FINISHED 05 Your first Convolutional Neural Network.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "import keras\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.datasets import cifar10\n",
    "\n",
    "(pics, labels), _ = cifar10.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LET'S GET THE IMAGES READY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50000, 32, 32, 3)\n"
     ]
    }
   ],
   "source": [
    "print(pics.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x173665b7940>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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R4FKrNcMJauY8ca3ZDIfupZb6/hLAPYHxr7n7gc6/rgJfCHHlcMHgd/fHAPBysUKI9yRb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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17365b8d6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.imshow(pics[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 59,  62,  63],\n",
       "        [ 43,  46,  45],\n",
       "        [ 50,  48,  43],\n",
       "        ..., \n",
       "        [158, 132, 108],\n",
       "        [152, 125, 102],\n",
       "        [148, 124, 103]],\n",
       "\n",
       "       [[ 16,  20,  20],\n",
       "        [  0,   0,   0],\n",
       "        [ 18,   8,   0],\n",
       "        ..., \n",
       "        [123,  88,  55],\n",
       "        [119,  83,  50],\n",
       "        [122,  87,  57]],\n",
       "\n",
       "       [[ 25,  24,  21],\n",
       "        [ 16,   7,   0],\n",
       "        [ 49,  27,   8],\n",
       "        ..., \n",
       "        [118,  84,  50],\n",
       "        [120,  84,  50],\n",
       "        [109,  73,  42]],\n",
       "\n",
       "       ..., \n",
       "       [[208, 170,  96],\n",
       "        [201, 153,  34],\n",
       "        [198, 161,  26],\n",
       "        ..., \n",
       "        [160, 133,  70],\n",
       "        [ 56,  31,   7],\n",
       "        [ 53,  34,  20]],\n",
       "\n",
       "       [[180, 139,  96],\n",
       "        [173, 123,  42],\n",
       "        [186, 144,  30],\n",
       "        ..., \n",
       "        [184, 148,  94],\n",
       "        [ 97,  62,  34],\n",
       "        [ 83,  53,  34]],\n",
       "\n",
       "       [[177, 144, 116],\n",
       "        [168, 129,  94],\n",
       "        [179, 142,  87],\n",
       "        ..., \n",
       "        [216, 184, 140],\n",
       "        [151, 118,  84],\n",
       "        [123,  92,  72]]], dtype=uint8)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pics[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 32, 32, 3)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pics = pics.astype('float32')\n",
    "pics /= 255\n",
    "pics.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0.23137255,  0.24313726,  0.24705882],\n",
       "        [ 0.16862746,  0.18039216,  0.17647059],\n",
       "        [ 0.19607843,  0.1882353 ,  0.16862746],\n",
       "        ..., \n",
       "        [ 0.61960787,  0.51764709,  0.42352942],\n",
       "        [ 0.59607846,  0.49019608,  0.40000001],\n",
       "        [ 0.58039218,  0.48627451,  0.40392157]],\n",
       "\n",
       "       [[ 0.0627451 ,  0.07843138,  0.07843138],\n",
       "        [ 0.        ,  0.        ,  0.        ],\n",
       "        [ 0.07058824,  0.03137255,  0.        ],\n",
       "        ..., \n",
       "        [ 0.48235294,  0.34509805,  0.21568628],\n",
       "        [ 0.46666667,  0.32549021,  0.19607843],\n",
       "        [ 0.47843137,  0.34117648,  0.22352941]],\n",
       "\n",
       "       [[ 0.09803922,  0.09411765,  0.08235294],\n",
       "        [ 0.0627451 ,  0.02745098,  0.        ],\n",
       "        [ 0.19215687,  0.10588235,  0.03137255],\n",
       "        ..., \n",
       "        [ 0.4627451 ,  0.32941177,  0.19607843],\n",
       "        [ 0.47058824,  0.32941177,  0.19607843],\n",
       "        [ 0.42745098,  0.28627452,  0.16470589]],\n",
       "\n",
       "       ..., \n",
       "       [[ 0.81568629,  0.66666669,  0.3764706 ],\n",
       "        [ 0.78823531,  0.60000002,  0.13333334],\n",
