cifar-10-keras.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3"},"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"},"colab":{"name":"cifar-10-keras.ipynb","provenance":[{"file_id":"1coZrayTKL5mHdarcHS_K739RmfcmL9je","timestamp":1578462566249}],"collapsed_sections":[]},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","metadata":{"id":"GkXTxvOIxlww","colab_type":"text"},"source":["# Handle the CIFAR-10 dataset"]},{"cell_type":"code","metadata":{"id":"KotVUGfYxlw6","colab_type":"code","outputId":"130f3860-f7c7-4e82-8bd0-8821b4d90f1c","executionInfo":{"status":"ok","timestamp":1578545715288,"user_tz":-330,"elapsed":87327,"user":{"displayName":"swarna kamal Paul","photoUrl":"","userId":"16172308933059963349"}},"colab":{"base_uri":"https://localhost:8080/","height":122}},"source":["from google.colab import drive\n","drive.mount('/content/drive')\n","import sys\n","sys.path.append('/content/drive/My Drive/')\n","%tensorflow_version 1.x\n","import tensorflow as tf\n","%matplotlib inline\n","import pickle\n","import numpy as np\n","from os import listdir\n","from os.path import isfile, join\n","import os\n","from sklearn.preprocessing import StandardScaler\n","\n","# Load the TensorBoard notebook extension\n","%load_ext tensorboard"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n","\n","Enter your authorization code:\n","··········\n","Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"_1FyTXtnxlxA","colab_type":"text"},"source":["## Create a function that will extract the data from the dataset\n","You can find the dataset here: https://www.cs.utoronto.ca/~kriz/cifar-10-python.tar.gz\n","\n","When you download the dataset you will get a bunch of pickled files <br>\n","Inside `cifar-10-batches-py`folder you will find <br>\n","1. data_batch_x files <br>\n","2. test_batch \n","\n","The data_batch_x files are the batched training data splited in x parts (in our case in 5 parts of 10000 samples each). The test_batch file contains 10000 samples for testing.\n","\n","### The structure\n","All files has the same structure: <br>\n","1. data -- a 10000x3072 numpy array of uint8s. Each row of the array stores a 32x32 colour image. The first 1024 entries contain the red channel values, the next 1024 the green, and the final 1024 the blue. The image is stored in row-major order, so that the first 32 entries of the array are the red channel values of the first row of the image.\n","2. labels -- a list of 10000 numbers in the range 0-9. The number at index i indicates the label of the ith image in the array data.\n","\n","There is also another file called batches.meta where it describes the classes"]},{"cell_type":"code","metadata":{"id":"2BPEfCVey0cM","colab_type":"code","colab":{}},"source":["############### Download CIFAR-10 data and save it to google drive\n","import requests  \n","file_url = \"https://www.cs.utoronto.ca/~kriz/cifar-10-python.tar.gz\"\n","    \n","r = requests.get(file_url, stream = True)  \n","  \n","with open(\"/content/drive/My Drive/DeepNN_HandsOn/cifar-10-python.tar.gz\", \"wb\") as file:  \n","    for block in r.iter_content(chunk_size = 1024): \n","         if block:  \n","             file.write(block)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"rzR_1A4T0SKl","colab_type":"code","colab":{}},"source":["### unzip the file\n","!gunzip \"/content/drive/My Drive/DeepNN_HandsOn/cifar-10-python.tar.gz\""],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"46ov4UZh1V8X","colab_type":"code","outputId":"e26bd8fa-07d5-485b-bcfe-07d1fd732787","executionInfo":{"status":"ok","timestamp":1578481820458,"user_tz":-330,"elapsed":8328,"user":{"displayName":"swarna kamal Paul","photoUrl":"","userId":"16172308933059963349"}},"colab":{"base_uri":"https://localhost:8080/","height":170}},"source":["##### Untar the file\n","!tar -xvf \"/content/drive/My Drive/DeepNN_HandsOn/cifar-10-python.tar\""],"execution_count":0,"outputs":[{"output_type":"stream","text":["cifar-10-batches-py/\n","cifar-10-batches-py/data_batch_4\n","cifar-10-batches-py/readme.html\n","cifar-10-batches-py/test_batch\n","cifar-10-batches-py/data_batch_3\n","cifar-10-batches-py/batches.meta\n","cifar-10-batches-py/data_batch_2\n","cifar-10-batches-py/data_batch_5\n","cifar-10-batches-py/data_batch_1\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"QCs2rpm3xlxC","colab_type":"code","colab":{}},"source":["# Function to unpickle the dataset\n","def unpickle_all_data(directory):\n","    \n","    # Initialize the variables\n","    train = dict()\n","    test = dict()\n","    train_x = []\n","    train_y = []\n","    test_x = []\n","    test_y = []\n","    \n","    # Iterate through all files that we want, train and test\n","    # Train is separated into batches\n","    for filename in listdir(directory):\n","        if isfile(join(directory, filename)):\n","            \n","            # The train data\n","            if 'data_batch' in filename:\n","                print('Handing file: %s' % filename)\n","                \n","                # Opent the file\n","                with open(directory + '/' + filename, 'rb') as fo:\n","                    data = pickle.load(fo, encoding='bytes')\n","\n","                if 'data' not in train:\n","                    train['data'] = data[b'data']\n","                    train['labels'] = np.array(data[b'labels'])\n","                else:\n","                    train['data'] = np.concatenate((train['data'], data[b'data']))\n","                    train['labels'] = np.concatenate((train['labels'], data[b'labels']))\n","            # The test data\n","            elif 'test_batch' in filename:\n","                print('Handing file: %s' % filename)\n","                \n","                # Open the file\n","                with open(directory + '/' + filename, 'rb') as fo:\n","                    data = pickle.load(fo, encoding='bytes')\n","                \n","                test['data'] = data[b'data']\n","                test['labels'] = data[b'labels']\n","    \n","    # Manipulate the data to the proper format\n","    for image in train['data']:\n","        train_x.append(np.transpose(np.reshape(image,(3, 32,32)), (1,2,0)))\n","    train_y = [label for label in train['labels']]\n","    \n","    for image in test['data']:\n","        test_x.append(np.transpose(np.reshape(image,(3, 32,32)), (1,2,0)))\n","    test_y = [label for label in test['labels']]\n","    \n","    # Transform the data to np array format\n","    train_x = np.array(train_x)\n","    train_y = np.array(train_y)\n","    test_x = np.array(test_x)\n","    test_y = np.array(test_y)\n","    \n","    return (train_x, train_y), (test_x, test_y)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"8teR2EhlxlxF","colab_type":"text"},"source":["## Using the function\n","\n","Returns four variables\n","1. x_train := A 3D numpy array with the images in a format (32, 32, 3) a 32x32 image with 3 dimmesions for colors RGB\n","2. y_train := A 1D numpy with the numbered labels for each sample stom x_train\n","3. x_test := same as x_train\n","4. y_test := same as y_train"]},{"cell_type":"code","metadata":{"scrolled":true,"id":"NPaBdiOqxlxH","colab_type":"code","outputId":"b0b4a55e-4c12-4edb-e791-0f6c514ff8a1","executionInfo":{"status":"ok","timestamp":1578481833343,"user_tz":-330,"elapsed":2125,"user":{"displayName":"swarna kamal Paul","photoUrl":"","userId":"16172308933059963349"}},"colab":{"base_uri":"https://localhost:8080/","height":119}},"source":["(x_train, y_train), (x_test, y_test) = unpickle_all_data(os.getcwd() + '/cifar-10-batches-py/')"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Handing file: data_batch_3\n","Handing file: data_batch_1\n","Handing file: data_batch_5\n","Handing file: test_batch\n","Handing file: data_batch_2\n","Handing file: data_batch_4\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"iBmSTrm5xlxK","colab_type":"code","outputId":"706881f6-f396-488e-f89d-8938c229dc94","executionInfo":{"status":"ok","timestamp":1578481905898,"user_tz":-330,"elapsed":1176,"user":{"displayName":"swarna kamal Paul","photoUrl":"","userId":"16172308933059963349"}},"colab":{"base_uri":"https://localhost:8080/","height":351}},"source":["import matplotlib.pyplot as plt\n","print(x_train.shape)\n","print(y_train.shape)\n","print(x_test.shape)\n","print(y_test.shape)\n","######### display image\n","plt.imshow(x_train[3])\n","print(y_train[3])"],"execution_count":0,"outputs":[{"output_type":"stream","text":["(50000, 32, 32, 3)\n","(50000,)\n","(10000, 32, 32, 3)\n","(10000,)\n","6\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAPsAAAD5CAYAAADhukOtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"BwASt1vExlxO","colab_type":"text"},"source":["## Preprocess the data\n","1. The data must be in a float32 represenation\n","2. The data should be normalized, here I chose [0,1] normalization\n","3. The labels are one-hot encoded"]},{"cell_type":"code","metadata":{"id":"VuajbwX4xlxO","colab_type":"code","outputId":"0073825f-3dab-45b1-9efb-649ab1ebee3c","executionInfo":{"status":"ok","timestamp":1578481916242,"user_tz":-330,"elapsed":2425,"user":{"displayName":"swarna kamal