from __future__ import print_function
import tensorflow.keras as keras
from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D, AveragePooling2D
from tensorflow.keras import backend as K
from tensorflow.keras import optimizers

batch_size = 128
num_classes = 10

def get_lenet():
  model = keras.Sequential()
  
  model.add(Conv2D(filters=6, kernel_size=(3, 3), activation='relu', input_shape=(28,28,1)))
  model.add(AveragePooling2D())
  
  model.add(Conv2D(filters=16, kernel_size=(3, 3), activation='relu'))
  model.add(AveragePooling2D())
  
  model.add(Flatten())
  
  model.add(Dense(units=120, activation='relu'))
  
  model.add(Dense(units=84, activation='relu'))
  
  model.add(Dense(units=10, activation = 'softmax'))
  return model


# input image dimensions
img_rows, img_cols = 28, 28

# the data, split between train and test sets
(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')

# 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 = get_lenet()

sgd = optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='mean_squared_error', optimizer=sgd)

model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=1,
          verbose=1)

y_pred = model.predict(x_test)

print(y_pred.shape)
print(y_test.shape)