def classifier(config,
pooled_output,
num_labels,
labels,
dropout_prob,
ratio_weight=None,
**kargs):
output_layer = pooled_output
hidden_size = output_layer.shape[-1].value
output_weights = tf.get_variable(
"output_weights", [num_labels, hidden_size],
initializer=tf.truncated_normal_initializer(stddev=0.02))
output_bias = tf.get_variable("output_bias", [num_labels],
initializer=tf.zeros_initializer())
output_layer = tf.nn.dropout(output_layer, keep_prob=1 - dropout_prob)
logits = tf.matmul(output_layer, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
if config.get("label_type", "single_label") == "single_label":
if config.get("loss", "entropy") == "entropy":
print("==standard cross entropy==")
per_example_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits, labels=tf.stop_gradient(labels))
elif config.get("loss", "entropy") == "focal_loss":
print("==multi_label focal loss==")
per_example_loss, _ = loss_utils.focal_loss_multi_v1(config,
logits=logits,
labels=labels)
try:
per_example_loss = loss_utils.weighted_loss_ratio(
config, per_example_loss, labels, ratio_weight)
loss = tf.reduce_sum(per_example_loss)
print(" == applying weighted loss == ")
except:
loss = tf.reduce_mean(per_example_loss)
if config.get("with_center_loss", "no") == "center_loss":
print("==apply with center loss==")
center_loss, _ = loss_utils.center_loss_v2(config,
features=pooled_output,
labels=labels)
loss += center_loss * config.get("center_loss_coef", 1e-3)
return (loss, per_example_loss, logits)
elif config.get("label_type", "single_label") == "multi_label":
logits = tf.log_sigmoid(logits)
per_example_loss = tf.nn.sigmoid_cross_entropy_with_logits(
logits=logits, labels=tf.stop_gradient(labels))
per_example_loss = tf.reduce_sum(per_example_loss, axis=-1)
loss = tf.reduce_mean(per_example_loss)
return (loss, per_example_loss, logits)
else:
raise NotImplementedError()
def order_classifier_v1(config,
output_lst,
num_labels,
labels,
dropout_prob,
ratio_weight=None):
assert len(output_lst) == 2
seq_output_a = output_lst[0]
seq_output_b = output_lst[1]
# batch x (hidden x 2)
# repres = tf.concat([seq_output_a, seq_output_b],
# axis=-1)
repres = seq_output_a + seq_output_b
hidden_size = repres.shape[-1].value
repres = tf.layers.dense(repres,
hidden_size,
activation=tf.nn.tanh,
name="output_dense")
output_weights = tf.get_variable(
"output_weights", [num_labels, hidden_size],
initializer=tf.truncated_normal_initializer(stddev=0.02))
output_bias = tf.get_variable("output_bias", [num_labels],
initializer=tf.zeros_initializer())
output_layer = tf.nn.dropout(repres, keep_prob=1 - dropout_prob)
logits = tf.matmul(output_layer, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
if config.get("label_type", "single_label") == "single_label":
if config.get("loss", "entropy") == "entropy":
per_example_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits, labels=tf.stop_gradient(labels))
elif config.get("loss", "entropy") == "focal_loss":
tf.logging.info("===apply multi-class focal loss===")
print("===apply multi-class focal loss===")
per_example_loss = loss_utils.focal_loss_multi_v1(config,
logits=logits,
labels=labels)
try:
per_example_loss = loss_utils.weighted_loss_ratio(
config, per_example_loss, labels, ratio_weight)
loss = tf.reduce_sum(per_example_loss)
except:
loss = tf.reduce_mean(per_example_loss)
return (loss, per_example_loss, logits)
