keras_mnist.py

import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D

num_classes = 10
width, height = 28, 28

(x_train, y_train), (x_test, y_test) = mnist.load_data()
# print(x_train.shape)

x_train = x_train.reshape(x_train.shape[0], width, height, 1)
x_test = x_test.reshape(x_test.shape[0], width, height, 1)

input_shape = (width, height, 1)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255 # norm to 0..1
x_test /= 255 # norm to 0..1

# convert class vectors to "one hot" binary class matrices 
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), input_shape=input_shape))
# model.add(Conv2D(64, (3, 3), 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(num_classes, activation='softmax'))

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

model.fit(x_train, y_train)

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

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