def add_adversarial_loss(self, loss):
## self.adv_embedding. The last element is char embedddings, which need computation
## run char computation
gradients = tf.gradients(loss, self.adv_embedding)
new_embeddings = []
for i, embedding in enumerate(self.adv_embedding):
normalized_gradient = tf.stop_gradient(
gradients[i] /
tf.norm(gradients[i], axis=-1,
keep_dims=True)) ## do not take second-order gradient
epsilon = 0.001 * tf.sqrt(
tf.cast(tf.shape(embedding)[-1], tf.float32))
new_embedding = embedding + epsilon * normalized_gradient
if i == len(self.adv_embedding) - 1: ## char computation
if self.opts.chars_dim > 0:
new_embedding = self.add_dropout(new_embedding,
self.input_keep_prob)
new_embedding = encode_char(
new_embedding, self.char_weights
) ## [seq_len, batch_size, nb_filters]
new_embeddings.append(new_embedding)
inputs_tensor = tf.concat(new_embeddings,
2) ## [seq_len, batch_size, inputs_dim]
adv_loss, projected_outputs = self.feed_network_inputs(inputs_tensor,
adv=True)
return adv_loss, projected_outputs
def add_char_embedding(self):
with tf.device('/cpu:0'):
with tf.variable_scope('char_embedding') as scope:
embedding = tf.get_variable('char_embedding_mat', [self.loader.nb_chars+1, self.opts.chars_dim]) # +1 for padding
inputs = tf.nn.embedding_lookup(embedding, self.inputs_placeholder_dict['chars']) ## [batch_size, seq_len, word_len, embedding_dim]
inputs = tf.transpose(inputs, perm=[1, 0, 2, 3])
## [seq_len, batch_size, word_len, embedding_dim]
inputs = self.add_dropout(inputs, self.input_keep_prob)
weights = get_char_weights(self.opts, 'char_encoding')
inputs = encode_char(inputs, weights) ## [seq_len, batch_size, nb_filters]
return inputs
def add_char_embedding(self):
with tf.device('/cpu:0'):
with tf.variable_scope('char_embedding') as scope:
embedding = tf.get_variable('char_embedding_mat', [self.loader.nb_chars+1, self.opts.chars_dim]) # +1 for padding
inputs = tf.nn.embedding_lookup(embedding, self.inputs_placeholder_dict['chars']) ## [batch_size, seq_len-1, word_len, embedding_dim]
## -1 because we don't have ROOT
inputs = tf.transpose(inputs, perm=[1, 0, 2, 3])
## [seq_len-1, batch_size, word_len, embedding_dim]
inputs = self.add_dropout(inputs, self.input_keep_prob)
weights = get_char_weights(self.opts, 'char_encoding')
inputs = encode_char(inputs, weights) ## [seq_len-1, batch_size, nb_filters]
shape = tf.shape(inputs)
## add 0 vectors for <-root->
inputs = tf.concat([tf.zeros([1, shape[1], shape[2]]), inputs], 0)
return inputs
