def test_combine_mean_std(self):
"""Test that Transpose can be invoked."""
dim = 2
batch_size = 10
mean_tensor = np.random.rand(dim)
std_tensor = np.random.rand(1, )
with self.session() as sess:
mean_tensor = tf.convert_to_tensor(mean_tensor, dtype=tf.float32)
std_tensor = tf.convert_to_tensor(std_tensor, dtype=tf.float32)
out_tensor = CombineMeanStd()(mean_tensor, std_tensor)
out_tensor = out_tensor.eval()
assert out_tensor.shape == (dim, )
def test_combine_mean_std(self):
"""Test that Transpose can be invoked."""
dim = 2
batch_size = 10
mean_tensor = np.random.rand(dim)
std_tensor = np.random.rand(1,)
with self.session() as sess:
mean_tensor = tf.convert_to_tensor(mean_tensor, dtype=tf.float32)
std_tensor = tf.convert_to_tensor(std_tensor, dtype=tf.float32)
out_tensor = CombineMeanStd()(mean_tensor, std_tensor)
out_tensor = out_tensor.eval()
assert out_tensor.shape == (dim,)
def test_CombineMeanStd_pickle(): tg = TensorGraph() feature = Feature(shape=(tg.batch_size, 1)) layer = CombineMeanStd(in_layers=[feature, feature]) tg.add_output(layer) tg.set_loss(layer) tg.build() tg.save()
def build_graph(self):
print("building")
features = Feature(shape=(None, self.n_features))
last_layer = features
for layer_size in self.encoder_layers:
last_layer = Dense(in_layers=last_layer,
activation_fn=tf.nn.elu,
out_channels=layer_size)
self.mean = Dense(in_layers=last_layer,
activation_fn=None,
out_channels=1)
self.std = Dense(in_layers=last_layer,
activation_fn=None,
out_channels=1)
readout = CombineMeanStd([self.mean, self.std], training_only=True)
last_layer = readout
for layer_size in self.decoder_layers:
last_layer = Dense(in_layers=readout,
activation_fn=tf.nn.elu,
out_channels=layer_size)
self.reconstruction = Dense(in_layers=last_layer,
activation_fn=None,
out_channels=self.n_features)
weights = Weights(shape=(None, self.n_features))
reproduction_loss = L2Loss(
in_layers=[features, self.reconstruction, weights])
reproduction_loss = ReduceSum(in_layers=reproduction_loss, axis=0)
global_step = TensorWrapper(self._get_tf("GlobalStep"))
kl_loss = KLDivergenceLoss(
in_layers=[self.mean, self.std, global_step],
annealing_start_step=self.kl_annealing_start_step,
annealing_stop_step=self.kl_annealing_stop_step)
loss = Add(in_layers=[kl_loss, reproduction_loss], weights=[0.5, 1])
self.add_output(self.mean)
self.add_output(self.reconstruction)
self.set_loss(loss)