def discriminator_dag_supervised(latent,
dag,
dag_bw,
dag_feats,
sequence_length,
params,
idx,
weights_regularizer=None,
is_training=True):
# latent (N, L, D)
with tf.variable_scope('discriminator'):
h = tf.concat([latent, dag_feats], axis=-1)
with tf.variable_scope("upward"):
h = message_passing(latent=h,
dag_bw=dag_bw,
params=params,
fully_connected_fn=sn_fully_connected,
weights_regularizer=weights_regularizer,
hidden_depth=params.discriminator_layers,
dim=params.discriminator_dim)
with tf.variable_scope("downward"):
h = message_passing(latent=h,
dag_bw=dag,
params=params,
fully_connected_fn=sn_fully_connected,
weights_regularizer=weights_regularizer,
hidden_depth=params.discriminator_layers,
dim=params.discriminator_dim)
with tf.variable_scope('output_mlp'):
if params.lstm_output_discriminator:
h, _ = lstm(x=h,
num_units=params.decoder_dim,
bidirectional=True,
num_layers=params.decoder_layers,
sequence_lengths=sequence_length)
else:
for i in range(params.discriminator_layers):
h = sn_fully_connected(
inputs=h,
activation_fn=tf.nn.leaky_relu,
weights_regularizer=weights_regularizer,
num_outputs=params.discriminator_dim,
scope='discriminator_output_{}'.format(i))
logits = sn_fully_connected(
inputs=h,
num_outputs=1,
activation_fn=None,
scope='discriminator_logits',
weights_regularizer=weights_regularizer) # (N,L,1)
logits = tf.squeeze(logits, axis=-1) # (N, L)
logits_values = tf.gather_nd(params=logits, indices=idx) # (X,)
sparse_logits = tf.SparseTensor(values=logits_values,
indices=tf.cast(idx, tf.int64),
dense_shape=tf.cast(
tf.shape(logits), tf.int64))
sparse_logits = tf.sparse_reorder(sparse_logits)
dis_values = tf.sparse_reduce_sum(
sp_input=sparse_logits, axis=-1) / tf.cast(
sequence_length, tf.float32) # (n,)
return dis_values # (n,)
def discriminator_dag_supervised(
latent, dag, dag_bw, params, idx,
tags, tag_size, weights_regularizer=None,
is_training=True):
# latent (N, L, D)
with tf.variable_scope('decoder'):
with tf.variable_scope('tag_embedding'):
tag_embeddings = sn_kernel(
shape=[tag_size, params.decoder_dim],
scope="tag_embeddings"
)
h_tags = tf.nn.embedding_lookup(params=tag_embeddings, ids=tags)
h = tf.concat([latent, h_tags], axis=-1)
with tf.variable_scope("forward"):
h = message_passing(
latent=h,
dag_bw=dag_bw,
params=params,
fully_connected_fn=sn_fully_connected
)
with tf.variable_scope("backward"):
h = message_passing(
latent=h,
dag_bw=dag,
params=params,
fully_connected_fn=sn_fully_connected
)
with tf.variable_scope('output_mlp'):
h = sn_fully_connected(
inputs=h,
activation_fn=tf.nn.leaky_relu,
num_outputs=params.decoder_dim,
scope='output_1',
weights_regularizer=weights_regularizer
)
logits = sn_fully_connected(
inputs=h,
num_outputs=1,
activation_fn=None,
scope='output_2',
weights_regularizer=weights_regularizer
) # (N,L,1)
logits = tf.squeeze(logits, axis=-1) # (N, L)
logits_values = tf.gather_nd(
params=logits,
indices=idx
) # (X,)
sparse_logits = tf.SparseTensor(
values=logits_values,
indices=tf.cast(idx, tf.int64),
dense_shape=tf.cast(tf.shape(logits), tf.int64)
)
sparse_logits = tf.sparse_reorder(sparse_logits)
dis_values = tf.sparse_reduce_sum(
sp_input=sparse_logits,
axis=-1
) # (n,)
return dis_values # (n,)
def decoder_dag_supervised(latent,
dag,
dag_bw,
sequence_length,
dag_feats,
vocab_size,
params,
weights_regularizer=None,
is_training=True):
# latent (N, L, D)
with tf.variable_scope('decoder'):
h = tf.concat([latent, dag_feats], axis=-1)
with tf.variable_scope("upward"):
h = message_passing(latent=h,
dag_bw=dag_bw,
params=params,
dim=params.decoder_dim,
hidden_depth=params.decoder_layers,
weights_regularizer=weights_regularizer)
