num_classes = 10
import glob
ckpt_filelist = glob.glob('./ckpt_lenet/*.h5')
if len(ckpt_filelist) == 0:
model = LeNet(input_shape, num_classes)
model.compile(loss=losses.categorical_crossentropy,
optimizer=optimizers.Adadelta(),
metrics=['accuracy'])
else:
filename = ckpt_filelist[-1]
model = load_model(filename)
initial_epoch = int(filename.split('weights.')[-1].split('-')[0])
print('restore from ckpt, initial_echo = {0}'.format(initial_epoch))
print(model.summary())
tensorboard = TensorBoard(log_dir='./logs_lenet')
# weights.{epoch:02d}-{val_loss:.2f}.hdf5 : a kind of code rule
checkpointer = ModelCheckpoint(
filepath='./ckpt_lenet/weights.{epoch:02d}-{val_loss:.2f}.h5',
monitor='val_loss')
history = model.fit(x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
callbacks=[tensorboard, checkpointer],
validation_data=(x_test, y_test),
initial_epoch=initial_epoch)
