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@dalequark
Created February 13, 2019 17:26
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BERT - Model Fn Builder
# model_fn_builder actually creates our model function
# using the passed parameters for num_labels, learning_rate, etc.
def model_fn_builder(num_labels, learning_rate, num_train_steps,
num_warmup_steps):
"""Returns `model_fn` closure for TPUEstimator."""
def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
"""The `model_fn` for TPUEstimator."""
input_ids = features["input_ids"]
input_mask = features["input_mask"]
segment_ids = features["segment_ids"]
label_ids = features["label_ids"]
is_predicting = (mode == tf.estimator.ModeKeys.PREDICT)
# TRAIN and EVAL
if not is_predicting:
(loss, predicted_labels, log_probs) = create_model(
is_predicting, input_ids, input_mask, segment_ids, label_ids, num_labels)
train_op = bert.optimization.create_optimizer(
loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu=False)
# Calculate evaluation metrics.
def metric_fn(label_ids, predicted_labels):
accuracy = tf.metrics.accuracy(label_ids, predicted_labels)
f1_score = tf.contrib.metrics.f1_score(
label_ids,
predicted_labels)
auc = tf.metrics.auc(
label_ids,
predicted_labels)
recall = tf.metrics.recall(
label_ids,
predicted_labels)
precision = tf.metrics.precision(
label_ids,
predicted_labels)
true_pos = tf.metrics.true_positives(
label_ids,
predicted_labels)
true_neg = tf.metrics.true_negatives(
label_ids,
predicted_labels)
false_pos = tf.metrics.false_positives(
label_ids,
predicted_labels)
false_neg = tf.metrics.false_negatives(
label_ids,
predicted_labels)
return {
"eval_accuracy": accuracy,
"f1_score": f1_score,
"auc": auc,
"precision": precision,
"recall": recall,
"true_positives": true_pos,
"true_negatives": true_neg,
"false_positives": false_pos,
"false_negatives": false_neg
}
eval_metrics = metric_fn(label_ids, predicted_labels)
if mode == tf.estimator.ModeKeys.TRAIN:
return tf.estimator.EstimatorSpec(mode=mode,
loss=loss,
train_op=train_op)
else:
return tf.estimator.EstimatorSpec(mode=mode,
loss=loss,
eval_metric_ops=eval_metrics)
else:
(predicted_labels, log_probs) = create_model(
is_predicting, input_ids, input_mask, segment_ids, label_ids, num_labels)
predictions = {
'probabilities': log_probs,
'labels': predicted_labels
}
return tf.estimator.EstimatorSpec(mode, predictions=predictions)
# Return the actual model function in the closure
return model_fn
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