with tf.variable_scope("downward"):
h = message_passing(latent=h,
dag_bw=dag,
params=params,
dim=params.decoder_dim,
hidden_depth=params.decoder_layers,
weights_regularizer=weights_regularizer)
with tf.variable_scope('output_mlp'):
if params.lstm_output:
h, _ = lstm(x=h,
num_units=params.decoder_dim,
bidirectional=True,
num_layers=params.decoder_layers,
sequence_lengths=sequence_length)
else:
for i in range(params.decoder_layers):
h = slim.fully_connected(
inputs=h,
activation_fn=tf.nn.leaky_relu,
weights_regularizer=weights_regularizer,
num_outputs=params.decoder_dim,
scope='decoder_output_{}'.format(i))
logits = slim.fully_connected(
inputs=h,
num_outputs=vocab_size,
activation_fn=None,
scope='decoder_output_logits',
weights_regularizer=weights_regularizer,
#biases_initializer=tf.initializers.constant(
# value=get_bias(smoothing=params.bias_smoothing),
# verify_shape=True)
) # (N,L,V)
return logits
def encoder_dag(dag,
dag_bw,
dag_feats,
text,
vocab_size,
params,
weights_regularizer=None):
with tf.variable_scope("encoder"):
text_embeddings = tf.get_variable(
dtype=tf.float32,
name="text_embeddings",
shape=[vocab_size, params.encoder_dim],
initializer=tf.initializers.truncated_normal(
stddev=1. /
tf.sqrt(tf.constant(params.encoder_dim, dtype=tf.float32))))
h_text = tf.nn.embedding_lookup(params=text_embeddings,
ids=text) # (N, L, D)
h = tf.concat([h_text, dag_feats], axis=-1)
with tf.variable_scope("upward"):
h = message_passing(latent=h,
dag_bw=dag_bw,
params=params,
dim=params.encoder_dim,
hidden_depth=params.encoder_layers,
weights_regularizer=weights_regularizer)
with tf.variable_scope("downward"):
h = message_passing(latent=h,
dag_bw=dag,
params=params,
dim=params.encoder_dim,
hidden_depth=params.encoder_layers,
weights_regularizer=weights_regularizer)
for i in range(params.encoder_layers):
h = slim.fully_connected(inputs=h,
activation_fn=tf.nn.leaky_relu,
num_outputs=params.encoder_dim,
scope='encoder_output_{}'.format(i),
weights_regularizer=weights_regularizer)
mu = slim.fully_connected(inputs=h,
num_outputs=params.latent_dim,
activation_fn=None,
scope='encoder_mlp_mu',
weights_regularizer=weights_regularizer)
logsigma = slim.fully_connected(
inputs=h,
num_outputs=params.latent_dim,
activation_fn=None,
scope='encoder_mlp_logsigma',
weights_regularizer=weights_regularizer)
return mu, logsigma
def discriminator_dag(latent, dag, dag_bw, sequence_length, params, weights_regularizer=None,
is_training=True):
# latent (N, L, D)
N = tf.shape(latent)[0]
L = tf.shape(latent)[1]
with tf.variable_scope('discriminator'):
h_linspace = tf.linspace(start=0, stop=L, num=L)
h_linspace = tf.tile(tf.expand_dims(h_linspace, 0), [N, 1])
h_linspace = h_linspace / tf.expand_dims(sequence_length, axis=1)
h = tf.concat([latent, tf.expand_dims(h_linspace, -1)], axis=-1)
with tf.variable_scope("upward"):
h = message_passing(
latent=h,
dag_bw=dag_bw,
params=params,
dim=params.discriminator_dim,
hidden_depth=params.discriminator_layers,
weights_regularizer=weights_regularizer
)
with tf.variable_scope("downward"):
h = message_passing(
latent=h,
dag_bw=dag,
params=params,
dim=params.discriminator_dim,
hidden_depth=params.discriminator_layers,
weights_regularizer=weights_regularizer
)
with tf.variable_scope('output_mlp'):
for i in range(params.discriminator_layers):
h = slim.fully_connected(
inputs=h,
activation_fn=tf.nn.leaky_relu,
weights_regularizer=weights_regularizer,
num_outputs=params.discriminator_dim,
scope='discriminator_output_{}'.format(i)
)
logits = slim.fully_connected(
inputs=h,
num_outputs=1,
activation_fn=None,
scope='discriminator_output_logits',
