from __future__ import print_function
import keras
from keras.datasets import cifar10
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D
import matplotlib.pyplot as plt

batch_size = 1231
num_classes = 10
epochs = 30
data_augmentation = False
num_predictions = 20

# The data, split between train and test sets:
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

# Convert class vectors to binary class matrices.
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, (3, 3), padding='same',
                 input_shape=x_train.shape[1:]))
model.add(Activation('sigmoid'))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('tanh'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Conv2D(64, (3, 3), padding='same'))
model.add(Activation('sigmoid'))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('tanh'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(512))
model.add(Activation('sigmoid'))
model.add(Dropout(0.5))
model.add(Dense(num_classes))
model.add(Activation('tanh'))

# initiate RMSprop optimizer
opt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6)

# Let's train the model using RMSprop
model.compile(loss='categorical_crossentropy',
              optimizer=opt,
              metrics=['accuracy'])

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255

# Model Summary
model.summary()

# Model Fitting
history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(x_test, y_test),shuffle=True)

# Score trained model.
scores = model.evaluate(x_test, y_test, verbose=1)
print('Test loss:', scores[0])
print('Test accuracy:', scores[1])

# Plot The CFIR-10 CNN LOSS
fig1 = plt.figure()
plt.plot(history.history['loss'], 'r', linewidth=3.0)
plt.plot(history.history['val_loss'], 'b', linewidth=3.0)
plt.legend(['Training loss', 'Validation Loss'], fontsize=18)
plt.xlabel('Epochs ', fontsize=16)
plt.ylabel('Loss', fontsize=16)
plt.title('Loss Curves : CIFR-10 CNN', fontsize=16)
fig1.savefig('cifr_loss_lstm.png')

# Plot the CIFR-10 CNN Accuracy
fig2=plt.figure()
plt.plot(history.history['acc'], 'r', linewidth=3.0)
plt.plot(history.history['val_acc'], 'b', linewidth=3.0)
plt.legend(['Training Accuracy', 'Validation Accuracy'], fontsize=18)
plt.xlabel('Epochs ', fontsize=16)
plt.ylabel('Accuracy', fontsize=16)
plt.title('Accuracy Curves : CIFR-10 CNN', fontsize=16)
fig2.savefig('cifr_accuracy_lstm.png')
plt.show()