def __init__(
self,
strategy=None,
**kwargs
):
self.strategy = strategy
autoencoder.__init__(
self,
**kwargs
)
self.ONES = tf.ones(shape=[self.batch_size, 1])
self.ZEROS = tf.zeros(shape=[self.batch_size, 1])
self.adversarial_models = {
'generative_discriminator_real':
{
'variable': None,
'adversarial_item': 'inference',
'adversarial_value': self.ONES
},
'generative_discriminator_fake':
{
'variable': None,
'adversarial_item': 'inference',
'adversarial_value': self.ZEROS
},
'generative_generator_fake':
{
'variable': None,
'adversarial_item': 'inference',
'adversarial_value': self.ONES
}
}
def __init__(self,
name,
inputs_shape,
outputs_shape,
latents_dim,
variables_params,
filepath=None):
autoencoder.__init__(self,
name=name,
inputs_shape=inputs_shape,
outputs_shape=outputs_shape,
latents_dim=latents_dim,
variables_params=variables_params,
filepath=filepath,
model_fn=create_graph)
self.encode_graph = encode_fn
def __init__(self, **kwargs):
kwargs['model_fn'] = create_graph
kwargs['encode_fn'] = encode_fn
autoencoder.__init__(self, **kwargs)
def __init__(self, episode_len, **kwargs):
self.episode_len = episode_len
basicAE.__init__(self, **kwargs)