@ -468,8 +468,10 @@ def train(dataset: tf.data.Dataset,
|
||||
'z_discriminator': model.ZDiscriminator(),
|
||||
'x_discriminator': model.XDiscriminator(),
|
||||
# define optimizers
|
||||
'decoder_optimizer': tf.train.AdamOptimizer(learning_rate=checkpointables['learning_rate_var'], beta1=0.5, beta2=0.999),
|
||||
'enc_dec_optimizer': tf.train.AdamOptimizer(learning_rate=checkpointables['learning_rate_var'], beta1=0.5, beta2=0.999),
|
||||
'decoder_optimizer': tf.train.AdamOptimizer(learning_rate=checkpointables['learning_rate_var'],
|
||||
beta1=0.5, beta2=0.999),
|
||||
'enc_dec_optimizer': tf.train.AdamOptimizer(learning_rate=checkpointables['learning_rate_var'],
|
||||
beta1=0.5, beta2=0.999),
|
||||
'z_discriminator_optimizer': tf.train.AdamOptimizer(learning_rate=checkpointables['learning_rate_var'],
|
||||
beta1=0.5, beta2=0.999),
|
||||
'x_discriminator_optimizer': tf.train.AdamOptimizer(learning_rate=checkpointables['learning_rate_var'],
|
||||
@ -606,7 +608,6 @@ def _train_one_epoch(epoch: int,
|
||||
global_step_decoder: tf.Variable,
|
||||
global_step_enc_dec: tf.Variable,
|
||||
epoch_var: tf.Variable) -> Dict[str, float]:
|
||||
|
||||
with summary_ops_v2.always_record_summaries():
|
||||
epoch_var.assign(epoch)
|
||||
epoch_start_time = time.time()
|
||||
|
||||
Reference in New Issue
Block a user