# Importing Libraries
from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split


def program():
    # Initialize batch size, number Classes, epochs
    batch_size = 128
    num_classes = 10
    epochs = 20
    img_rows, img_cols = 28, 28
    f = open('output.txt','a')
    print("\n \t Question 10 Programming \n",file = f)
    # Getting Training and testing data
    (x_train, y_train), (x_test, y_test) = mnist.load_data()

    if K.image_data_format() == 'channels_first':
        x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
        x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
        input_shape = (1, img_rows, img_cols)
    else:
        x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
        x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
        input_shape = (img_rows, img_cols, 1)

    x_train = x_train.astype('float32')
    x_test = x_test.astype('float32')
    x_train /= 255
    x_test /= 255
    print('x_train shape:', x_train.shape)
    print(x_train.shape[0], 'train samples')
    print(x_test.shape[0], 'test samples')

    y_train = keras.utils.to_categorical(y_train, num_classes)
    y_test = keras.utils.to_categorical(y_test, num_classes)

    model = Sequential()
    model.add(Conv2D(32, kernel_size=(3, 3),
                     activation='relu',
                     input_shape=input_shape))
    model.add(Conv2D(64, (3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(num_classes, activation='softmax'))

    
    model.compile(loss=keras.losses.categorical_crossentropy,
                  optimizer=keras.optimizers.Adadelta(),
                  metrics=['accuracy'])
    
    # Splitting validation and Training Data 
    x_train, x_validation, y_train, y_validation = train_test_split(x_train, y_train, test_size=0.3)
    
    #Fitting the model 
    history = model.fit(x_train, y_train,
                        batch_size=batch_size,
                        epochs=epochs,
                        verbose=1,
                        validation_data=(x_validation, y_validation))
    score = model.evaluate(x_test, y_test, verbose=0)
    
    train_acc = history.history['acc']
    train_loss = history.history['loss']
    validation_acc = history.history['val_acc']
    validation_loss = history.history['val_loss']
    
    
    #Trainig 
    print('Train loss:', train_loss[epochs-1],file=f)
    print('Train accuracy:', train_acc[epochs-1],file=f)
    
    #Validation 
    print('Validation loss:', validation_loss[epochs-1],file=f)
    print('Validation accuracy:', validation_acc[epochs-1],file=f)
    
    # Testing 
    print('Test loss:', score[0],file=f)
    print('Test accuracy:', score[1],file=f)
    
    
    # summarize history for accuracy
    plt.plot(history.history['acc'])
    plt.plot(history.history['val_acc'])
    plt.title('model accuracy')
    plt.ylabel('accuracy')
    plt.xlabel('epoch')
    plt.legend(['train', 'Validation'], loc='upper left')
    plt.savefig('Model Accuracy Plot.png')
    plt.show()
    plt.close(1)

    # summarize history for loss
    plt.plot(history.history['loss'])
    plt.plot(history.history['val_loss'])
    plt.title('model loss')
    plt.ylabel('loss')
    plt.xlabel('epoch')
    plt.legend(['train', 'Validation'], loc='upper left')
    plt.savefig('Model Loss Plot.png')
    plt.show()
    plt.close(1)

    #File Closing
    f.close()
def main():
    program()


main()
