning with Keras/Introduction to Deep Learning with Keras.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Deep Learning with Keras  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\"><td>\n",
    "  <a target=\"_blank\"  href=\"https://colab.research.google.com/github/TannerGilbert/Tutorials/blob/master/Introduction%20to%20Deep%20Learning%20with%C2%A0Keras/Introduction%20to%20Deep%20Learning%20with%20Keras.ipynb\">\n",
    "    <img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab\n",
    "  </a>\n",
    "</td><td>\n",
    "  <a target=\"_blank\"  href=\"https://github.com/TannerGilbert/Tutorials/blob/master/Introduction%20to%20Deep%20Learning%20with%C2%A0Keras/Introduction%20to%20Deep%20Learning%20with%20Keras.ipynb\">\n",
    "    <img width=32px src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
    "</td></table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Keras is a high-level neural networks API, running on top of Tensorflow. It enables fast experimentation through a high level, user-friendly, modular and extensible API. Keras can also be run on both CPU and GPU.\n",
    "\n",
    "For more information check out the [corresponding article](https://gilberttanner.com/blog/introduction-to-deep-learning-withkeras)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.datasets import mnist\n",
    "\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)\n",
    "x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.astype('float32')\n",
    "x_train /= 255\n",
    "x_test /= 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28, 1)\n",
      "(10000, 28, 28, 1)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape)\n",
    "print(x_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28, 1)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(x_train.shape[1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.utils import to_categorical\n",
    "\n",
    "y_train = to_categorical(y_train, num_classes=10)\n",
    "y_test = to_categorical(y_test, num_classes=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 10)\n",
      "(10000, 10)\n"
     ]
    }
   ],
   "source": [
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((28, 28, 1), 1)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(x_train.shape[1:], 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating convolutional neural network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv2D, MaxPool2D, Dense, Flatten, Dropout\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Conv2D(filters=32, kernel_size=(5,5), activation='relu', input_shape=x_train.shape[1:]))\n",
    "model.add(Conv2D(filters=32, kernel_size=(5,5), activation='relu'))\n",
    "model.add(MaxPool2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(rate=0.25))\n",
    "model.add(Conv2D(filters=64, kernel_size=(3, 3), activation='relu'))\n",
    "model.add(Conv2D(filters=64, kernel_size=(3, 3), activation='relu'))\n",
    "model.add(MaxPool2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(rate=0.25))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(256, activation='relu'))\n",
    "model.add(Dropout(rate=0.5))\n",
    "model.add(Dense(10, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv2D, MaxPool2D, Dense, Flatten, Dropout\n",
    "\n",
    "model = Sequential([\n",
    "    Conv2D(filters=32, kernel_size=(5,5), activation='relu', input_shape=x_train.shape[1:]),\n",
    "    Conv2D(filters=32, kernel_size=(5,5), activation='relu'),\n",
    "    MaxPool2D(pool_size=(2, 2)),\n",
    "    Dropout(rate=0.25),\n",
    "    Conv2D(filters=64, kernel_size=(3,3), activation='relu'),\n",
    "    Conv2D(filters=64, kernel_size=(3,3), activation='relu'),\n",
    "    MaxPool2D(pool_size=(2, 2)),\n",
    "    Dropout(rate=0.25),\n",
    "    Flatten(),\n",
    "    Dense(256, activation='relu'),\n",
    "    Dropout(rate=0.5),\n",
    "    Dense(10, activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Conv2D, MaxPool2D, Dense, Flatten, Dropout, Input\n",
    "\n",
    "inputs = Input(shape=x_train.shape[1:])\n",
    "\n",
    "x = Conv2D(filters=32, kernel_size=(5,5), activation='relu')(inputs)\n",
    "x = Conv2D(filters=32, kernel_size=(5,5), activation='relu')(x)\n",
    "x = MaxPool2D(pool_size=(2, 2))(x)\n",
    "x = Dropout(rate=0.25)(x)\n",
    "\n",
    "x = Conv2D(filters=64, kernel_size=(3,3), activation='relu')(x)\n",
    "x = Conv2D(filters=64, kernel_size=(3,3), activation='relu')(x)\n",
    "x = MaxPool2D(pool_size=(2, 2))(x)\n",
    "x = Dropout(rate=0.25)(x)\n",
    "\n",
    "x = Flatten()(x)\n",
    "x = Dense(256, activation='relu')(x)\n",
    "x = Dropout(rate=0.5)(x)\n",
    "predictions = Dense(10, activation='softmax')(x)\n",
    "\n",
    "model = Model(inputs=inputs, outputs=predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    loss='categorical_crossentropy', \n",
    "    optimizer='adam', \n",
    "    metrics=['accuracy']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "# creating datagenerator for augmenting images\n",
    "datagen = ImageDataGenerator(\n",
    "        rotation_range=10,\n",
    "        zoom_range=0.1,\n",
    "        width_shift_range=0.1,\n",
    "        height_shift_range=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1875/1875 [==============================] - 22s 12ms/step - loss: 0.1037 - acc: 0.9741 - val_loss: 0.0445 - val_acc: 0.9908\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 22s 12ms/step - loss: 0.0879 - acc: 0.9781 - val_loss: 0.0259 - val_acc: 0.9937\n",
      "Epoch 3/5\n",
      "1875/1875 [==============================] - 22s 12ms/step - loss: 0.0835 - acc: 0.9788 - val_loss: 0.0321 - val_acc: 0.9926\n",
      "Epoch 4/5\n",
      "1875/1875 [==============================] - 22s 12ms/step - loss: 0.0819 - acc: 0.9792 - val_loss: 0.0264 - val_acc: 0.9936\n",
      "Epoch 5/5\n",
      "1875/1875 [==============================] - 22s 12ms/step - loss: 0.0790 - acc: 0.9790 - val_loss: 0.0220 - val_acc: 0.9938\n"
     ]
    }
   ],
   "source": [
    "epochs = 3\n",
    "batch_size = 32\n",
    "\n",
    "history = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size), epochs=epochs,\n",
    "                              validation_data=(x_test, y_test), steps_per_epoch=x_train.shape[0]//batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2873c579d68>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(history.history['accuracy'], label='training accuracy')\n",
    "plt.plot(history.history['val_accuracy'], label='testing accuracy')\n",
    "plt.title('Accuracy')\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('accuracy')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2873c5e1390>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'], label='training loss')\n",
    "plt.plot(history.history['val_loss'], label='testing loss')\n",
    "plt.title('Loss')\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('loss')\n",
    "plt.legend()"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}