model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adam(),
metrics=['accuracy'])
filepath = "weights-{epoch:02d}-{val_acc:.3f}.hdf5"
checkpoint = keras.callbacks.ModelCheckpoint(filepath,
monitor='val_acc',
verbose=1,
save_best_only=True,
mode='max')
reduce_lr = keras.callbacks.ReduceLROnPlateau(monitor='val_acc',
factor=0.1,
patience=5,
cooldown=0,
min_lr=1e-5)
callbacks_list = [checkpoint, reduce_lr]
datagen = ImageDataGenerator(width_shift_range=10. / 64,
height_shift_range=10. / 64,
horizontal_flip=True)
# training loop
batch_size = 64
epochs = 300
steps_per_epoch = int(np.ceil(x_train.shape[0] / float(batch_size)))
model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),
steps_per_epoch=steps_per_epoch,
epochs=epochs,
validation_data=(x_val, y_val),
callbacks=callbacks_list)
filepath = "weights-{epoch:02d}-{val_acc:.3f}.hdf5"
checkpoint = keras.callbacks.ModelCheckpoint(filepath,
monitor='val_acc',
verbose=1,
save_best_only=True,
mode='max')
reduce_lr = keras.callbacks.ReduceLROnPlateau(monitor='val_acc',
factor=0.1,
patience=5,
cooldown=0,
min_lr=1e-5)
callbacks_list = [checkpoint, reduce_lr]
dataaug = ImageDataGenerator(width_shift_range=10. / 64,
height_shift_range=10. / 64,
horizontal_flip=True)
# training loop
batch_size = 64
epochs = 300
steps_per_epoch = int(np.ceil(x_train.shape[0] / float(batch_size)))
model.fit_generator(mixup_generator(x_train,
y_train,
batch_size,
alpha=0.2,
dataaug=dataaug),
steps_per_epoch=steps_per_epoch,
epochs=epochs,
validation_data=(x_val, y_val),
callbacks=callbacks_list)