def _sampling(self, z_mean, z_log_var):
"""reparameterization trick
"""
# by default, random_normal has mean=0 and std=1.0
epsilon = layers.gaussian_random_batch_size_like(
self.tar, shape=[-1, self.latent_size])
epsilon.stop_gradient = True
return z_mean + layers.exp(0.5 * z_log_var) * epsilon
def test_gaussian_random_batch_size_like(self):
program = Program()
with program_guard(program):
input = layers.data(name="input", shape=[13, 11], dtype='float32')
out = layers.gaussian_random_batch_size_like(
input, shape=[-1, 11], mean=1.0, std=2.0)
self.assertIsNotNone(out)
print(str(program))
def _build_decoder(self,
z_mean=None,
z_log_var=None,
enc_output=None,
mode='train',
beam_size=10):
dec_input = layers.dropout(self.tar_emb,
dropout_prob=self.dec_dropout_in,
dropout_implementation="upscale_in_train")
# `output_layer` will be used within BeamSearchDecoder
output_layer = lambda x: layers.fc(x,
size=self.tar_vocab_size,
num_flatten_dims=len(x.shape) - 1,
name="output_w")
# `sample_output_layer` samples an id from the logits distribution instead of argmax(logits)
# it will be used within BeamSearchDecoder
sample_output_layer = lambda x: layers.unsqueeze(
fluid.one_hot(layers.unsqueeze(
layers.sampling_id(layers.softmax(
layers.squeeze(output_layer(x), [1])),
dtype='int'), [1]),
depth=self.tar_vocab_size), [1])
if mode == 'train':
latent_z = self._sampling(z_mean, z_log_var)
else:
latent_z = layers.gaussian_random_batch_size_like(
self.tar, shape=[-1, self.latent_size])
dec_first_hidden_cell = layers.fc(latent_z,
2 * self.hidden_size *
self.num_layers,
name='fc_hc')
dec_first_hidden, dec_first_cell = layers.split(
dec_first_hidden_cell, 2)
if self.num_layers > 1:
dec_first_hidden = layers.split(dec_first_hidden, self.num_layers)
dec_first_cell = layers.split(dec_first_cell, self.num_layers)
else:
dec_first_hidden = [dec_first_hidden]
dec_first_cell = [dec_first_cell]
dec_initial_states = [[h, c]
for h, c in zip(dec_first_hidden, dec_first_cell)
]
dec_cell = DecoderCell(self.num_layers, self.hidden_size, latent_z,
self.param_attr_initializer,
self.param_attr_scale, self.dec_dropout_out)
if mode == 'train':
dec_output, _ = rnn(cell=dec_cell,
inputs=dec_input,
initial_states=dec_initial_states,
sequence_length=self.tar_sequence_length)
dec_output = output_layer(dec_output)
return dec_output
elif mode == 'greedy':
start_token = 1
end_token = 2
max_length = 100
beam_search_decoder = BeamSearchDecoder(
dec_cell,
start_token,
end_token,
beam_size=1,
embedding_fn=self.tar_embeder,
output_fn=output_layer)
outputs, _ = dynamic_decode(beam_search_decoder,
inits=dec_initial_states,
max_step_num=max_length)
return outputs
elif mode == 'sampling':
start_token = 1
end_token = 2
max_length = 100
beam_search_decoder = BeamSearchDecoder(
dec_cell,
start_token,
end_token,
beam_size=1,
embedding_fn=self.tar_embeder,
output_fn=sample_output_layer)
outputs, _ = dynamic_decode(beam_search_decoder,
inits=dec_initial_states,
max_step_num=max_length)
return outputs
else:
print("mode not supprt", mode)