kerasMNIST-CNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Keras MNIST with CNN\n",
    "### LeNet Architecture\n",
    "#### input ==> convolution ==> pooling ==> convolution ==> pooling ==> flatten ==> fully connected NN ==> output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- number of parameters drastically decreases with CNN, since the parameters to tune are the kernels, not individual pixel inputs\n",
    "- kernel stride is how many pixels the convolution kernal moves across an image\n",
    "- multiply the kernel, sum the values, divide by number of pixels in the kernel\n",
    "- https://mlnotebook.github.io/post/CNN1/\n",
    "- https://www.saama.com/blog/different-kinds-convolutional-filters/\n",
    "- some kernels http://aishack.in/tutorials/image-convolution-examples/\n",
    "- relu is used since sigmoid and tanh result in vanishing gradient problem\n",
    "- https://en.wikipedia.org/wiki/Vanishing_gradient_problem\n",
    "- max pooling https://computersciencewiki.org/index.php/Max-pooling_/_Pooling\n",
    "- visualize the convolution and pooling http://scs.ryerson.ca/~aharley/vis/conv/flat.html\n",
    "- google colab https://colab.research.google.com/notebooks/welcome.ipynb#scrollTo=-Rh3-Vt9Nev9 use free GPU for training\n",
    "- runtime, select hardware accelerator\n",
    "- save and deploy keras model http://www.bitbionic.com/2017/08/18/run-your-keras-models-in-c-tensorflow/\n",
    "- port keras model to c++ https://github.com/pplonski/keras2cpp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Flatten\n",
    "from keras.layers import Dropout\n",
    "from keras.layers.convolutional import Conv2D\n",
    "from keras.layers.convolutional import MaxPooling2D\n",
    "from keras.optimizers import Adam\n",
    "from keras.utils.np_utils import to_categorical\n",
    "from keras.models import Model #helps visualize output of the model\n",
    "import random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functions\n",
    "- TODO: Parameterize the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def leNetModel():\n",
    "    \"\"\"\n",
    "    Create a LeNet CNN Model\n",
    "    See https://www.pyimagesearch.com/2018/12/31/keras-conv2d-and-convolutional-layers/ for model parameter tutorial\n",
    "    https://www.pyimagesearch.com/2018/09/10/keras-tutorial-how-to-get-started-with-keras-deep-learning-and-python/\n",
    "    \"\"\"\n",
    "    model = Sequential()\n",
    "    model.add(Conv2D(32, (3,3), input_shape=(28, 28, 1), activation='relu', strides=(1,1), padding='valid'))\n",
    "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "    model.add(Conv2D(16, (3,3), activation='relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(512, activation='relu'))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(10, activation='softmax'))\n",
    "    model.compile(Adam(.01), loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the dataset\n",
    "- note the order of X_train, y_train, X_test, y_test!\n",
    "- NOT the same as sklearn's train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28)\n",
      "(10000, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot a sample of the dataset\n",
    "- changed the background color of the plot with fig.set_facecolor due to dark theme in my environment\n",
    "- https://stackoverflow.com/questions/14088687/how-to-change-plot-background-color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x1080 with 50 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "numSamples = []\n",
    "rows = 10\n",
    "cols = 5\n",
    "fig, axes = plt.subplots(nrows=rows, ncols=cols, figsize=(10,15))\n",
    "fig.set_facecolor('c') #c for cyan\n",
    "fig.tight_layout()\n",
    "for i in range(0, cols):\n",
    "    for j in range(0, rows):\n",
    "        _x = X_train[y_train==j] #could iterate over a smaller set, but we need to count total number of samples per digit\n",
    "        axes[j][i].imshow(_x[random.randint(0, len(_x)-1), :,:]) #could use cmap=\"gray\"\n",
    "        axes[j][i].set_xticks([]) #turns the tick marks off\n",
    "        axes[j][i].set_yticks([])\n",
    "        if(i == 2):\n",
    "            axes[j][i].set_title(\"digit: %d\" %j)\n",
