import keras
    from keras import backend as K
    import os
    from keras.layers.advanced_activations import LeakyReLU
    from __future__ import print_function
    from keras.datasets import mnist
    import matplotlib.pylab as plt


    from importlib import reload
    def set_keras_backend(backend):

        if K.backend() != backend:
            os.environ['KERAS_BACKEND'] = backend
            reload(K)
            assert K.backend() == backend
    set_keras_backend("tensorflow")


    DATA = joblib.load(open('Data.sav', 'rb'))
    LABEL = joblib.load(open('Lable.sav', 'rb'))

    print(DATA.shape)
    print(LABEL.shape)

print(tf.__version__)


X_train, X_test, y_train, y_test = train_test_split(DATA, LABEL, test_size=0.30, random_state=45)
print(X_train.shape)
print(X_test.shape)
print(y_train.shape)
print(y_test.shape)
print(X_train[0])


    X_train = np.reshape(X_train,(X_train.shape[0],200,180,1))
    X_test = np.reshape(X_test,(X_test.shape[0],200,180,1))

# convert the data from binary to float

    X_train = X_train.astype('float32')
    X_test = X_test.astype('float32')
    X_train /= 255
    X_test /= 255


model = Sequential()

model.add(Conv2D(32, kernel_size=(5,5), strides=(1, 1),
                     activation='relu',
                     input_shape=([200,180,1])))

model.add(LeakyReLU(alpha=0.1))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(64,(5,5), activation='relu'))
model.add(LeakyReLU(alpha=0.1))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(128,(5,5), activation='relu'))
model.add(LeakyReLU(alpha=0.1))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.30))


model.add(Flatten())
model.add(Dense(1000, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(72, activation='softmax'))

# When we compile the model, we declare the loss function and the optimizer

model.compile(loss=keras.losses.categorical_crossentropy,

             optimizer=keras.optimizers.Adam(),
metrics=['accuracy'])

# Train the model
hist = model.fit(X_train, Y_train,batch_size=32,epochs=12, verbose=1, validation_data=(X_test, Y_test))


score = model.evaluate(X_test, Y_test, verbose=0)

print("%s: %.2f%%" % ('Accuracy', score[1]*100))