DigitRecognizer

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import seaborn as sns
import itertools
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix

from keras.utils.np_utils import to_categorical # convert to one-hot-encoding
from keras.models import Sequential
from keras.models import load_model
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D
from keras.optimizers import RMSprop
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import ReduceLROnPlateau

np.random.seed(2)

# Load the data
train = pd.read_csv("./input/train.csv")
test = pd.read_csv("./input/test.csv")

Y_train = train["label"]
# Drop 'label' column
X_train = train.drop(labels = ["label"],axis = 1)

# free some space
del train
#Reshape
# Normalize the data
X_train = X_train / 255.0
test = test / 255.0

X_train = np.array(X_train.values.reshape(-1,28,28,1))
test = np.array(test.values.reshape(-1,28,28,1))

def analysis_data():
    # g=sns.countplot(Y_train)
    # Y_train.value_counts()
    # plt.show()
    # #Check for null and missing values
    # X_train.isnull().any().describe()
    # test.isnull().any().describe()
    print(X_train[0].shape)
    g=plt.imshow(X_train[0][:,:,0])
    plt.show()


#analysis_data()



#Label encoding
Y_train = to_categorical(Y_train, num_classes = 10)

# Set the random seed
random_seed = 2

# Split the train and the validation set for the fitting
X_train, X_val, Y_train, Y_val = train_test_split(X_train, Y_train, test_size = 0.1, random_state=random_seed)



model_name='cnn_model.h5'
def train():
    # Define the model
    model = Sequential()
    model.add(Conv2D(filters=32, kernel_size=(5, 5), padding='Same',
                     activation='relu', input_shape=(28, 28, 1)))
    model.add(Conv2D(filters=32, kernel_size=(5, 5), padding='Same',
                     activation='relu'))
    model.add(MaxPool2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))

    model.add(Conv2D(filters=64, kernel_size=(3, 3), padding='Same',
                     activation='relu'))
    model.add(Conv2D(filters=64, kernel_size=(3, 3), padding='Same',
                     activation='relu'))
    model.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))
    model.add(Dropout(0.25))

    model.add(Flatten())
    model.add(Dense(256, activation="relu"))
    model.add(Dropout(0.5))
    model.add(Dense(10, activation="softmax"))

    # Define the optimizer
    optimizer = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)

    # Compile the model
    model.compile(optimizer=optimizer, loss="categorical_crossentropy", metrics=["accuracy"])

    # Set a learning rate annealer
    learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc',
                                                patience=3,
                                                verbose=1,
                                                factor=0.5,
                                                min_lr=0.00001)

    epochs = 2  # Turn epochs to 30 to get 0.9967 accuracy
    batch_size = 86

    # Data augmentation
    datagen = ImageDataGenerator(
        featurewise_center=False,  # set input mean to 0 over the dataset
        samplewise_center=False,  # set each sample mean to 0
        featurewise_std_normalization=False,  # divide inputs by std of the dataset
        samplewise_std_normalization=False,  # divide each input by its std
        zca_whitening=False,  # apply ZCA whitening
        rotation_range=10,  # randomly rotate images in the range (degrees, 0 to 180)
        zoom_range=0.1,  # Randomly zoom image
        width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)
        height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)
        horizontal_flip=False,  # randomly flip images
        vertical_flip=False)  # randomly flip images

    datagen.fit(X_train)

    # Fit the model
    history = model.fit_generator(datagen.flow(X_train,Y_train, batch_size=batch_size),
                                  epochs = epochs, validation_data = (X_val,Y_val),
                                  verbose = 2, steps_per_epoch=X_train.shape[0] // batch_size
                                  , callbacks=[learning_rate_reduction])

    model.save(model_name)

    # Plot the loss and accuracy curves for training and validation
    fig, ax = plt.subplots(2,1)
    ax[0].plot(history.history['loss'], color='b', label="Training loss")
    ax[0].plot(history.history['val_loss'], color='r', label="validation loss",axes =ax[0])
    legend = ax[0].legend(loc='best', shadow=True)

    ax[1].plot(history.history['acc'], color='b', label="Training accuracy")
    ax[1].plot(history.history['val_acc'], color='r',label="Validation accuracy")
    legend = ax[1].legend(loc='best', shadow=True)
    plt.show()


def plot_confusion_matrix(cm, classes,
                          normalize=False,
                          title='Confusion matrix',
                          cmap=plt.cm.Blues):
    """
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    """
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=45)
    plt.yticks(tick_marks, classes)

    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]

    thresh = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, cm[i, j],
                 horizontalalignment="center",
                 color="white" if cm[i, j] > thresh else "black")

    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')
    plt.show()

def compute_confusion_matrix():
    model=load_model(model_name)
    # Predict the values from the validation dataset
    Y_pred = model.predict(X_val)
    # Convert predictions classes to one hot vectors
    Y_pred_classes = np.argmax(Y_pred, axis=1)
    # Convert validation observations to one hot vectors
    Y_true = np.argmax(Y_val, axis=1)
    # compute the confusion matrix
    confusion_mtx = confusion_matrix(Y_true, Y_pred_classes)

    # plot the confusion matrix
    plot_confusion_matrix(confusion_mtx, classes=range(10))

def display_errors(errors_index,img_errors,pred_errors, obs_errors):
    """ This function shows 6 images with their predicted and real labels"""
    n = 0
    nrows = 2
    ncols = 3
    fig, ax = plt.subplots(nrows,ncols,sharex=True,sharey=True)
    for row in range(nrows):
        for col in range(ncols):
            error = errors_index[n]
            ax[row,col].imshow((img_errors[error]).reshape((28,28)))
            ax[row,col].set_title("Predicted label :{}\nTrue label :{}".format(pred_errors[error],obs_errors[error]))
            n += 1

    plt.show()

def display_error_results():
    model=load_model(model_name)
    # Predict the values from the validation dataset
    Y_pred = model.predict(X_val)
    # Convert predictions classes to one hot vectors
    Y_pred_classes = np.argmax(Y_pred, axis=1)
    # Convert validation observations to one hot vectors
    Y_true = np.argmax(Y_val, axis=1)

    errors=(Y_pred_classes-Y_true!=0)
    Y_pred_classes_errors = Y_pred_classes[errors]

    Y_true_errors = Y_true[errors]
    X_val_errors = X_val[errors]

    Y_pred_errors=Y_pred[errors]
    # Probabilities of the wrong predicted numbers
    Y_pred_errors_prob = np.max(Y_pred_errors, axis=1)

    # Predicted probabilities of the true values in the error set
    true_prob_errors = np.diagonal(np.take(Y_pred_errors, Y_true_errors, axis=1))

    # Difference between the probability of the predicted label and the true label
    delta_pred_true_errors = Y_pred_errors_prob - true_prob_errors

    # Sorted list of the delta prob errors
    sorted_dela_errors = np.argsort(delta_pred_true_errors)

    # Top 6 errors
    most_important_errors = sorted_dela_errors[-6:]

    # Show the top 6 errors
    display_errors(most_important_errors, X_val_errors, Y_pred_classes_errors, Y_true_errors)




def test():
    model=load_model(model_name)

    results=model.predict(test)
    results=np.argmax(results,axis=1)
    results=pd.Series(results,name='Label')
    submission=pd.concat([pd.Series(range(1,28001),name='ImageId'),results],axis=1)
    submission.to_csv('result.csv',index=False)



if __name__=='__main__':
    train()
    #compute_confusion_matrix()
    #display_error_results()
    #test()