def __init__(self, name, configuration, graph=None):
c = configuration
self.configuration = c
AutoEncoder.__init__(self, name, graph, configuration)
with tf.variable_scope(name):
self.z = c.encoder(self.x, **c.encoder_args)
self.vz = c.embedder(self.vx, **c.embedder_args)
self.bottleneck_size = int(self.z.get_shape()[1])
layer = c.decoder(self.z, **c.decoder_args)
c.decoder_args['reuse'] = True
vlayer = c.decoder(self.vz, **c.decoder_args)
c.decoder_args['reuse'] = False
if c.exists_and_is_not_none('close_with_tanh'):
layer = tf.nn.tanh(layer)
vlayer = tf.nn.tanh(vlayer)
self.x_reconstr = tf.reshape(
layer, [-1, self.n_output[0], self.n_output[1]])
self.vx_reconstr = tf.reshape(
vlayer, [-1, self.n_output[0], self.n_output[1]])
self.saver = tf.train.Saver(tf.global_variables(),
max_to_keep=c.saver_max_to_keep)
self._create_loss()
self._setup_optimizer()
# GPU configuration
if hasattr(c, 'allow_gpu_growth'):
growth = c.allow_gpu_growth
else:
growth = True
config = tf.ConfigProto()
config.gpu_options.allow_growth = growth
# Summaries
self.merged_summaries = tf.summary.merge_all()
self.train_writer = tf.summary.FileWriter(
osp.join(configuration.train_dir, 'summaries'), self.graph)
# Initializing the tensor flow variables
self.init = tf.global_variables_initializer()
# Launch the session
self.sess = tf.Session(config=config)
self.sess.run(self.init)
def __init__(self, numpy_rng=None, input=None, n_visible=8, n_hidden=4,
corrupt_level=0.0,
W=None, bhid=None, bvis=None, theano_rng=None,
sparsity=0.05, beta=0.001):
AutoEncoder.__init__(self,
numpy_rng=numpy_rng,
input = input,
n_visible = n_visible,
n_hidden = n_hidden,
sparsity = sparsity,
beta = beta,
W = W,
bhid = bhid,
bvis = bvis
)
if not theano_rng:
theano_rng = RandomStreams(self.numpy_rng.randint(2 ** 3))
self.theano_rng = theano_rng
self.corrupt_level = corrupt_level