def create_model():
    """
    This method create neural network model.
    :return: model
    """

    model = Sequential()
    model.add(Convolution2D(16, (5, 5), input_shape=(64, 64, 6)))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Convolution2D(64, (5, 5)))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Convolution2D(256, (5, 5)))
    model.add(BatchNormalization())
    model.add(Activation('relu'))

    model.add(Flatten())
    model.add(Dropout(0.5))

    model.add(Dense(640))
    model.add(BatchNormalization())
    model.add(Activation('relu'))

    model.add(Dropout(0.5))
    model.add(Dense(2))
    model.add(Activation('softmax'))

    sgd = SGD(lr=0.0001, momentum=0.9, decay=0.005)
    model.compile(optimizer='sgd', loss="categorical_crossentropy", metrics=['accuracy'])

    print(model.summary())
    return model