CNN.py

from tensorflow.contrib.keras.python.keras.datasets import mnist
from tensorflow.contrib.keras.python.keras.layers import Convolution2D, MaxPooling2D, Dropout, Flatten, Dense
from tensorflow.contrib.keras.python.keras.models import Sequential
from tensorflow.contrib.keras.python.keras.utils import np_utils

(X_train, y_train), (X_test, y_test) = mnist.load_data()

# Preprocess
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255

# Preprocess  labels
Y_train = np_utils.to_categorical(y_train, 10)
Y_test = np_utils.to_categorical(y_test, 10)

# Model
model = Sequential()

#defaultformat: (samples, rows, col
model.add(Convolution2D(32, kernel_size=(3, 3),strides=(1, 1),activation='relu', input_shape=(28, 28, 1)))
model.add(Convolution2D(32, kernel_size=(3, 3),strides=(1, 1), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
model.summary()


model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy']
              )


model.fit(X_train, Y_train,
          batch_size=32, epochs=10, verbose=1)


score = model.evaluate(X_test, Y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])