{"cells":[{"metadata":{},"cell_type":"markdown","source":"# Introduction\n\n\nIn this version of my notebook, I have used the ensemble method using Sequential Convolutional Neural Network for digits recognition trained on MNIST dataset. I choosed to build it with keras API (Tensorflow backend) which is very intuitive. Firstly, I will prepare the data (handwritten digits images) then i will focus on the CNN modeling and evaluation.\n\nThis Notebook follows three main parts:\n* The data preparation\n* The CNN modeling and evaluation\n* The results prediction and submission"},{"metadata":{},"cell_type":"markdown","source":"![mnist.png](attachment:mnist.png)","attachments":{"mnist.png":{"image/png":"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"}}},{"metadata":{"_execution_state":"idle","_uuid":"72334cb006d02a4bcfc2a2fe622524eba824c6f8","_cell_guid":"f67b9393-8ea1-4e23-b856-2ce149cfe421","trusted":true},"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nimport seaborn as sns\n%matplotlib inline\n\nnp.random.seed(2)\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import confusion_matrix\nimport itertools\nimport tensorflow as tf\n\nfrom keras.utils.np_utils import to_categorical # convert to one-hot-encoding\nfrom keras.models import Sequential, load_model\nfrom keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D, BatchNormalization, ZeroPadding2D\nfrom keras.optimizers import RMSprop\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.callbacks import ReduceLROnPlateau, LearningRateScheduler\n\n\nsns.set(style='white', context='notebook', palette='deep')","execution_count":null,"outputs":[]},{"metadata":{"_execution_state":"idle","_uuid":"86061d98eccaa02efe0dab0fa3884e71fcf4c310","_cell_guid":"6d2fb3e6-ab71-4974-b5a2-4af1ebdb99f4"},"cell_type":"markdown","source":"# 2. Data preparation\n## 2.1 Load data"},{"metadata":{"_execution_state":"idle","_uuid":"84bbd5ab8d7895bd430d5ecfe2f7ddf77baa7b74","_cell_guid":"5e51d00e-62fd-4141-bf73-50ac4f2da7d0","trusted":true},"cell_type":"code","source":"# Load the data\ntrain = pd.read_csv(\"../input/train.csv\")\ntest = pd.read_csv(\"../input/test.csv\")","execution_count":null,"outputs":[]},{"metadata":{"_execution_state":"idle","_uuid":"1213b979d5ed3e0d13824d17d694c79d2ece92fa","_cell_guid":"86570a36-5c20-460a-9dfd-2070548532a7","trusted":true},"cell_type":"code","source":"Y_train = train[\"label\"]\n\n# Drop 'label' column\nX_train = train.drop(labels = [\"label\"],axis = 1) \n\n# free some space\ndel train \n\ng = sns.countplot(Y_train)\n\nY_train.value_counts()","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"b7b1b1d36243c885e57374c8b60c5a7e10abe922","_cell_guid":"5aea4062-1790-4987-b739-c4bebd79030f"},"cell_type":"markdown","source":"We have similar counts for the 10 digits."},{"metadata":{"_uuid":"5d77934302869925c19128c77e247b3c8ca84d71","_cell_guid":"5b7d4b66-a140-4fcc-a889-bcef007c880a"},"cell_type":"markdown","source":"## 2.2 Check for null and missing values"},{"metadata":{"_execution_state":"idle","_uuid":"cdf27c27e2a5b15e6d7bfc70de7a18c08f3feb7a","_cell_guid":"ececaa00-2ae3-4d13-b631-438df085b030","trusted":true},"cell_type":"code","source":"# Check the data\nX_train.isnull().any().describe()","execution_count":null,"outputs":[]},{"metadata":{"_execution_state":"idle","_uuid":"a0089bb7ec9aec76373db475399aea24699ae989","_cell_guid":"92572e80-8543-4e72-8767-5c9be8381b04","trusted":true},"cell_type":"code","source":"test.isnull().any().describe()","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"c0bee59691c2df0b275c78e38e7f9907d02ac038","_cell_guid":"09d04cae-4245-4659-85dd-ef48531da295"},"cell_type":"markdown","source":"I check for corrupted images (missing values inside).