    "            numSamples.append(len(_x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display training set distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x7f1d25556da0>,\n",
       "  <matplotlib.axis.XTick at 0x7f1d25556668>,\n",
       "  <matplotlib.axis.XTick at 0x7f1d255563c8>,\n",
       "  <matplotlib.axis.XTick at 0x7f1d25567b00>,\n",
       "  <matplotlib.axis.XTick at 0x7f1d25567d30>,\n",
       "  <matplotlib.axis.XTick at 0x7f1d244f24e0>,\n",
       "  <matplotlib.axis.XTick at 0x7f1d244f29b0>,\n",
       "  <matplotlib.axis.XTick at 0x7f1d244f2e80>,\n",
       "  <matplotlib.axis.XTick at 0x7f1d244f8390>,\n",
       "  <matplotlib.axis.XTick at 0x7f1d244f8860>],\n",
       " <a list of 10 Text xticklabel objects>)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "numSamples\n",
    "plt.figure(figsize=(12,4))\n",
    "plt.bar(range(0,rows), numSamples)\n",
    "plt.title(\"digit distribution\")\n",
    "plt.xlabel(\"digit\")\n",
    "plt.ylabel(\"count\")\n",
    "plt.xticks(np.arange(0,10,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare the data\n",
    "- use one hot encoding (to_categorical)\n",
    "- normalize the data set, the math uses exponential numbers and e^1 is much smaller than e^255, also it ensures features are not misproportionally weighted\n",
    "- add depth to the data set, each image is (28,28) ==> (28,28,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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)\n",
    "y_train = to_categorical(y_train, 10)\n",
    "y_test = to_categorical(y_test, 10)\n",
    "X_train = X_train / 255\n",
    "y_train = y_train / 255\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a CNN Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 26, 26, 32)        320       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 11, 11, 16)        4624      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 5, 5, 16)          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 400)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 512)               205312    \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                5130      \n",
      "=================================================================\n",
      "Total params: 215,386\n",
      "Trainable params: 215,386\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model = leNetModel()\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit the model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 51000 samples, validate on 9000 samples\n",
      "Epoch 1/10\n",
      "51000/51000 [==============================] - 14s 267us/step - loss: 0.0011 - acc: 0.9128 - val_loss: 2.3202e-04 - val_acc: 0.9828\n",
      "Epoch 2/10\n",
      "51000/51000 [==============================] - 13s 257us/step - loss: 2.8941e-04 - acc: 0.9773 - val_loss: 1.7231e-04 - val_acc: 0.9874\n",
      "Epoch 3/10\n",
      "51000/51000 [==============================] - 15s 286us/step - loss: 2.0528e-04 - acc: 0.9840 - val_loss: 1.6354e-04 - val_acc: 0.9873\n",
      "Epoch 4/10\n",
      "51000/51000 [==============================] - 15s 285us/step - loss: 1.7938e-04 - acc: 0.9855 - val_loss: 1.4609e-04 - val_acc: 0.9890\n",
      "Epoch 5/10\n",
      "51000/51000 [==============================] - 15s 285us/step - loss: 1.6330e-04 - acc: 0.9863 - val_loss: 1.4841e-04 - val_acc: 0.9889\n",
      "Epoch 6/10\n",
      "51000/51000 [==============================] - 15s 290us/step - loss: 1.4401e-04 - acc: 0.9882 - val_loss: 1.5469e-04 - val_acc: 0.9877\n",
      "Epoch 7/10\n",
      "51000/51000 [==============================] - 14s 278us/step - loss: 1.3304e-04 - acc: 0.9889 - val_loss: 1.4842e-04 - val_acc: 0.9892\n",
      "Epoch 8/10\n",
      "51000/51000 [==============================] - 14s 271us/step - loss: 1.2485e-04 - acc: 0.9898 - val_loss: 1.4892e-04 - val_acc: 0.9903\n",
      "Epoch 9/10\n",
      "51000/51000 [==============================] - 14s 272us/step - loss: 1.1973e-04 - acc: 0.9902 - val_loss: 1.5412e-04 - val_acc: 0.9890\n",
      "Epoch 10/10\n",
      "51000/51000 [==============================] - 14s 266us/step - loss: 1.0533e-04 - acc: 0.9909 - val_loss: 1.5094e-04 - val_acc: 0.9893\n"
     ]
