MNIST using CNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.datasets import mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train,y_train),(x_test,y_test)=mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2 as cv\n",
    "import numpy as np\n",
    "for i in range(0,6):\n",
    "    numeric=np.random.randint(0,len(x_train))\n",
    "    img=x_train[numeric]\n",
    "    window = \"Randome Sample #\" + str(i)\n",
    "    cv.imshow(window,img)\n",
    "    cv.waitKey(0)\n",
    "cv.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_rows=x_train[0].shape[0]\n",
    "img_cols=x_train[1].shape[0]\n",
    "\n",
    "x_train = x_train.reshape(x_train.shape[0],img_rows,img_cols,1)\n",
    "x_test = x_test.reshape(x_test.shape[0],img_rows,img_cols,1)\n",
    "\n",
    "input_shape = (img_rows,img_cols,1)\n",
    "\n",
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.astype('float32')\n",
    "\n",
    "x_train/=255\n",
    "x_test/=255\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## we need to Hot encode our label data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of classes: 10\n"
     ]
    }
   ],
   "source": [
    "from keras.utils import np_utils\n",
    "\n",
    "y_train=np_utils.to_categorical(y_train)\n",
    "y_test=np_utils.to_categorical(y_test)\n",
    "\n",
    "\n",
    "print(\"Number of classes: \"+str(y_test.shape[1]))\n",
    "\n",
    "num_classes=y_test.shape[1]\n",
    "num_pixels=x_train.shape[1]*x_train.shape[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D,Dense,MaxPooling2D,Dropout,Flatten\n",
    "from keras.optimizers import SGD\n",
    "from keras import backend as K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=Sequential()\n",
    "model.add(Conv2D(32,kernel_size=(3,3),input_shape=input_shape,activation='relu'))\n",
    "model.add(Conv2D(64,(3,3),activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(128,activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(num_classes,activation='softmax'))\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "             optimizer = SGD(0.01),\n",
    "             metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 26, 26, 32)        320       \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 24, 24, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 12, 12, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 12, 12, 64)        0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 9216)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 128)               1179776   \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 1,199,882\n",
      "Trainable params: 1,199,882\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Our Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1875/1875 [==============================] - 237s 126ms/step - loss: 0.9573 - accuracy: 0.6905 - val_loss: 0.1919 - val_accuracy: 0.9429\n",
      "Epoch 2/10\n",
      "1875/1875 [==============================] - 223s 119ms/step - loss: 0.3113 - accuracy: 0.9049 - val_loss: 0.1420 - val_accuracy: 0.9562\n",
      "Epoch 3/10\n",
      "1875/1875 [==============================] - 222s 118ms/step - loss: 0.2347 - accuracy: 0.9292 - val_loss: 0.1103 - val_accuracy: 0.9683\n",
      "Epoch 4/10\n",
      "1875/1875 [==============================] - 196s 104ms/step - loss: 0.1899 - accuracy: 0.9433 - val_loss: 0.0884 - val_accuracy: 0.9723\n",
      "Epoch 5/10\n",
      "1875/1875 [==============================] - 199s 106ms/step - loss: 0.1539 - accuracy: 0.9534 - val_loss: 0.0715 - val_accuracy: 0.9789\n",
      "Epoch 6/10\n",
      "1875/1875 [==============================] - 216s 115ms/step - loss: 0.1251 - accuracy: 0.9627 - val_loss: 0.0608 - val_accuracy: 0.9805\n",
      "Epoch 7/10\n",
      "1875/1875 [==============================] - 198s 106ms/step - loss: 0.1114 - accuracy: 0.9664 - val_loss: 0.0537 - val_accuracy: 0.9829\n",
      "Epoch 8/10\n",
      "1875/1875 [==============================] - 198s 106ms/step - loss: 0.0978 - accuracy: 0.9699 - val_loss: 0.0489 - val_accuracy: 0.9842\n",
      "Epoch 9/10\n",
      "1875/1875 [==============================] - 199s 106ms/step - loss: 0.0919 - accuracy: 0.9724 - val_loss: 0.0473 - val_accuracy: 0.9844\n",
      "Epoch 10/10\n",
      "1875/1875 [==============================] - 199s 106ms/step - loss: 0.0857 - accuracy: 0.9740 - val_loss: 0.0439 - val_accuracy: 0.9857\n",
      "Test loss: 0.043942976742982864\n",
      "Test accuracy: 0.9857000112533569\n"
     ]
    }
   ],
   "source": [
    "batch_size = 32\n",
    "epochs = 10\n",
    "\n",
    "history = model.fit(x_train,\n",
    "                    y_train,\n",
    "                    batch_size = batch_size,\n",
    "                    epochs = epochs,\n",
    "                    verbose = 1,\n",
    "                    validation_data = (x_test, y_test))\n",
    "\n",
    "score = model.evaluate(x_test, y_test, verbose=0)\n",
    "print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ploting our Loss and Accuracy Charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Plotting our loss charts\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "history_dict = history.history\n",
    "\n",
    "loss_values = history_dict['loss']\n",
    "val_loss_values = history_dict['val_loss']\n",
    "epochs = range(1, len(loss_values) + 1)\n",
    "\n",
    "line1 = plt.plot(epochs, val_loss_values, label='Validation/Test Loss')\n",
    "line2 = plt.plot(epochs, loss_values, label='Training Loss')\n",
    "plt.setp(line1, linewidth=2.0, marker = '+', markersize=10.0)\n",
    "plt.setp(line2, linewidth=2.0, marker = '4', markersize=10.0)\n",
    "plt.xlabel('Epochs') \n",
    "plt.ylabel('Loss')\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Plotting our accuracy charts\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "history_dict = history.history\n",