\n\nThere is no missing values in the train and test dataset. So we can safely go ahead."},{"metadata":{"_uuid":"2954681f25f0dcbe986e6914396cdbce61db591f","_cell_guid":"6812040d-80ad-43d2-a571-275f4f20067b"},"cell_type":"markdown","source":"## 2.3 Normalization"},{"metadata":{"_uuid":"0ecf4b52510ab7957d0d4eb646c0aa1ba5986273","_cell_guid":"159d5854-437a-4d0f-bc1e-fc3f7e43d178"},"cell_type":"markdown","source":"We perform a grayscale normalization to reduce the effect of illumination's differences. \n\nMoreover the CNN converg faster on [0..1] data than on [0..255]."},{"metadata":{"_execution_state":"idle","_uuid":"b5d4f8fcf2a967e2c7d57daedf95aa8c5ab7f8cb","_cell_guid":"cdc4340b-6e24-4e12-be99-ac806098ff17","trusted":true},"cell_type":"code","source":"# Normalize the data\nX_train = X_train / 255.0\ntest = test / 255.0","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"a66741bf1ac597094f3a3166877008feef27c519","_cell_guid":"7413df94-bcb9-4f75-b174-c127d4445766"},"cell_type":"markdown","source":"## 2.3 Reshape"},{"metadata":{"_execution_state":"idle","_uuid":"f0a6ad80dab8e0f2c2e46165ccd9cd82dd162bc3","_cell_guid":"34b6a5f7-8fd2-4387-8ef4-c9dc19584fed","trusted":true},"cell_type":"code","source":"# Reshape image in 3 dimensions (height = 28px, width = 28px , channel = 1)\nX_train = X_train.values.reshape(-1,28,28,1)\ntest = test.values.reshape(-1,28,28,1)","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"f4fb5553e188d9956f5d8b3a5d275ab00ea667ce","_cell_guid":"8decd1ce-7b7e-431d-8458-eaca18e0e1f7"},"cell_type":"markdown","source":"Train and test images (28px x 28px) has been stock into pandas.Dataframe as 1D vectors of 784 values. We reshape all data to 28x28x1 3D matrices. \n\nKeras requires an extra dimension in the end which correspond to channels. MNIST images are gray scaled so it use only one channel. For RGB images, there is 3 channels, we would have reshaped 784px vectors to 28x28x3 3D matrices. "},{"metadata":{"_uuid":"39b7a31e843bac6b705461bcce89da216b91799e","_cell_guid":"bdb422e2-bdec-444f-97a5-283a1e54bf2c"},"cell_type":"markdown","source":"## 2.5 Label encoding"},{"metadata":{"_execution_state":"idle","_uuid":"cabefd1478d5c1bdfe57fd6a34395340916a854c","_cell_guid":"4b7f3e78-44dc-4561-b1f0-9429ee024cf4","trusted":true},"cell_type":"code","source":"# Encode labels to one hot vectors (ex : 2 -> [0,0,1,0,0,0,0,0,0,0])\nY_train = to_categorical(Y_train, num_classes = 10)","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"dcfb688587dfc6feafd27442a3505e35dc01b82d","_cell_guid":"ae068bd8-b12e-4768-8a7e-0fc865dd7562"},"cell_type":"markdown","source":"Labels are 10 digits numbers from 0 to 9. We need to encode these lables to one hot vectors (ex : 2 -> [0,0,1,0,0,0,0,0,0,0])."},{"metadata":{"_uuid":"d8abbbf31483b94e1b29d07c4c8253d1311648a7","_cell_guid":"32152fc3-a570-4d64-8a7d-6c689a4acd33"},"cell_type":"markdown","source":"## 2.6 Split training and valdiation set "},{"metadata":{"_execution_state":"idle","_uuid":"6e51c925c6e0f1b936679c9649fef345c853555f","_cell_guid":"3a698301-9759-4279-ae48-fd980f89ea53","trusted":true},"cell_type":"code","source":"# Set the random seed\nrandom_seed = 2","execution_count":null,"outputs":[]},{"metadata":{"_execution_state":"idle","_uuid":"b779ac76d8317647db92d5a88b4098d212d72884","_cell_guid":"dcd25ebb-d845-4d32-9867-082e352b1396","trusted":true},"cell_type":"code","source":"# Split the train and the validation set for the fitting\nX_train, X_val, Y_train, Y_val = train_test_split(X_train, Y_train, test_size = 0.1, random_state=random_seed)","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"72ed54a305eebf80e8b8b0f7eabde1332a5a85a3","_cell_guid":"3c99964f-4500-4f1c-947d-c67e644e34db"},"cell_type":"markdown","source":"I chose to split the train set in two parts : a small fraction (10%) became the validation set which the model is evaluated and the rest (90%) is used to train the model.