       "        [ 0.7764706 ,  0.63137257,  0.10196079],\n",
       "        ..., \n",
       "        [ 0.627451  ,  0.52156866,  0.27450982],\n",
       "        [ 0.21960784,  0.12156863,  0.02745098],\n",
       "        [ 0.20784314,  0.13333334,  0.07843138]],\n",
       "\n",
       "       [[ 0.70588237,  0.54509807,  0.3764706 ],\n",
       "        [ 0.67843139,  0.48235294,  0.16470589],\n",
       "        [ 0.72941178,  0.56470591,  0.11764706],\n",
       "        ..., \n",
       "        [ 0.72156864,  0.58039218,  0.36862746],\n",
       "        [ 0.38039216,  0.24313726,  0.13333334],\n",
       "        [ 0.32549021,  0.20784314,  0.13333334]],\n",
       "\n",
       "       [[ 0.69411767,  0.56470591,  0.45490196],\n",
       "        [ 0.65882355,  0.50588238,  0.36862746],\n",
       "        [ 0.7019608 ,  0.55686277,  0.34117648],\n",
       "        ..., \n",
       "        [ 0.84705883,  0.72156864,  0.54901963],\n",
       "        [ 0.59215689,  0.4627451 ,  0.32941177],\n",
       "        [ 0.48235294,  0.36078432,  0.28235295]]], dtype=float32)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pics[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NOW LET'S GET OUR LABELS PREPARED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6]\n"
     ]
    }
   ],
   "source": [
    "print(labels[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The CIFAR database has labels for 10 different categories. Let's create a lookup so that we can match the labels with more human readable text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "frog\n"
     ]
    }
   ],
   "source": [
    "class_names = ['airplane','automobile','bird','cat','deer',\n",
    "               'dog','frog','horse','ship','truck']\n",
    "\n",
    "number_of_classes = len(class_names)\n",
    "\n",
    "word_label = class_names[ int(labels[0]) ]\n",
    "print(word_label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have a way of getting our labels into human readable format, let's one_hot_enocode these labels so that they can be used by the A.I. for training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.  0.  0.  0.  0.  0.  1.  0.  0.  0.]\n"
     ]
    }
   ],
   "source": [
    "labels = keras.utils.to_categorical(labels, number_of_classes)\n",
    "print(labels[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "frog\n"
     ]
    }
   ],
   "source": [
    "def labelToText(_label):\n",
    "    print(class_names[np.argmax(_label)])\n",
    "    \n",
    "labelToText(labels[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BUILDING OUR MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, Activation, Dense, Flatten\n",
    "# adding some additional features...\n",
    "from keras.layers import MaxPooling2D, Dropout\n",
    "\n",
    "# THIS TELLS KERAS WE WILL BE ADDING LAYERS IN ORDER\n",
    "model = Sequential()\n",
    "\n",
    "# OUR FIRST COLLECTION OF LAYERS\n",
    "model.add( Conv2D(48, (3, 3), padding='same', input_shape=(32, 32, 3), activation='relu' ))\n",
    "model.add( Conv2D(48, (3,3), activation='relu' ))\n",
    "model.add( MaxPooling2D( pool_size=(2,2)))\n",
    "model.add( Dropout(0.25))\n",
    "\n",
    "# SECOND LAYER, SAME AS FIRST EXCEPT FOR TWICE THE FEATURE MAPS\n",
    "model.add( Conv2D(96, (3, 3), padding='same', activation='relu'))\n",
    "model.add( Conv2D(96, (3,3), activation='relu' ))\n",
    "model.add( MaxPooling2D( pool_size=(2,2)))\n",
    "model.add( Dropout(0.25))\n",
    "\n",
    "# ONE MORE TIME, DOUBLED AGAIN...\n",
    "model.add( Conv2D(192, (3, 3), padding='same', activation='relu'))\n",
    "model.add( Conv2D(192, (3,3), activation='relu' ))\n",
    "model.add( MaxPooling2D( pool_size=(2,2)))\n",
    "model.add( Dropout(0.25))\n",
    "\n",
    "# NOW LET'S START FORCING THE MODEL TO CONVERGE ON 1 of !0 CLASS NAMES\n",
    "model.add(Flatten())\n",
    "model.add(Dense(512, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(256, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(number_of_classes, activation='softmax'))\n",
    "\n",
    "# OUR MODEL IS FINISHED. LET'S TELL KERAS TO COMPILE IT\n",
