keras_cnn

#!/usr/bin/python3

import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Flatten
from keras.layers import Conv2D, AveragePooling2D
from keras.callbacks import ModelCheckpoint
from keras.datasets import mnist
import numpy as np

(x_train, y_train), (x_test, y_test) = mnist.load_data()

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

# The known number of output classes.
num_classes = 10
# Input image dimensions
img_rows, img_cols = 28, 28

# Channels go last for TensorFlow backend
x_train_reshaped = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test_reshaped = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)

# Convert class vectors to binary class matrices. This uses 1 hot encoding.
y_train_binary = keras.utils.to_categorical(y_train, num_classes)
y_test_binary = keras.utils.to_categorical(y_test, num_classes)

model = Sequential()
model.add(Conv2D(6, kernel_size=(5, 5),
                 activation='sigmoid',
                 input_shape=input_shape))
model.add(AveragePooling2D(pool_size=(2, 2)))
model.add(Conv2D(6, kernel_size=(5, 5),
                 activation='sigmoid'))
model.add(AveragePooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(num_classes, activation='sigmoid'))

model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer='sgd',
              metrics=['accuracy'])

epochs = 10
batch_size = 30
# Fit the model weights.
model.fit(x_train_reshaped, y_train_binary,
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          validation_data=(x_test_reshaped, y_test_binary))