\n\nSince we have 42 000 training images of balanced labels (see 2.1 Load data), a random split of the train set doesn't cause some labels to be over represented in the validation set. Be carefull with some unbalanced dataset a simple random split could cause inaccurate evaluation during the validation. \n\nTo avoid that, you could use stratify = True option in train_test_split function (**Only for >=0.17 sklearn versions**)."},{"metadata":{"_uuid":"60eed15ec5bc0d354385301789ecb8538fc02267","_cell_guid":"adbeacf0-0dc0-4675-b2df-9c9663750f32"},"cell_type":"markdown","source":"We can get a better sense for one of these examples by visualising the image and looking at the label."},{"metadata":{"_execution_state":"idle","_uuid":"e0dae8943d3d35f075dba3d7ba31bde1d4bf2ff4","_cell_guid":"5f76131b-4ba0-45f1-a98c-bd4e7d561793","trusted":true},"cell_type":"code","source":"# Some examples\ng = plt.imshow(X_train[0][:,:,0])","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"5fa18b37a9acd9e098bac1d12264b0dd4310fdd3","_cell_guid":"d5265777-aeb3-449d-b171-d88cad74c0a4"},"cell_type":"markdown","source":"# 3. CNN\n## 3.1 Define the model"},{"metadata":{"_uuid":"7697570491420f957f6e4d3569d51410b5277250","_cell_guid":"504fa00e-148c-4364-9b68-218b3aaedfdb"},"cell_type":"markdown","source":"Here I have followed the ensemble method to train the model and used the Keras Sequential API, where you have just to add one layer at a time, starting from the input. \n\nDropout is a regularization method, where a proportion of nodes in the layer are randomly ignored (setting their wieghts to zero) for each training sample. This drops randomly a propotion of the network and forces the network to learn features in a distributed way. This technique also improves generalization and reduces the overfitting. \n\n'relu' is the rectifier (activation function max(0,x). The rectifier activation function is used to add non linearity to the network. \n\nThe Flatten layer is use to convert the final feature maps into a one single 1D vector. This flattening step is needed so that you can make use of fully connected layers after some convolutional layers. It combines all the found local features of the previous convolutional layers.\n\nIn the end i used the features in two fully-connected (Dense) layers which is just artificial an neural networks (ANN) classifier. In the last layer(Dense(10,activation=\"softmax\")) the net outputs distribution of probability of each class."},{"metadata":{"_execution_state":"idle","_uuid":"f7991ef6871a26f9fa57acdcd460a69bab53e804","_cell_guid":"1e0f3f88-2ad7-459e-8e02-aecc5f3511ae","trusted":true},"cell_type":"code","source":"# BUILD CONVOLUTIONAL NEURAL NETWORKS\nnets = 5\nmodel = [0] *nets\nfor j in range(nets):\n    model[j] = Sequential()\n\n    model[j].add(Conv2D(32, kernel_size = 3, padding='same', activation='relu', input_shape = (28, 28, 1)))\n    model[j].add(BatchNormalization())\n    #model[j].add(MaxPooling2D(pool_size=(2, 2)))\n    #model[j].add(Conv2D(32, kernel_size = 3, padding='same', activation='relu'))\n   # model[j].add(BatchNormalization())\n    #model[j].add(MaxPooling2D(pool_size=(2, 2)))\n    model[j].add(Conv2D(32, kernel_size = 3, padding='same', activation='relu'))\n    model[j].add(BatchNormalization())\n    