Paul","photoUrl":"","userId":"16172308933059963349"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["from keras.utils import np_utils\n","\n","# Transofrm them to a float32 type\n","x_train = x_train.astype('float32')\n","x_test = x_test.astype('float32')\n","\n","# Normalize the input \n","train_mean = np.mean(x_train,axis=(0,1,2,3))\n","train_std = np.std(x_train,axis=(0,1,2,3))\n","test_mean = np.mean(x_test,axis=(0,1,2,3))\n","test_std = np.std(x_test,axis=(0,1,2,3))\n","x_train = (x_train-train_mean)/(train_std+1e-7)\n","x_test = (x_test-test_mean)/(test_std+1e-7)\n","#x_train /= 255\n","#x_test /= 255\n","\n","# One-hot Encoding\n","num_classes = 10\n","y_train = np_utils.to_categorical(y_train, num_classes)\n","y_test = np_utils.to_categorical(y_test, num_classes)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Using TensorFlow backend.\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"PnT57-UvDpUy","colab_type":"code","outputId":"009054fc-1821-4249-8cfb-a67eba1ba10a","executionInfo":{"status":"ok","timestamp":1578481943315,"user_tz":-330,"elapsed":1160,"user":{"displayName":"swarna kamal Paul","photoUrl":"","userId":"16172308933059963349"}},"colab":{"base_uri":"https://localhost:8080/","height":300}},"source":["######### display image\n","plt.imshow(x_train[3])\n","#print(x_train[3])"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"],"name":"stderr"},{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f957f2ee438>"]},"metadata":{"tags":[]},"execution_count":11},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAPsAAAD5CAYAAADhukOtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"fOLfKy1nxlxc","colab_type":"text"},"source":["## Building the model\n","1. The input data for the model is each image sample 32x32x3\n","2. Convolve the image into 3x3 squares with padding same (https://stackoverflow.com/questions/37674306/what-is-the-difference-between-same-and-valid-padding-in-tf-nn-max-pool-of-t) and Pooling with 2x2. Add a dropout of 0.2 to avoid early overfitting\n","3. Same situation for the next two layers for 64 input size, and 128 input size\n","4. Finally, add a Dense(fully connected) layer for the output\n","\n","You can try different layouts to improve the accuracy"]},{"cell_type":"code","metadata":{"id":"6Q-SEJkhxlxe","colab_type":"code","outputId":"0276004f-692e-4ade-b430-8fad8042974d","executionInfo":{"status":"ok","timestamp":1578481985882,"user_tz":-330,"elapsed":1187,"user":{"displayName":"swarna kamal Paul","photoUrl":"","userId":"16172308933059963349"}},"colab":{"base_uri":"https://localhost:8080/","height":258}},"source":["import keras\n","from keras.models import Sequential\n","from keras.utils import np_utils\n","from keras.preprocessing.image import ImageDataGenerator\n","from keras.layers import Dense, Activation, Flatten, Dropout, BatchNormalization\n","from keras.layers import Conv2D, MaxPooling2D\n","from keras import regularizers\n","from keras.callbacks import LearningRateScheduler\n","import numpy as np\n","\n","# Create the model\n","model = Sequential()\n","model.add(Conv2D(32, (3, 3), padding='same',\n","                 input_shape=x_train.shape[1:]))\n","model.add(Activation('relu'))\n","model.add(Conv2D(32, (3, 3)))\n","model.add(Activation('relu'))\n","model.add(MaxPooling2D(pool_size=(2, 2)))\n","model.add(Dropout(0.25))\n","\n","model.add(Conv2D(64, (3, 3), padding='same'))\n","model.add(Activation('relu'))\n","model.add(Conv2D(64, (3, 3)))\n","model.add(Activation('relu'))\n","model.add(MaxPooling2D(pool_size=(2, 2)))\n","model.add(Dropout(0.25))\n","\n","model.add(Flatten())\n","model.add(Dense(512))\n","model.add(Activation('relu'))\n","model.add(Dropout(0.5))\n","model.add(Dense(num_classes))\n","model.add(Activation('softmax'))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4267: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:148: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3733: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"R8Y-uqdWxlxi","colab_type":"text"},"source":["## Augment the data\n","From one sample, extract multiple versions\n","\n","For example, rotate the image, shift, flip etc"]},{"cell_type":"code","metadata":{"id":"umjiY-MUxlxj","colab_type":"code","colab":{}},"source":["# data augmentation\n","datagen = ImageDataGenerator(\n","    featurewise_center=False,  # set input mean to 0 over the dataset\n","    samplewise_center=False,  # set each sample mean to 0\n","    featurewise_std_normalization=False,  # divide inputs by std of the dataset\n","    samplewise_std_normalization=False,  # divide each input by its std\n","    zca_whitening=False,  # apply ZCA whitening\n","    rotation_range=0,  # randomly rotate images in the range (degrees, 0 to 180)\n","    width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)\n","    height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)\n","    horizontal_flip=True,  # randomly flip images\n","    vertical_flip=False)  # randomly flip images\n","\n","# Compute quantities required for feature-wise normalization\n","# (std, mean, and principal components if ZCA whitening is applied).