kerasMNIST-MLP.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Keras MLP with MNIST \n",
    "- use training set to learn the features and model parameters \n",
    "- use validation set to tune the model hyperparameters\n",
    "- use test set to measure model performance"
   ]
  },
  {
   "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",
    "import tensorflow as tf\n",
    "#used to grab random samples to visualize\n",
    "import random\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.optimizers import Adam\n",
    "from keras. utils.np_utils import to_categorical"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functions\n",
    "- builds a 3 layer model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getModel(numInput, numHidden, numOutput, alpha):\n",
    "    \"\"\"Returns a DNN model for the MNIST set\"\"\"\n",
    "    model = Sequential()\n",
    "    model.add(Dense(numInput, input_dim=784, activation='relu'))\n",
    "    model.add(Dense(numHidden, activation='relu'))\n",
    "    model.add(Dense(numOutput, activation='softmax')) #use softmax here because this is the output layer\n",
    "    model.compile(Adam(alpha), 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": {
      "image/png": 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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\n",
    "- uses a bar plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x7f27f6cfcef0>,\n",
       "  <matplotlib.axis.XTick at 0x7f27f6cfc828>,\n",
       "  <matplotlib.axis.XTick at 0x7f27f6cfc550>,\n",
       "  <matplotlib.axis.XTick at 0x7f27f6d0ccf8>,\n",
       "  <matplotlib.axis.XTick at 0x7f27f6d18208>,\n",
       "  <matplotlib.axis.XTick at 0x7f27f6d186d8>,\n",
       "  <matplotlib.axis.XTick at 0x7f27f6d18ba8>,\n",
       "  <matplotlib.axis.XTick at 0x7f27f6d1e128>,\n",
       "  <matplotlib.axis.XTick at 0x7f27f6d1e588>,\n",
       "  <matplotlib.axis.XTick at 0x7f27f6d1ea58>],\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 (X / 255) to ensure features (pixel intensities) are not misproportionally weighted\n",
    "- 255 due to pixel value range [0 255]\n",
    "- flatten the training and test set with reshape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    "\n",
    "X_train = X_train.reshape(X_train.shape[0], 784) #images are 28x28, which is 784\n",
    "X_test = X_test.reshape(X_test.shape[0], 784) #images are 28x28, which is 784"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a multi layer perceptron model \n",
    "- 3 layers\n",
    "- 10 input nodes, 10 hidden nodes, 10 output nodes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 10)                7850      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                110       \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 10)                110       \n",
      "=================================================================\n",
      "Total params: 8,070\n",
      "Trainable params: 8,070\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "numIn = 10\n",
    "numHid = 10\n",
    "numOut = 10\n",
    "alpha = .01\n",
    "model = getModel(numIn, numHid, numOut, alpha)\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the DNN model\n",
    "- split a validation set\n",
    "- https://machinelearningmastery.com/difference-test-validation-datasets/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 54000 samples, validate on 6000 samples\n",
      "Epoch 1/10\n",
      "54000/54000 [==============================] - 0s 9us/step - loss: 0.0025 - acc: 0.8027 - val_loss: 0.0012 - val_acc: 0.9103\n",
      "Epoch 2/10\n",
      "54000/54000 [==============================] - 0s 6us/step - loss: 0.0014 - acc: 0.8975 - val_loss: 0.0011 - val_acc: 0.9223\n",
      "Epoch 3/10\n",
      "54000/54000 [==============================] - 0s 6us/step - loss: 0.0013 - acc: 0.9064 - val_loss: 0.0011 - val_acc: 0.9202\n",
      "Epoch 4/10\n",
      "54000/54000 [==============================] - 0s 6us/step - loss: 0.0012 - acc: 0.9113 - val_loss: 0.0011 - val_acc: 0.9213\n",
      "Epoch 5/10\n",
      "54000/54000 [==============================] - 0s 6us/step - loss: 0.0011 - acc: 0.9147 - val_loss: 9.4975e-04 - val_acc: 0.9310\n",
      "Epoch 6/10\n",
      "54000/54000 [==============================] - 0s 6us/step - loss: 0.0011 - acc: 0.9184 - val_loss: 9.5172e-04 - val_acc: 0.9293\n",
      "Epoch 7/10\n",
      "54000/54000 [==============================] - 0s 6us/step - loss: 0.0011 - acc: 0.9202 - val_loss: 9.1767e-04 - val_acc: 0.9333\n",
      "Epoch 8/10\n",
      "54000/54000 [==============================] - 0s 6us/step - loss: 0.0011 - acc: 0.9213 - val_loss: 0.0010 - val_acc: 0.9218\n",
      "Epoch 9/10\n",
      "54000/54000 [==============================] - 0s 6us/step - loss: 0.0011 - acc: 0.9222 - val_loss: 8.8856e-04 - val_acc: 0.9353\n",
      "Epoch 10/10\n",
      "54000/54000 [==============================] - 0s 6us/step - loss: 0.0010 - acc: 0.9246 - val_loss: 9.3334e-04 - val_acc: 0.9283\n"
     ]
    }
   ],
   "source": [
    "hist = model.fit(X_train, y_train, validation_split=.1, epochs=10, batch_size=200, 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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f27fc0382e8>"
      ]
     },
     "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=\"train_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 0x7f27fc063be0>"
      ]
     },
     "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=\"train_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": [
    "### What about a different network?\n",