model[j].add(MaxPooling2D(pool_size=(2, 2)))\n    model[j].add(Dropout(0.4))\n\n    model[j].add(Conv2D(64, kernel_size = 3, padding='same', activation='relu'))\n    model[j].add(BatchNormalization())\n   # model[j].add(MaxPooling2D(pool_size=(2, 2)))\n    #model[j].add(Conv2D(64, kernel_size = 3, padding='same', activation='relu'))\n    #model[j].add(BatchNormalization())\n   # model[j].add(MaxPooling2D(pool_size=(2, 2)))\n    model[j].add(Conv2D(64, kernel_size = 5, padding='same', activation='relu'))\n    model[j].add(BatchNormalization())\n    model[j].add(MaxPooling2D(pool_size=(2, 2)))\n    model[j].add(Dropout(0.25))\n\n    model[j].add(Conv2D(128, kernel_size = 4, padding='same', activation='relu'))\n    model[j].add(BatchNormalization())\n    model[j].add(MaxPooling2D(pool_size=(2, 2)))\n    \n    model[j].add(Flatten())\n    model[j].add(Dense(128, activation='relu'))\n    model[j].add(Dropout(0.7))\n    model[j].add(Dense(10, activation='softmax'))\n\n    # COMPILE WITH ADAM OPTIMIZER AND CROSS ENTROPY COST\n    model[j].compile(optimizer=\"adam\", loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"f24df64b223e0177c94025b6767ab19b722c5386","_cell_guid":"241a0f4f-13f5-4b13-be1e-4e3e4a714c06"},"cell_type":"markdown","source":"## 3.3 Data augmentation "},{"metadata":{"_uuid":"9e498b91419439f0fa791e595f202d9a0d56ad6b","_cell_guid":"e2d41e30-0724-40fb-a901-750e514ba9f9"},"cell_type":"markdown","source":"In order to avoid overfitting problem, we need to expand artificially our handwritten digit dataset. We can make your existing dataset even larger. The idea is to alter the training data with small transformations to reproduce the variations occuring when someone is writing a digit.\n\nFor example, the number is not centered \nThe scale is not the same (some who write with big/small numbers)\nThe image is rotated...\n\nApproaches that alter the training data in ways that change the array representation while keeping the label the same are known as data augmentation techniques. Some popular augmentations people use are grayscales, horizontal flips, vertical flips, random crops, color jitters, translations, rotations, and much more. \n\nBy applying just a couple of these transformations to our training data, we can easily double or triple the number of training examples and create a very robust model.\n\nThe improvement is important : \n   - Without data augmentation i obtained an accuracy of 98.114%\n   - With data augmentation i achieved 99.67% of accuracy"},{"metadata":{"_execution_state":"idle","_uuid":"d4e9e1ade3c04f9ca4d8cd44e799f9e09524d5a1","_cell_guid":"ade93d2d-90c6-4401-af95-f7d65f8c0a20","trusted":true},"cell_type":"code","source":"# Without data augmentation i obtained an accuracy of 0.98114\n#history = model.fit(X_train, Y_train, batch_size = batch_size, epochs = epochs, \n#          validation_data = (X_val, Y_val), verbose = 2)","execution_count":null,"outputs":[]},{"metadata":{"_execution_state":"idle","_uuid":"21d6192c87d92d497c797656474bccd9cefc5647","_cell_guid":"b342befe-1a6f-44bf-8dab-28033a729122","trusted":true},"cell_type":"code","source":"# With data augmentation to prevent overfitting (accuracy 0.99286)\n\ndatagen = 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=10,  # randomly rotate images in the range (degrees, 0 to 180)\n        zoom_range = 0.3, # Randomly zoom image \n        shear_range = 0.2, #Randomly shears images\n        width_shift_range=0.2,  # randomly shift images horizontally (fraction of total width)\n        height_shift_range=0.2,  # randomly shift images vertically (fraction of total height)\n        horizontal_flip=False,  # randomly