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revert back Adam

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This commit is contained in:
iperov 2019-03-11 21:52:36 +04:00
parent e4637336ef
commit ee8dbcbc35
3 changed files with 56 additions and 66 deletions
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20
models/Model_SAE/Model.py
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@ -223,13 +223,6 @@ class SAEModel(ModelBase):
pred_dst_dstm = self.decoder_dstm(warped_dst_code)
pred_src_dstm = self.decoder_srcm(warped_dst_code)
self.src_dst_opt, \
self.src_dst_mask_opt = self.load_weights_safe(
weights_to_load,
[ [Adam(lr=5e-5, beta_1=0.5, beta_2=0.999), 'src_dst_opt'],
[Adam(lr=5e-5, beta_1=0.5, beta_2=0.999), 'src_dst_mask_opt']
])
pred_src_src, pred_dst_dst, pred_src_dst, = [ [x] if type(x) != list else x for x in [pred_src_src, pred_dst_dst, pred_src_dst, ] ]
if self.options['learn_mask']:
@ -267,6 +260,9 @@ class SAEModel(ModelBase):
psd_target_dst_anti_masked_ar = [ pred_src_dst_sigm_ar[i]*target_dstm_anti_sigm_ar[i] for i in range(len(pred_src_dst_sigm_ar))]
if self.is_training_mode:
self.src_dst_opt = Adam(lr=5e-5, beta_1=0.5, beta_2=0.999)
self.src_dst_mask_opt = Adam(lr=5e-5, beta_1=0.5, beta_2=0.999)
if self.options['archi'] == 'liae':
src_dst_loss_train_weights = self.encoder.trainable_weights + self.inter_B.trainable_weights + self.inter_AB.trainable_weights + self.decoder.trainable_weights
if self.options['learn_mask']:
@ -325,14 +321,17 @@ class SAEModel(ModelBase):
self.AE_view = K.function ([warped_src, warped_dst], [pred_src_src[-1], pred_dst_dst[-1], pred_src_dst[-1], pred_src_dstm[-1]])
else:
self.AE_view = K.function ([warped_src, warped_dst], [pred_src_src[-1], pred_dst_dst[-1], pred_src_dst[-1] ] )
self.load_weights_safe(weights_to_load)#, [ [self.src_dst_opt, 'src_dst_opt'], [self.src_dst_mask_opt, 'src_dst_mask_opt']])
else:
self.load_weights_safe(weights_to_load)
if self.options['learn_mask']:
self.AE_convert = K.function ([warped_dst],[ pred_src_dst[-1], pred_src_dstm[-1] ])
else:
self.AE_convert = K.function ([warped_dst],[ pred_src_dst[-1] ])
if self.is_training_mode:
if self.is_training_mode:
self.src_sample_losses = []
self.dst_sample_losses = []
@ -353,6 +352,7 @@ class SAEModel(ModelBase):
sample_process_options=SampleProcessor.Options(random_flip=self.random_flip, normalize_tanh = True),
output_sample_types=output_sample_types )
])
#override
def onSave(self):
opt_ar = [ [self.src_dst_opt, 'src_dst_opt'],