keras-traning.py

# Training and saving weight
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Conv2D, MaxPooling2D, Flatten
from keras.optimizers import RMSprop
import numpy
import os


(mnist_train_images, mnist_train_labels), (mnist_test_images, mnist_test_labels) = mnist.load_data()

from keras import backend as K

if K.image_data_format() == 'channels_first':
    train_images = mnist_train_images.reshape(mnist_train_images.shape[0], 1, 28, 28)
    test_images = mnist_test_images.reshape(mnist_test_images.shape[0], 1, 28, 28)
    input_shape = (1, 28, 28)
else:
    train_images = mnist_train_images.reshape(mnist_train_images.shape[0], 28, 28, 1)
    test_images = mnist_test_images.reshape(mnist_test_images.shape[0], 28, 28, 1)
    input_shape = (28, 28, 1)
    
train_images = train_images.astype('float32')
test_images = test_images.astype('float32')
train_images /= 255
test_images /= 255

train_labels = keras.utils.to_categorical(mnist_train_labels, 10)
test_labels = keras.utils.to_categorical(mnist_test_labels, 10)

model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
                 activation='relu',
                 input_shape=input_shape))
# 64 3x3 kernels
model.add(Conv2D(64, (3, 3), activation='relu'))
# Reduce by taking the max of each 2x2 block
model.add(MaxPooling2D(pool_size=(2, 2)))
# Dropout to avoid overfitting
model.add(Dropout(0.25))
# Flatten the results to one dimension for passing into our final layer
model.add(Flatten())
# A hidden layer to learn with
model.add(Dense(128, activation='relu'))
# Another dropout
model.add(Dropout(0.5))
# Final categorization from 0-9 with softmax
model.add(Dense(10, activation='softmax'))

model.summary()

model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])
              
history = model.fit(train_images, train_labels,
                    batch_size=32,
                    epochs=15,
                    verbose=2,
                    validation_data=(test_images, test_labels))

score = model.evaluate(test_images, test_labels, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
 
# serialize model to JSON
model_json = model.to_json()
with open("model.json", "w") as json_file:
    json_file.write(model_json)
# serialize weights to HDF5
model.save_weights("model.h5")
print("Saved model to disk")