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different learn rates for each component #24

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different learn rates for each component #24

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5 changes: 3 additions & 2 deletions config/gan.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,8 @@ model:
# encoder_hidden_states: [100]
decoder_hidden_states: [1000, 1500]
# decoder_hidden_states: [50]
learn_rate: 0.0035
generator_learn_rate: 0.0035
discriminator_learn_rate: 0.0035
# optimizer: 'gd'
# optimizer: 'adam'
optimizer: 'rmsp'
Expand Down Expand Up @@ -60,6 +61,6 @@ embedding:
sentence:
limit: 300000
# limit: 150
min_length: 6
min_length: 15
# min_length: 3
max_length: 15
9 changes: 5 additions & 4 deletions v1_embedding/gan_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -163,8 +163,9 @@ def _init_discriminator(self):
self.config['discriminator_content']['hidden_states'],
self.discriminator_dropout_placeholder)

def _get_optimizer(self):
learn_rate = self.config['model']['learn_rate']
def _get_optimizer(self, is_generator):
learn_rate = self.config['model']['generator_learn_rate'] if is_generator else self.config['model'][
'discriminator_learn_rate']
if self.config['model']['optimizer'] == 'gd':
return tf.train.GradientDescentOptimizer(learn_rate)
if self.config['model']['optimizer'] == 'adam':
Expand Down Expand Up @@ -215,7 +216,7 @@ def _get_discriminator_train_step(self):
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)

with tf.variable_scope('TrainDiscriminatorSteps'):
discriminator_optimizer = self._get_optimizer()
discriminator_optimizer = self._get_optimizer(False)
discriminator_var_list = self.discriminator.get_trainable_parameters()

discriminator_grads_and_vars = discriminator_optimizer.compute_gradients(
Expand All @@ -233,7 +234,7 @@ def _get_discriminator_train_step(self):

def _get_generator_train_step(self):
with tf.variable_scope('TrainGeneratorSteps'):
generator_optimizer = self._get_optimizer()
generator_optimizer = self._get_optimizer(True)
generator_var_list = self.encoder.get_trainable_parameters() + self.decoder.get_trainable_parameters() + \
self.embedding_container.get_trainable_parameters()
generator_grads_and_vars = generator_optimizer.compute_gradients(
Expand Down