def train(self):
# load data
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
y_train = keras.utils.to_categorical(y_train, self.num_classes)
y_test = keras.utils.to_categorical(y_test, self.num_classes)
x_train, x_test = self.color_preprocessing(x_train, x_test)
# build network
model = self.build_model()
model.summary()
# Save the best model during each training checkpoint
checkpoint = ModelCheckpoint(self.model_filename,
monitor='val_loss',
verbose=0,
save_best_only= True,
mode='auto')
plot_callback = PlotLearning()
tb_cb = TensorBoard(log_dir=self.log_filepath, histogram_freq=0)
cbks = [checkpoint, plot_callback, tb_cb]
# set data augmentation
print('Using real-time data augmentation.')
datagen = ImageDataGenerator(horizontal_flip=True,width_shift_range=0.125,height_shift_range=0.125,fill_mode='constant',cval=0.)
datagen.fit(x_train)
# start training
model.fit_generator(datagen.flow(x_train, y_train,batch_size=self.batch_size),steps_per_epoch=self.iterations,epochs=self.epochs,callbacks=cbks,validation_data=(x_test, y_test))
model.save(self.model_filename)
self._model = model
def train(self):
# load data
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
y_train = keras.utils.to_categorical(y_train, self.num_classes)
y_test = keras.utils.to_categorical(y_test, self.num_classes)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
# color preprocessing
x_train, x_test = self.color_preprocessing(x_train, x_test)
# build network
img_input = Input(shape=(self.img_rows, self.img_cols,
self.img_channels))
output = self.densenet(img_input, self.num_classes)
model = Model(img_input, output)
model.summary()
# plot_model(model, show_shapes=True, to_file='model.png')
# set optimizer
sgd = optimizers.SGD(lr=.1, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy',
optimizer=sgd,
metrics=['accuracy'])
# set callback
tb_cb = TensorBoard(log_dir=self.log_filepath, histogram_freq=0)
change_lr = LearningRateScheduler(self.scheduler)
ckpt = ModelCheckpoint(self.model_filename,
monitor='val_loss',
verbose=0,
save_best_only=True,
mode='auto')
plot_callback = PlotLearning()
cbks = [change_lr, tb_cb, ckpt, plot_callback]
# set data augmentation
print('Using real-time data augmentation.')
datagen = ImageDataGenerator(horizontal_flip=True,
width_shift_range=0.125,
height_shift_range=0.125,
fill_mode='constant',
cval=0.)
datagen.fit(x_train)
# start training
model.fit_generator(datagen.flow(x_train,
y_train,
batch_size=self.batch_size),
steps_per_epoch=self.iterations,
epochs=self.epochs,
callbacks=cbks,
validation_data=(x_test, y_test))
model.save(self.model_filename)
self._model = model
self.param_count = self._model.count_params()
def train(self):
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
y_train = keras.utils.to_categorical(y_train, self.num_classes)
y_test = keras.utils.to_categorical(y_test, self.num_classes)
# color preprocessing
x_train, x_test = self.color_preprocessing(x_train, x_test)
model = self.pure_cnn_network(self.input_shape)
model.summary()
# Save the best model during each training checkpoint
checkpoint = ModelCheckpoint(self.model_filename,
monitor='val_loss',
verbose=0,
save_best_only=True,
mode='auto')
plot_callback = PlotLearning()
tb_cb = TensorBoard(log_dir=self.log_filepath, histogram_freq=0)
cbks = [checkpoint, plot_callback, tb_cb]
# set data augmentation
print('Using real-time data augmentation.')
datagen = ImageDataGenerator(horizontal_flip=True,
width_shift_range=0.125,
height_shift_range=0.125,
fill_mode='constant',
cval=0.)
datagen.fit(x_train)
model.compile(
loss=
'categorical_crossentropy', # Better loss function for neural networks
optimizer=Adam(lr=self.learn_rate
), # Adam optimizer with 1.0e-4 learning rate
metrics=['accuracy']) # Metrics to be evaluated by the model
model.fit_generator(datagen.flow(x_train,
y_train,
batch_size=self.batch_size),
epochs=self.epochs,
validation_data=(x_test, y_test),
callbacks=cbks,
verbose=1)
model.save(self.model_filename)
self._model = model