    "- what about changing epochs, batch size, validation split, or alpha?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_4 (Dense)              (None, 28)                21980     \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 14)                406       \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 10)                150       \n",
      "=================================================================\n",
      "Total params: 22,536\n",
      "Trainable params: 22,536\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "Train on 54000 samples, validate on 6000 samples\n",
      "Epoch 1/10\n",
      "54000/54000 [==============================] - 1s 10us/step - loss: 0.0015 - acc: 0.8858 - val_loss: 6.3435e-04 - val_acc: 0.9548\n",
      "Epoch 2/10\n",
      "54000/54000 [==============================] - 0s 7us/step - loss: 7.1180e-04 - acc: 0.9453 - val_loss: 5.8330e-04 - val_acc: 0.9592\n",
      "Epoch 3/10\n",
      "54000/54000 [==============================] - 0s 7us/step - loss: 6.0267e-04 - acc: 0.9539 - val_loss: 5.4602e-04 - val_acc: 0.9615\n",
      "Epoch 4/10\n",
      "54000/54000 [==============================] - 0s 7us/step - loss: 5.3463e-04 - acc: 0.9582 - val_loss: 5.3748e-04 - val_acc: 0.9612\n",
      "Epoch 5/10\n",
      "54000/54000 [==============================] - 0s 7us/step - loss: 4.8342e-04 - acc: 0.9620 - val_loss: 5.0376e-04 - val_acc: 0.9640\n",
      "Epoch 6/10\n",
      "54000/54000 [==============================] - 0s 7us/step - loss: 4.3907e-04 - acc: 0.9655 - val_loss: 4.8788e-04 - val_acc: 0.9632\n",
      "Epoch 7/10\n",
      "54000/54000 [==============================] - 0s 7us/step - loss: 4.0999e-04 - acc: 0.9680 - val_loss: 5.0462e-04 - val_acc: 0.9638\n",
      "Epoch 8/10\n",
      "54000/54000 [==============================] - 0s 7us/step - loss: 4.0104e-04 - acc: 0.9682 - val_loss: 4.9611e-04 - val_acc: 0.9642\n",
      "Epoch 9/10\n",
      "54000/54000 [==============================] - 0s 7us/step - loss: 3.8088e-04 - acc: 0.9701 - val_loss: 5.0340e-04 - val_acc: 0.9620\n",
      "Epoch 10/10\n",
      "54000/54000 [==============================] - 0s 7us/step - loss: 3.7434e-04 - acc: 0.9708 - val_loss: 4.6727e-04 - val_acc: 0.9657\n"
     ]
    }
   ],
   "source": [
    "l1 = 28 #nodes in first layer\n",
    "h1 = 14 #nodes in hidden layer\n",
    "out = 10 #still 10 in output layer since there are 10 classes\n",
    "alpha = .01\n",
    "model2 = getModel(l1, h1, out, alpha)\n",
    "print(model2.summary())\n",
    "hist2 = model2.fit(X_train, y_train, validation_split=.1, epochs=10, batch_size=200, verbose=1, shuffle=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f27b06f8cc0>"
      ]
     },
     "execution_count": 13,
     "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(hist2.history['loss'], label=\"train_loss\")\n",
    "plt.plot(hist2.history['val_loss'], label=\"val_loss\")\n",
    "plt.title(\"model loss summary for l1=28 h1=14\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f27fbf8d9e8>"
      ]
     },
     "execution_count": 14,
     "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(hist2.history['acc'], label=\"train_accuracy\")\n",
    "plt.plot(hist2.history['val_acc'], label=\"val_accuracy\")\n",
    "plt.title(\"model accuracy summary for l1=28 h1=14\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate the models\n",
    "- score is a measure of loss, accuracy is a measure of performance (see above link)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first model score: 2.28, accuracy: 0.86\n",
      "second model score: 0.69, accuracy: 0.96\n"
     ]
    }
   ],
   "source": [
    "score1 = model.evaluate(X_test, y_test, verbose=0)\n",
    "score2 = model2.evaluate(X_test, y_test, verbose=0)\n",
    "print(\"first model score: {:.2f}, accuracy: {:.2f}\".format(score1[0], score1[1]))\n",
    "print(\"second model score: {:.2f}, accuracy: {:.2f}\".format(score2[0], score2[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test the models with a random image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f277a3eb9e8>"
      ]
     },
     "execution_count": 16,
     "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://www.researchgate.net/profile/Jose_Sempere/publication/221258631/figure/fig1/AS:305526891139075@1449854695342/Handwritten-digit-2.png'\n",
    "res = requests.get(url, stream=True)\n",
    "img = Image.open(res.raw)\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f27fbff6ba8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "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.imshow(gray)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "gray = gray/255\n",
    "gray = gray.reshape(1, 784)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make a final prediction using both models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first model score: 2.28, accuracy: 0.86\n",
      "first model prediction:  [2]\n",
      "second model score: 0.69, accuracy: 0.96\n",
      "second model prediction:  [3]\n"
     ]
    }
   ],
   "source": [
    "pred = model.predict_classes(gray)\n",
    "pred2 = model2.predict_classes(gray)\n",
    "print(\"first model score: {:.2f}, accuracy: {:.2f}\".format(score1[0], score1[1]))\n",
    "print(\"first model prediction: \", str(pred))\n",
    "print(\"second model score: {:.2f}, accuracy: {:.2f}\".format(score2[0], score2[1]))\n",
    "print(\"second model prediction: \", str(pred2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This is why Neural Networks are so difficult to work with!\n",
    "## Recapping their scores, it looks like model2 shold have made the correct prediction!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This is why we use CNNs..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- https://machinelearningmastery.com/metrics-evaluate-machine-learning-algorithms-python/"
   ]
  }
 ],
 "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
}