if opt.load_from is not None:
model.load_weights(opt.load_from)
DSSIM_L1 = get_dssim_l1_loss()
model.compile(optimizer='adam', loss=DSSIM_L1, metrics=['mse', DSSIM_L1])
sources = TransformedLRSR(opt)
tensorboard = TensorBoard(log_dir=os.path.join(opt.checkpoints_dir, 'logs'),
histogram_freq=0,
batch_size=32,
write_graph=True,
write_grads=False,
write_images=True)
checkpointer = ModelCheckpoint(filepath=os.path.join(opt.checkpoints_dir,
'weights.hdf5'),
verbose=1,
save_best_only=True)
model.fit_generator(make_generator(sources['train'],
batch_size=opt.batch_size),
validation_data=make_generator(sources['test'],
batch_size=opt.batch_size),
validation_steps=4,
steps_per_epoch=200,
epochs=1000,
verbose=2,
callbacks=[checkpointer, tensorboard])
export_model_to_js(model, opt.work_dir + '/__js_model__')
def export(self, my):
opt = self._opt
export_model_to_js(self.model, opt.work_dir+'/__js_model__')