\n","datagen.fit(x_train)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"colab_type":"code","outputId":"6eca8391-e9bf-4c34-bf1e-4b9e7309dd0e","executionInfo":{"status":"ok","timestamp":1578482153906,"user_tz":-330,"elapsed":1167,"user":{"displayName":"swarna kamal Paul","photoUrl":"","userId":"16172308933059963349"}},"id":"IIid0pRVHlFU","colab":{"base_uri":"https://localhost:8080/","height":317}},"source":["#view images\n","gen = datagen.flow(x_train, y_train, batch_size=64)\n","x,y = gen.next()\n","image = x[0].astype('uint')\n","print(y[0])\n","plt.imshow(image)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"],"name":"stderr"},{"output_type":"stream","text":["[0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f957ed09710>"]},"metadata":{"tags":[]},"execution_count":23},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAPsAAAD5CAYAAADhukOtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKm+QAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"EsX2u30Cxlxm","colab_type":"text"},"source":["## Compiling the model"]},{"cell_type":"code","metadata":{"id":"SiOYH81Rxlxn","colab_type":"code","outputId":"bf142098-d23e-40fd-e5ef-3a662668aa41","executionInfo":{"status":"ok","timestamp":1578482178702,"user_tz":-330,"elapsed":2939,"user":{"displayName":"swarna kamal Paul","photoUrl":"","userId":"16172308933059963349"}},"colab":{"base_uri":"https://localhost:8080/","height":105}},"source":["# Compile the model\n","import datetime\n","from keras.callbacks import TensorBoard\n","batch_size = 64\n","\n","opt_rms = keras.optimizers.rmsprop(lr=0.001, decay=1e-6)\n","model.compile(loss='categorical_crossentropy', \n","              optimizer=opt_rms, \n","              metrics=['accuracy'])\n","logdir = os.path.join(\"/content/drive/My Drive/DeepNN_HandsOn/logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n","tensorboard_callback = TensorBoard(logdir, histogram_freq=1)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3576: The name tf.log is deprecated. Please use tf.math.log instead.\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"98ERbYXcw8Zo","colab_type":"code","outputId":"8ed881ee-b382-4a7f-8127-2ace9417a7c8","executionInfo":{"status":"ok","timestamp":1578483058499,"user_tz":-330,"elapsed":1129,"user":{"displayName":"swarna kamal Paul","photoUrl":"","userId":"16172308933059963349"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["############## initiate tensorboard\n","#!kill 443\n","%tensorboard --logdir \"/content/drive/My Drive/DeepNN_HandsOn/logs\""],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"text/plain":["Reusing TensorBoard on port 6006 (pid 475), started 0:13:09 ago. (Use '!kill 475' to kill it.)"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/html":["\n","    <div id=\"root\"></div>\n","    <script>\n","      (function() {\n","        window.TENSORBOARD_ENV = window.TENSORBOARD_ENV || {};\n","        window.TENSORBOARD_ENV[\"IN_COLAB\"] = true;\n","        document.querySelector(\"base\").href = \"https://localhost:6006\";\n","        function fixUpTensorboard(root) {\n","          const tftb = root.querySelector(\"tf-tensorboard\");\n","          // Disable the fragment manipulation behavior in Colab. Not\n","          // only is the behavior not useful (as the iframe's location\n","          // is not visible to the user), it causes TensorBoard's usage\n","          // of `window.replace` to navigate away from the page and to\n","          // the `localhost:<port>` URL specified by the base URI, which\n","          // in turn causes the frame to (likely) crash.\n","          tftb.removeAttribute(\"use-hash\");\n","        }\n","        function executeAllScripts(root) {\n","          // When `script` elements are inserted into the DOM by\n","          // assigning to an element's `innerHTML`, the scripts are not\n","          // executed. Thus, we manually re-insert these scripts so that\n","          // TensorBoard can initialize itself.