def build_graph(self):
graph = Graph(self.mode)
logits = graph.build(self.features)
transition_params = tf.get_variable("transitions", [Config.model.fc_unit, Config.model.fc_unit])
viterbi_sequence, _ = tf.contrib.crf.crf_decode(logits, transition_params, self.features['length'])
self.predictions = viterbi_sequence
tf.identity(self.predictions, 'prediction')
if self.mode != tf.estimator.ModeKeys.PREDICT:
self._build_loss(logits, transition_params)
if self.mode == tf.estimator.ModeKeys.TRAIN:
self._build_train_op()
else:
seg_precision = tf.placeholder(tf.float32, None, 'seg_p_ph')
seg_recall = tf.placeholder(tf.float32, None, 'seg_r_ph')
seg_f1_measure = tf.placeholder(tf.float32, None, 'seg_f1_ph')
tf.summary.scalar('seg_precision', seg_precision, ['prf'], 'seg_score')
tf.summary.scalar('seg_recall', seg_recall, ['prf'], 'seg_score')
tf.summary.scalar('seg_f1_measure', seg_f1_measure, ['prf'], 'seg_score')
tag_precision = tf.placeholder(tf.float32, None, 'tag_p_ph')
tag_recall = tf.placeholder(tf.float32, None, 'tag_r_ph')
tag_f1_measure = tf.placeholder(tf.float32, None, 'tag_f1_ph')
tf.summary.scalar('tag_precison', tag_precision, ['prf'], 'tag_score')
tf.summary.scalar('tag_recall', tag_recall, ['prf'], 'tag_score')
tf.summary.scalar('tag_f1_measure', tag_f1_measure, ['prf'], 'tag_score')
def build_graph(self):
graph = Graph(self.mode)
logits = graph.build(self.inputs)
self.predictions = tf.argmax(logits, 1)
if self.mode != tf.estimator.ModeKeys.PREDICT:
self._build_loss(logits)
self._build_train_op()
self._build_metric()
def build_graph(self):
graph = Graph(self.mode)
logits, locs, softmax_logits = graph.build(self.inputs)
softmax_logits_dict = OrderedDict({
f'softmax_feat{n+1}': softmax_logits[n]
for n in range(len(softmax_logits))
})
locs_dict = OrderedDict(
{f'locs_feat{n+1}': locs[n]
for n in range(len(locs))})
softmax_logits_dict.update(locs_dict)
self.predictions = softmax_logits_dict
if self.mode != tf.estimator.ModeKeys.PREDICT:
self._build_loss(logits, locs)
self._build_optimizer()
def build_graph(self):
graph = Graph(self.mode)
outputs = graph.build(self.inputs)
softmax_w = tf.get_variable('w', [Config.model.vocab_num, Config.model.embedding_size], tf.float32,
slim.xavier_initializer())
softmax_b = tf.get_variable('b', [Config.model.vocab_num], tf.float32, tf.constant_initializer(0.0))
if self.mode == tf.estimator.ModeKeys.TRAIN:
self._build_loss(outputs,softmax_w,softmax_b)
self._build_train_op()
else:
for_logits = tf.tensordot(outputs[0], tf.transpose(softmax_w),[[2],[0]])
for_logits = tf.nn.bias_add(for_logits, softmax_b)
for_loss = tf.losses.sparse_softmax_cross_entropy(self.targets['for_labels'],for_logits)
back_logits = tf.tensordot(outputs[1], tf.transpose(softmax_w),[[2],[0]])
back_logits = tf.nn.bias_add(back_logits, softmax_b)
back_loss = tf.losses.sparse_softmax_cross_entropy(self.targets['back_labels'],back_logits)
self.loss = 0.5 * (for_loss + back_loss)
def build_graph(self):
graph = Graph(self.mode)
arc_logits, label_logits = graph.build(self.inputs)
tf.identity(arc_logits, 'arc_logits')
tf.identity(label_logits, 'label_logits')
self.predictions = {
'arc_logits': arc_logits,
'label_logits': label_logits
}
if self.mode != tf.estimator.ModeKeys.PREDICT:
self._build_loss(arc_logits, label_logits)
if self.mode == tf.estimator.ModeKeys.TRAIN:
self._build_train_op()
else:
arc_acc = tf.placeholder(tf.float32, None, 'arc_ph')
label_acc = tf.placeholder(tf.float32, None, 'label_ph')
tf.summary.scalar('UAS', arc_acc, ['acc'], 'score')
tf.summary.scalar('LAS', label_acc, ['acc'], 'score')