flip images\n        vertical_flip=False)  # randomly flip images\n\ndatagen.fit(X_train)","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"51f16d0a5b9d9373438474e7defa7348359d7c18","_cell_guid":"22f80b8a-d4f6-4a34-b33d-ff7334f45d94"},"cell_type":"markdown","source":"For the data augmentation, i choosed to :\n   - Randomly rotate some training images\n   - Randomly  Zoom some training images\n   - Randomly shift images horizontally\n   - Randomly shift images vertically\n   \nI did not apply a vertical_flip nor horizontal_flip since it could have lead to misclassify symetrical numbers such as 6 and 9.\n\nOnce our model is ready, we fit the training dataset and during training, we save the models and use the best model to perform prediction."},{"metadata":{"trusted":true},"cell_type":"code","source":"# DECREASE LEARNING RATE EACH EPOCH\nannealer = LearningRateScheduler(lambda x: 1e-3 * 0.95 ** x)\n# TRAIN NETWORKS\nhistory = [0] * nets\nepochs = 25\nfor j in range(nets):\n    X_train2, X_val2, Y_train2, Y_val2 = train_test_split(X_train, Y_train, test_size = 0.1)\n    history[j] = model[j].fit_generator(datagen.flow(X_train2,Y_train2, batch_size=32),\n        epochs = epochs, steps_per_epoch = X_train2.shape[0]//32, verbose = 2,  \n        validation_data = (X_val2,Y_val2), callbacks=[annealer])\n    print(\"CNN {0:d}: Epochs={1:d}, Train accuracy={2:.5f}, Validation accuracy={3:.5f}\".format(\n        j+1,epochs,max(history[j].history['accuracy']),max(history[j].history['val_accuracy']) ))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Prediction"},{"metadata":{},"cell_type":"markdown","source":"**Here among all the models, the best performing model is estracted to perform the prediction.**"},{"metadata":{"trusted":true},"cell_type":"code","source":"# ENSEMBLE PREDICTIONS AND SUBMIT\nresults = np.zeros( (test.shape[0],10) ) \nfor j in range(nets):\n    results = results + model[j].predict(test)\nresults = np.argmax(results,axis = 1)\nresults = pd.Series(results,name=\"Label\")\nsubmission = pd.concat([pd.Series(range(1,28001),name = \"ImageId\"),results],axis = 1)\nsubmission.to_csv(\"MNIST-CNN-ENSEMBLE.csv\",index=False)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**This cell of code shows the accuracy curves for all models.**"},{"metadata":{"trusted":true},"cell_type":"code","source":"for j in range(nets):\n    accs = history[j].history['accuracy']\n    val_accs = history[j].history['val_accuracy']\n\n    plt.title(\"For CNN: \"+ str(j+1))\n    plt.plot(range(len(accs)),accs, label = 'Training_accuracy')\n    plt.plot(range(len(accs)),val_accs, label = 'Validation_accuracy')\n    plt.ylabel('accuracy')\n    plt.xlabel('epochs')\n    plt.legend()\n    plt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**This cell of code shows the loss curves for all models.**"},{"metadata":{"trusted":true},"cell_type":"code","source":"for j in range(nets):\n    loss = history[j].history['loss']\n    val_loss = history[j].history['val_loss']\n\n    plt.title(\"For CNN: \"+ str(j+1))\n    plt.plot(range(len(loss)),loss, label = 'Training_loss')\n    plt.plot(range(len(loss)),val_loss, label = 'Validation_loss')\n    plt.ylabel('loss')\n    plt.xlabel('epochs')\n    plt.legend()\n    plt.show()","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"0ec3bcf47cd49afea61aa9ed60ced64de8fc2d3d","_cell_guid":"b1c2790e-4313-4b47-937a-b4eaaae6d845"},"cell_type":"markdown","source":"\n\n** you found this notebook helpful or you just liked it , some upvotes would be very much appreciated - That will keep me motivated :)**\n"}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}