weights_regularizer=weights_regularizer,
biases_initializer=tf.initializers.constant(
value=get_bias(smoothing=params.bias_smoothing),
verify_shape=True)
) # (N,L,1)
return logits
def encoder_dag(dag,
dag_bw,
text,
tags,
vocab_size,
tags_size,
params,
weights_regularizer=None):
with tf.variable_scope("encoder"):
text_embeddings = tf.get_variable(
dtype=tf.float32,
name="text_embeddings",
shape=[vocab_size, params.encoder_dim],
initializer=tf.initializers.truncated_normal(
stddev=1. /
tf.sqrt(tf.constant(params.encoder_dim, dtype=tf.float32))))
tag_embeddings = tf.get_variable(
dtype=tf.float32,
name="tag_embeddings",
shape=[tags_size, params.encoder_dim],
initializer=tf.initializers.truncated_normal(
stddev=1. /
tf.sqrt(tf.constant(params.encoder_dim, dtype=tf.float32))))
h_text = tf.nn.embedding_lookup(params=text_embeddings,
ids=text) # (L, N, D)
h_tags = tf.nn.embedding_lookup(params=tag_embeddings,
ids=tags) # (L, N, D)
h = tf.concat([h_text, h_tags], axis=-1)
with tf.variable_scope("forward"):
h = message_passing(latent=h, dag_bw=dag_bw, params=params)
with tf.variable_scope("backward"):
h = message_passing(latent=h, dag_bw=dag, params=params)
mu = slim.fully_connected(inputs=h,
num_outputs=params.latent_dim,
activation_fn=None,
scope='encoder_mlp_mu',
weights_regularizer=weights_regularizer)
logsigma = slim.fully_connected(
inputs=h,
num_outputs=params.latent_dim,
activation_fn=None,
scope='encoder_mlp_logsigma',
weights_regularizer=weights_regularizer)
return mu, logsigma
def vae_decoder_dag_supervised(latent,
dag,
dag_bw,
vocab_size,
params,
tags,
tag_size,
weights_regularizer=None,
is_training=True):
# latent (N, L, D)
with tf.variable_scope('decoder'):
tag_embeddings = tf.get_variable(
dtype=tf.float32,
name="tag_embeddings",
shape=[tag_size, params.decoder_dim],
initializer=tf.initializers.truncated_normal(
stddev=1. /
tf.sqrt(tf.constant(params.encoder_dim, dtype=tf.float32))))
h_tags = tf.nn.embedding_lookup(params=tag_embeddings, ids=tags)
h = tf.concat([latent, h_tags], axis=-1)
with tf.variable_scope("forward"):
h = message_passing(latent=h, dag_bw=dag_bw, params=params)
with tf.variable_scope("backward"):
h = message_passing(latent=h, dag_bw=dag, params=params)
with tf.variable_scope('output_mlp'):
h = slim.fully_connected(inputs=h,
activation_fn=tf.nn.leaky_relu,
num_outputs=params.decoder_dim,
scope='output_1')
logits = slim.fully_connected(
inputs=h,
num_outputs=vocab_size,
activation_fn=None,
scope='output_2',
weights_regularizer=weights_regularizer,
biases_initializer=tf.initializers.constant(
value=get_bias(smoothing=params.bias_smoothing),
verify_shape=True)) # (N,L,V)
return logits
def vae_decoder_dag(latent,
sequence_lengths,
vocab_size,
params,
n,
weights_regularizer=None,
is_training=True):
# latent (N, L, D)
with tf.variable_scope('decoder'):
latent_processed = process_latent(latent=latent,
sequence_lengths=sequence_lengths,
params=params)
dag, penalty = make_dag(latent=latent_processed,
sequence_lengths=sequence_lengths,
params=params)
dag_bw = tf.transpose(dag, (0, 2, 1))
hidden = message_passing(latent=latent, dag_bw=dag, params=params)
hidden = message_passing(latent=hidden, dag_bw=dag_bw, params=params)
# hidden (N, L, Dlatent+decoder_dim)
h = slim.fully_connected(inputs=hidden,
activation_fn=tf.nn.leaky_relu,
num_outputs=params.decoder_dim,
scope='output_1')
logits = slim.fully_connected(
inputs=h,
num_outputs=vocab_size,
activation_fn=None,
scope='output_2',
weights_regularizer=weights_regularizer,
biases_initializer=tf.initializers.constant(
value=get_bias(smoothing=params.bias_smoothing),
verify_shape=True)) # (N,L,V)
return logits, penalty
