def metric_fn(
per_example_loss,
label_ids,
logits,
is_real_example, probabilities):
if FLAGS.data_repeat:
raw_shape = [-1, FLAGS.data_repeat, probabilities.shape[-1].value]
probabilities = tf.reduce_mean(tf.reshape(probabilities, shape=raw_shape), -2)
predictions = tf.argmax(probabilities, axis=-1, output_type=tf.int32)
raw_shape = [-1, FLAGS.data_repeat]
label_ids = tf.reduce_mean(tf.reshape(label_ids, shape=raw_shape), -1)
label_ids = tf.cast(label_ids, dtype=tf.int32)
is_real_example_a = tf.reduce_mean(tf.reshape(is_real_example, shape=raw_shape), -1)
is_real_example_a = tf.cast(is_real_example_a, dtype=tf.int32)
conf_mat = get_metrics_ops(label_ids, predictions, 3, is_real_example_a)
accuracy = tf.metrics.accuracy(
labels=label_ids,
predictions=predictions,
weights=is_real_example_a)
else:
predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
conf_mat = get_metrics_ops(label_ids, predictions, 3, is_real_example)
accuracy = tf.metrics.accuracy(
labels=label_ids,
predictions=predictions,
weights=is_real_example)
# predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
# eval_input_dict = {
# 'labels': label_ids,
# 'predictions': predictions,
# 'weights': is_real_example
# }
# accuracy = tf.metrics.accuracy(**eval_input_dict)
# conf_mat = get_metrics_ops(label_ids, predictions, 3, is_real_example)
loss = tf.metrics.mean(
values=per_example_loss,
weights=is_real_example)
return {
'eval_accuracy': accuracy,
'eval_loss': loss,
"conf_mat": conf_mat}
def metric_fn(
per_example_loss,
label_ids,
logits,
is_real_example):
predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
eval_input_dict = {
'labels': label_ids,
'predictions': predictions,
'weights': is_real_example
}
accuracy = tf.metrics.accuracy(**eval_input_dict)
"""
change
"""
conf_mat = get_metrics_ops(label_ids, predictions, 3, is_real_example)
loss = tf.metrics.mean(
values=per_example_loss,
weights=is_real_example)
return {
'eval_accuracy': accuracy,
'eval_loss': loss,
"conf_mat": conf_mat}
def metric_fn(input_label_ids, input_label_mask, logits,
num_labels, trans_params, sequence_lengths):
predictions, _ = tf.contrib.crf.crf_decode(
logits, trans_params, sequence_lengths)
conf_mat = metrics.get_metrics_ops(input_label_ids,
predictions, num_labels,
input_label_mask)
accuracy = tf.metrics.accuracy(labels=input_label_ids,
predictions=predictions,
weights=input_label_mask)
return {
"eval_accuracy": accuracy,
"eval_cm": conf_mat,
}
def metric_fn(per_example_loss, label_ids, logits,
is_real_example):
predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
conf_mat = get_metrics_ops(label_ids, predictions, 3,
is_real_example)
accuracy = tf.metrics.accuracy(labels=label_ids,
predictions=predictions,
weights=is_real_example)
loss = tf.metrics.mean(values=per_example_loss,
weights=is_real_example)
return {
"eval_accuracy": accuracy,
"eval_loss": loss,
"cf": conf_mat,
}
def metric_fn(per_example_loss, label_ids, logits, num_labels,
is_real_example):
label_ids = tf.argmax(label_ids, axis=-1, output_type=tf.int32)
predicted_labels = tf.argmax(logits,
axis=-1,
output_type=tf.int32)
accuracy = tf.metrics.accuracy(label_ids,
predicted_labels,
weights=is_real_example)
loss = tf.metrics.mean(values=per_example_loss,
weights=is_real_example)
""" 13: 调用metrics模型的ops 函数，
得到包含F1，precision和recall的confusion_matrix。"""
confusion_matrix = get_metrics_ops(label_ids, predicted_labels,
num_labels)
return {
"eval_accuracy": accuracy,
"eval_loss": loss,
"eval_matrix": confusion_matrix
}
