# Keras model 
## Convolution layers 
model = Sequential()
model.add(Conv2D(10, kernel_size=(3, 3),
                 activation='relu',
                 input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(10, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(10, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())

## Fully connected layers
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))

# Loss layer 
model.add(Dense(n_classes, activation='softmax'))
