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Fixed coding style

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Signed-off-by: Jim Martens <github@2martens.de>
This commit is contained in:
Jim Martens
2019-03-25 14:32:08 +01:00
parent 9a08ea3bb9
commit 6835c284af
1 changed files with 25 additions and 24 deletions
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49
src/twomartens/masterthesis/aae/train.py
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@ -447,7 +447,7 @@ def train(dataset: tf.data.Dataset,
sample = K.expand_dims(K.expand_dims(K.random_normal((64, zsize)), axis=1), axis=1) sample = K.expand_dims(K.expand_dims(K.random_normal((64, zsize)), axis=1), axis=1)
# z generator function # z generator function
z_generator = functools.partial(_get_z_variable, batch_size=batch_size, zsize=zsize) z_generator = functools.partial(_get_z_variable, batch_size=batch_size, zsize=zsize)
# non-preserved python variables # non-preserved python variables
encoder_lowest_loss = math.inf encoder_lowest_loss = math.inf
decoder_lowest_loss = math.inf decoder_lowest_loss = math.inf
@ -463,24 +463,26 @@ def train(dataset: tf.data.Dataset,
} }
checkpointables.update({ checkpointables.update({
# get models # get models
'encoder': model.Encoder(zsize), 'encoder': model.Encoder(zsize),
'decoder': model.Decoder(channels), 'decoder': model.Decoder(channels),
'z_discriminator': model.ZDiscriminator(), 'z_discriminator': model.ZDiscriminator(),
'x_discriminator': model.XDiscriminator(), 'x_discriminator': model.XDiscriminator(),
# define optimizers # define optimizers
'decoder_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'],
'enc_dec_optimizer': tf.train.AdamOptimizer(learning_rate=checkpointables['learning_rate_var'], beta1=0.5, beta2=0.999), 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'], 'z_discriminator_optimizer': tf.train.AdamOptimizer(learning_rate=checkpointables['learning_rate_var'],
beta1=0.5, beta2=0.999), beta1=0.5, beta2=0.999),
'x_discriminator_optimizer': tf.train.AdamOptimizer(learning_rate=checkpointables['learning_rate_var'], 'x_discriminator_optimizer': tf.train.AdamOptimizer(learning_rate=checkpointables['learning_rate_var'],
beta1=0.5, beta2=0.999), beta1=0.5, beta2=0.999),
# global step counter # global step counter
'epoch_var': K.variable(-1, dtype=tf.int64), 'epoch_var': K.variable(-1, dtype=tf.int64),
'global_step': tf.train.get_or_create_global_step(), 'global_step': tf.train.get_or_create_global_step(),
'global_step_decoder': K.variable(0, dtype=tf.int64), 'global_step_decoder': K.variable(0, dtype=tf.int64),
'global_step_enc_dec': K.variable(0, dtype=tf.int64), 'global_step_enc_dec': K.variable(0, dtype=tf.int64),
'global_step_xd': K.variable(0, dtype=tf.int64), 'global_step_xd': K.variable(0, dtype=tf.int64),
'global_step_zd': K.variable(0, dtype=tf.int64), 'global_step_zd': K.variable(0, dtype=tf.int64),
}) })
# checkpoint # checkpoint
@ -533,13 +535,13 @@ def train(dataset: tf.data.Dataset,
# save weights at end of epoch # save weights at end of epoch
checkpoint.save(checkpoint_prefix) checkpoint.save(checkpoint_prefix)
# check for improvements in error reduction - otherwise early stopping # check for improvements in error reduction - otherwise early stopping
if early_stopping: if early_stopping:
strike = False strike = False
total_strike = False total_strike = False
total_loss = outputs['encoder_loss'] + outputs['decoder_loss'] + outputs['enc_dec_loss'] + \ total_loss = outputs['encoder_loss'] + outputs['decoder_loss'] + outputs['enc_dec_loss'] + \
outputs['xd_loss'] + outputs['zd_loss'] outputs['xd_loss'] + outputs['zd_loss']
if total_loss < total_lowest_loss: if total_loss < total_lowest_loss:
total_lowest_loss = total_loss total_lowest_loss = total_loss
elif total_loss > TOTAL_LOSS_GRACE_CAP: elif total_loss > TOTAL_LOSS_GRACE_CAP:
@ -571,16 +573,16 @@ def train(dataset: tf.data.Dataset,
pass pass
else: else:
grace_period = GRACE grace_period = GRACE
if grace_period == 0: if grace_period == 0:
break break
if verbose: if verbose:
if grace_period > 0: if grace_period > 0:
print("Training finish!... save model weights") print("Training finish!... save model weights")
if grace_period == 0: if grace_period == 0:
print("Training stopped early!... save model weights") print("Training stopped early!... save model weights")
# save trained models # save trained models
checkpoint.save(checkpoint_prefix) checkpoint.save(checkpoint_prefix)
@ -606,7 +608,6 @@ def _train_one_epoch(epoch: int,
global_step_decoder: tf.Variable, global_step_decoder: tf.Variable,
global_step_enc_dec: tf.Variable, global_step_enc_dec: tf.Variable,
epoch_var: tf.Variable) -> Dict[str, float]: epoch_var: tf.Variable) -> Dict[str, float]:
with summary_ops_v2.always_record_summaries(): with summary_ops_v2.always_record_summaries():
epoch_var.assign(epoch) epoch_var.assign(epoch)
epoch_start_time = time.time() epoch_start_time = time.time()
@ -688,11 +689,11 @@ def _train_one_epoch(epoch: int,
# final losses of epoch # final losses of epoch
outputs = { outputs = {
'decoder_loss': decoder_loss_avg.result(False), 'decoder_loss': decoder_loss_avg.result(False),
'encoder_loss': encoder_loss_avg.result(False), 'encoder_loss': encoder_loss_avg.result(False),
'enc_dec_loss': enc_dec_loss_avg.result(False), 'enc_dec_loss': enc_dec_loss_avg.result(False),
'xd_loss': xd_loss_avg.result(False), 'xd_loss': xd_loss_avg.result(False),
'zd_loss': zd_loss_avg.result(False), 'zd_loss': zd_loss_avg.result(False),
'per_epoch_time': per_epoch_time, 'per_epoch_time': per_epoch_time,
} }