def make_inputs(self, features, training=None):
inputs = features["char_ids"]
flat_inputs = tf.reshape(inputs, [-1, tf.shape(inputs)[-1]])
embeddings = self._embed(flat_inputs, training)
sequence_length = tf.count_nonzero(flat_inputs, axis=1)
cell = build_cell(1,
self.num_units,
tf.estimator.ModeKeys.TRAIN if training else None,
dropout=self.dropout,
cell_class=self.cell_class)
rnn_outputs, rnn_state = tf.nn.dynamic_rnn(
cell,
embeddings,
sequence_length=sequence_length,
dtype=embeddings.dtype)
if self.encoding == "average":
encoding = tf.reduce_mean(rnn_outputs, axis=1)
elif self.encoding == "last":
encoding = last_encoding_from_state(rnn_state)
outputs = tf.reshape(encoding,
[-1, tf.shape(inputs)[1], self.num_units])
return outputs
def _build(self, features, labels, params, mode, config=None):
with tf.variable_scope("encoder"):
inputs = self.features_inputter.transform_data(
features,
mode=mode,
log_dir=config.model_dir if config is not None else None)
encoder_outputs, encoder_state, _ = self.encoder.encode(
inputs,
sequence_length=self.features_inputter.get_length(features),
mode=mode)
if self.encoding == "average":
encoding = tf.reduce_mean(encoder_outputs, axis=1)
elif self.encoding == "last":
encoding = last_encoding_from_state(encoder_state)
with tf.variable_scope("generator"):
logits = tf.layers.dense(
encoding,
self.labels_inputter.vocabulary_size)
if mode != tf.estimator.ModeKeys.TRAIN:
labels_vocab_rev = self.labels_inputter.vocabulary_lookup_reverse()
classes_prob = tf.nn.softmax(logits)
classes_id = tf.argmax(classes_prob, axis=1)
predictions = {
"classes": labels_vocab_rev.lookup(classes_id)
}
else:
predictions = None
return logits, predictions
def _call(self, features, labels, params, mode):
training = mode == tf.estimator.ModeKeys.TRAIN
if self.features_inputter.has_word():
with tf.variable_scope("encoder"):
inputs = self.features_inputter.get_word(features,
training=training)
to_concat = [inputs]
if self.features_inputter.has_lm():
lm_layers = self.features_inputter.get_lm(features)
lm_layer_weights = tf.nn.softmax(tf.Variable(
tf.random.uniform([lm_layers.shape[-2].value],
maxval=1)),
axis=0)
w = tf.tensordot(lm_layers,
lm_layer_weights,
axes=[[2], [0]])
to_concat.append(w)
inputs = tf.concat(to_concat, axis=-1)
encoder_outputs, encoder_state, _ = self.encoder.encode(
inputs,
sequence_length=self.features_inputter.get_length(
features),
mode=mode)
if self.encoding == "average":
encoding = tf.reduce_mean(encoder_outputs, axis=1)
elif self.encoding == "last":
encoding = last_encoding_from_state(encoder_state)
if self.features_inputter.has_global():
global_features = self.features_inputter.get_global(features)
print(global_features)
encoding = tf.concat([encoding, global_features],
axis=-1) if self.features_inputter.has_word(
) else global_features
encoding = tf.layers.dense(encoding, 256, activation='relu')
with tf.variable_scope("generator"):
logits = tf.layers.dense(encoding, self.number_of_labels)
if mode != tf.estimator.ModeKeys.TRAIN:
prob = tf.math.sigmoid(logits)
predictions = {"probabilities": prob, "labels": tf.round(prob)}
else:
predictions = None
return logits, predictions
def transform(self, inputs, mode):
flat_inputs = tf.reshape(inputs, [-1, tf.shape(inputs)[-1]])
embeddings = self._embed(flat_inputs, mode)
sequence_length = tf.count_nonzero(flat_inputs, axis=1)
cell = build_cell(1,
self.num_units,
mode,
dropout=self.dropout,
cell_class=self.cell_class)
rnn_outputs, rnn_state = tf.nn.dynamic_rnn(
cell,
embeddings,
sequence_length=sequence_length,
dtype=embeddings.dtype)
if self.encoding == "average":
encoding = tf.reduce_mean(rnn_outputs, axis=1)
elif self.encoding == "last":
encoding = last_encoding_from_state(rnn_state)
outputs = tf.reshape(encoding,
[-1, tf.shape(inputs)[1], self.num_units])
return outputs