    "\n",
    "acc_values = history_dict['accuracy']\n",
    "val_acc_values = history_dict['val_accuracy']\n",
    "epochs = range(1, len(loss_values) + 1)\n",
    "\n",
    "line1 = plt.plot(epochs, val_acc_values, label='Validation/Test Accuracy')\n",
    "line2 = plt.plot(epochs, acc_values, label='Training Accuracy')\n",
    "plt.setp(line1, linewidth=2.0, marker = '+', markersize=10.0)\n",
    "plt.setp(line2, linewidth=2.0, marker = '4', markersize=10.0)\n",
    "plt.xlabel('Epochs') \n",
    "plt.ylabel('Accuracy')\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving our Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Saved\n"
     ]
    }
   ],
   "source": [
    "model.save(\"mnist_simple_cnn_10_Epochs.h5\")\n",
    "print(\"Model Saved\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading  our Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import load_model\n",
    "classifier = load_model('mnist_simple_cnn_10_Epochs.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lets input some of our test data into our classifer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "def draw_test(name, pred, input_im):\n",
    "    BLACK = [0,0,0]\n",
    "    expanded_image = cv2.copyMakeBorder(input_im, 0, 0, 0, imageL.shape[0] ,cv2.BORDER_CONSTANT,value=BLACK)\n",
    "    expanded_image = cv2.cvtColor(expanded_image, cv2.COLOR_GRAY2BGR)\n",
    "    cv2.putText(expanded_image, str(pred), (152, 70) , cv2.FONT_HERSHEY_COMPLEX_SMALL,4, (0,255,0), 2)\n",
    "    cv2.imshow(name, expanded_image)\n",
    "\n",
    "\n",
    "for i in range(0,10):\n",
    "    rand = np.random.randint(0,len(x_test))\n",
    "    input_im = x_test[rand]\n",
    "\n",
    "    imageL = cv2.resize(input_im, None, fx=4, fy=4, interpolation = cv2.INTER_CUBIC)\n",
    "    input_im = input_im.reshape(1,28,28,1) \n",
    "    \n",
    "    ## Get Prediction\n",
    "    res = str(np.argmax(classifier.predict(input_im, 1, verbose = 0)[0],axis=-1))\n",
    "\n",
    "    draw_test(\"Prediction\", res, imageL) \n",
    "    cv2.waitKey(0)\n",
    "\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating the diagram of the model architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Failed to import pydot. You must `pip install pydot` and install graphviz (https://graphviz.gitlab.io/download/), ', 'for `pydotprint` to work.')\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.image as mpimg\n",
    "from keras.utils.vis_utils import plot_model\n",
    "# Save our model diagrams to this path\n",
    "\n",
    "model_diagrams_path = 'Downloads/DeeplearningCV/Trained Models/'\n",
    "\n",
    "# Generate the plot\n",
    "plot_model(model, to_file = model_diagrams_path + 'model_plot.png',\n",
    "           show_shapes = True,\n",
    "           show_layer_names = True)\n",
    "\n",
    "# Show the plot here\n",
    "img = mpimg.imread(model_diagrams_path + 'model_plot.png')\n",
    "plt.figure(figsize=(30,15))\n",
    "imgplot = plt.imshow(img) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert keras model to tflite model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From c:\\users\\shekh\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\tensorflow\\python\\training\\tracking\\tracking.py:109: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
      "WARNING:tensorflow:From c:\\users\\shekh\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\tensorflow\\python\\training\\tracking\\tracking.py:109: Layer.updates (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\shekh\\AppData\\Local\\Temp\\tmpu4i2z7hi\\assets\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "keras_model = tf.keras.models.load_model('mnist_simple_cnn_10_Epochs.h5')\n",
    "converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)\n",
    "tflite_model = converter.convert()\n",
    "open(\"converted_mnist_model.tflite\", \"wb\").write(tflite_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Validate Converted model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'tensorflow' has no attribute 'contrib'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-919007d3954f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtensorflow\u001b[0m  \u001b[1;32mas\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;31m# Load TFLite model and allocate tensors.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0minterpreter\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcontrib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlite\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mInterpreter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel_path\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"converted_model.tflite\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0minterpreter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mallocate_tensors\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;31m# Get input and output tensors\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: module 'tensorflow' has no attribute 'contrib'"
     ]
    }
   ],
   "source": [
    "import tensorflow  as tf\n",
    "# Load TFLite model and allocate tensors.\n",
    "interpreter = tf.contrib.lite.Interpreter(model_path=\"converted_model.tflite\") \n",
    "interpreter.allocate_tensors()\n",
    "# Get input and output tensors\n",
    "input_details = interpreter.get_input_details() \n",
    "output_details = interpreter.get_output_details()\n",
    "# Test model on random input data\n",
    "input_shape = input_details[0]['shape']\n",
    "input_data = np.array(np.random.random_sample(input_shape), dtype=np.float32) \n",
    "interpreter.set_tensor(input_details[0]['index'], input_data)\n",
    "interpreter.invoke()\n",
    "output_data = interpreter.get_tensor(output_details[0]['index']) \n",
    "print(output_data)"
   ]
  },
  {
   "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}