\n","          for (const script of root.querySelectorAll(\"script\")) {\n","            const newScript = document.createElement(\"script\");\n","            newScript.type = script.type;\n","            newScript.textContent = script.textContent;\n","            root.appendChild(newScript);\n","            script.remove();\n","          }\n","        }\n","        function setHeight(root, height) {\n","          // We set the height dynamically after the TensorBoard UI has\n","          // been initialized. This avoids an intermediate state in\n","          // which the container plus the UI become taller than the\n","          // final width and cause the Colab output frame to be\n","          // permanently resized, eventually leading to an empty\n","          // vertical gap below the TensorBoard UI. It's not clear\n","          // exactly what causes this problematic intermediate state,\n","          // but setting the height late seems to fix it.\n","          root.style.height = `${height}px`;\n","        }\n","        const root = document.getElementById(\"root\");\n","        fetch(\".\")\n","          .then((x) => x.text())\n","          .then((html) => void (root.innerHTML = html))\n","          .then(() => fixUpTensorboard(root))\n","          .then(() => executeAllScripts(root))\n","          .then(() => setHeight(root, 800));\n","      })();\n","    </script>\n","  "],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"c2l2ZsBPxlxr","colab_type":"text"},"source":["## Training the algorithm\n","I would suggest more than 100 epochs"]},{"cell_type":"code","metadata":{"id":"xwDle_bqxlxs","colab_type":"code","outputId":"1e268906-e5c6-4e79-ca0d-ebb3aabd427a","executionInfo":{"status":"ok","timestamp":1578482877233,"user_tz":-330,"elapsed":538426,"user":{"displayName":"swarna kamal Paul","photoUrl":"","userId":"16172308933059963349"}},"colab":{"base_uri":"https://localhost:8080/","height":683}},"source":["epochs = 5\n","model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),\n","                    steps_per_epoch=x_train.shape[0] // batch_size,\n","                    epochs=epochs,\n","                    verbose=1,\n","                    validation_data=(x_test, y_test),\n","                    callbacks= [tensorboard_callback])#[TensorBoardColabCallback(tbc)])"],"execution_count":0,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Use tf.where in 2.0, which has the same broadcast rule as np.where\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3005: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/callbacks.py:1068: The name tf.summary.histogram is deprecated. Please use tf.compat.v1.summary.histogram instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/callbacks.py:1122: The name tf.summary.merge_all is deprecated. Please use tf.compat.v1.summary.merge_all instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/callbacks.py:1125: The name tf.summary.FileWriter is deprecated. Please use tf.compat.v1.summary.FileWriter instead.\n","\n","Epoch 1/5\n","781/781 [==============================] - 41s 52ms/step - loss: 1.5730 - acc: 0.4279 - val_loss: 1.1969 - val_acc: 0.5757\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/callbacks.py:1265: The name tf.Summary is deprecated. Please use tf.compat.v1.Summary instead.\n","\n","Epoch 2/5\n","781/781 [==============================] - 35s 45ms/step - loss: 1.2070 - acc: 0.5738 - val_loss: 0.9467 - val_acc: 0.6606\n","Epoch 3/5\n","781/781 [==============================] - 35s 45ms/step - loss: 1.0692 - acc: 0.6279 - val_loss: 0.8877 - val_acc: 0.6874\n","Epoch 4/5\n","781/781 [==============================] - 35s 45ms/step - loss: 1.0011 - acc: 0.6513 - val_loss: 0.9626 - val_acc: 0.6696\n","Epoch 5/5\n","781/781 [==============================] - 35s 45ms/step - loss: 0.9563 - acc: 0.6692 - val_loss: 0.8665 - val_acc: 0.7049\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["<keras.callbacks.History at 0x7f957856cc50>"]},"metadata":{"tags":[]},"execution_count":31}]},{"cell_type":"code","metadata":{"id":"a4EJGHsCxlxv","colab_type":"code","outputId":"9b7d5741-c724-4ec6-fd82-26fa23ccc3e3","executionInfo":{"status":"ok","timestamp":1578391132725,"user_tz":-330,"elapsed":787,"user":{"displayName":"swarna kamal Paul","photoUrl":"","userId":"16172308933059963349"}},"colab":{"base_uri":"https://localhost:8080/","height":68}},"source":["out= model.predict(x_train[0:4])\n","out[3]"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([1.0872292e-06, 1.0250151e-05, 1.9979252e-02, 1.2615286e-01,\n","       5.9632465e-02, 3.6577124e-02, 7.5343728e-01, 4.1993009e-03,\n","       3.2080740e-07, 1.0025548e-05], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":81}]}]}