    }
   ],
   "source": [
    "hist = model.fit(X_train, y_train, epochs=10, validation_split=.20, batch_size=510, verbose=1, shuffle=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model performance\n",
    "- good explaination of model loss and accuracy https://stackoverflow.com/questions/34518656/how-to-interpret-loss-and-accuracy-for-a-machine-learning-model\n",
    "- the larger the gap between the validation and training loss / accuracy, the higher the chance that the model is overfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f1d2702a630>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(hist.history['loss'], label=\"loss\")\n",
    "plt.plot(hist.history['val_loss'], label=\"val_loss\")\n",
    "plt.title(\"model loss summary\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f1d27054208>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(hist.history['acc'], label=\"accuracy\")\n",
    "plt.plot(hist.history['val_acc'], label=\"val_accuracy\")\n",
    "plt.title(\"model accuracy summary\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate the model\n",
    "- score is a measure of loss, accuracy is a measure of performance (see above link)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model score: 0.23, accuracy: 0.99\n"
     ]
    }
   ],
   "source": [
    "score1 = model.evaluate(X_test, y_test, verbose=0)\n",
    "print(\"model score: {:.2f}, accuracy: {:.2f}\".format(score1[0], score1[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test the models with a random image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f1cf80e16a0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2\n",
    "import requests\n",
    "from PIL import Image\n",
    "url = 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcST8KzXHtkSHcxzdpnllMhAj0upLEwnNFdtY6j4YUPcmaf4Ty3u'\n",
    "res = requests.get(url, stream=True)\n",
    "img = Image.open(res.raw)\n",
    "plt.title(\"raw image\")\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f1cf80ba978>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = np.asarray(img)\n",
    "img = cv2.resize(img, (28,28))\n",
    "#need to invert the colors! (this is black on white, above digits are white on black)\n",
    "gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "gray = cv2.bitwise_not(gray)\n",
    "print(gray.shape)\n",
    "plt.title(\"black and white\")\n",
    "plt.imshow(gray)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "gray = gray/255\n",
    "gray = gray.reshape(1, 28, 28, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model score: 0.23, accuracy: 0.99\n",
      "model prediction:  [3]\n"
     ]
    }
   ],
   "source": [
    "pred = model.predict_classes(gray)\n",
    "print(\"model score: {:.2f}, accuracy: {:.2f}\".format(score1[0], score1[1]))\n",
    "print(\"model prediction: \", str(pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize the Convolution kernels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "l1 = Model(inputs=model.layers[0].input, outputs=model.layers[0].output)\n",
    "l2 = Model(inputs=model.layers[0].input, outputs=model.layers[2].output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 26, 26, 32)\n",
      "(1, 11, 11, 16)\n"
     ]
    }
   ],
   "source": [
    "l1Viz, l2Viz = l1.predict(gray), l2.predict(gray)\n",
    "print(l1Viz.shape)\n",
    "print(l2Viz.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x864 with 32 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rows = 4\n",
    "cols = 8\n",
    "fig, axes = plt.subplots(nrows=rows, ncols=cols, figsize=(18,12))\n",
    "#fig.tight_layout()\n",
    "for i in range(0,32):\n",
    "    ax = plt.subplot(rows, cols, i+1)\n",
    "    ax.imshow(l1Viz[0,:,:, i], cmap='jet')\n",
    "    ax.set_title(\"kernel: %d\" % (i))\n",
    "    plt.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rows = 4\n",
    "cols = 4\n",
    "fig, axes = plt.subplots(nrows=rows, ncols=cols, figsize=(12,10))\n",
    "#fig.tight_layout()\n",
    "for i in range(0,16):\n",
    "    ax = plt.subplot(rows, cols, i+1)\n",
    "    ax.imshow(l2Viz[0,:,:, i], cmap='jet')\n",
    "    ax.set_title(\"kernel: %d\" % (i))\n",
    "    plt.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}