    "model.compile( optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 45000 samples, validate on 5000 samples\n",
      "Epoch 1/1\n",
      "45000/45000 [==============================] - 25s 550us/step - loss: 1.8852 - acc: 0.2722 - val_loss: 1.4462 - val_acc: 0.4694\n"
     ]
    }
   ],
   "source": [
    "batch_size = 128\n",
    "\n",
    "def TrainingLoop(number_of_epochs):\n",
    "    model.fit(pics, labels, \\\n",
    "              batch_size=batch_size, \\\n",
    "              epochs=number_of_epochs, \\\n",
    "              verbose=1, \\\n",
    "              validation_split=.1 )\n",
    "    \n",
    "TrainingLoop(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TESTING OUR MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32, 32, 3)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_image = pics[30]\n",
    "test_image.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1738e80deb8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qCo6v/QSX7P7vt/uobcfuedS25W1eJGbt0pPB8XkRebBc4j0UK0M8k7FY4JmC1UiWqVl4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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x173662c5630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(test_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 32, 32, 3)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_image_batch = np.expand_dims(test_image, axis=0)\n",
    "test_image_batch.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 71ms/step\n",
      "[[ 0.21419773  0.24433167  0.03350829  0.01116295  0.02244099  0.00662247\n",
      "   0.01248895  0.00691097  0.33798772  0.11034828]]\n"
     ]
    }
   ],
   "source": [
    "result = model.predict(test_image_batch, batch_size=1, verbose=1)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ship\n"
     ]
    }
   ],
   "source": [
    "print( class_names[np.argmax(result, axis=1)[0]] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# WE NEED A LOT MORE TRAINING...\n",
    "\n",
    "But our computers are a bit too slow. Let's figure out how to load 'pre-trained weights' into our model.\n",
    "\n",
    "First, let's see what I did previously (on a much faster computer with external GPU) after I loaded this Jupyter Notebook and ran TrainingLoop(10). Once I felt the A.I. model had increased in accuracy to an acceptable degree, I saved the trained weights like this:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# I'm commenting this out so you don't overwrite the file on your copy!\n",
    "#model.save_weights('cifar_cnn_weights_final.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assuming you have code for instantiating your model, you can then load the weights you saved from one computer running this notebook, into a model with the same architecture on a different computer, by sharing the weights file and doing this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_weights('cifar_cnn_weights_final.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's test it again, with the weights we've trained over time on a faster system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 4ms/step\n",
      "------------------------------------------\n",
      "airplane\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1738f98d358>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FwS9Eoij4hUgUBb8QiaLgFyJRFPxCJIqCX4hE+X9VF3cp+U3wQQAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1738f95bac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "test_image = pics[30]\n",
    "\n",
    "test_image_batch = np.expand_dims(test_image, axis=0)\n",
    "result = model.predict(test_image_batch, batch_size=1, verbose=1)\n",
    "\n",
    "print(\"------------------------------------------\")\n",
    "print( class_names[np.argmax(result, axis=1)[0]] )\n",
    "plt.imshow(test_image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use this technique of loading in pre-trained weights for a future project, where the training time would be significant and computationally expensive."
   ]
  }
 ],
 "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}