Created
February 13, 2019 17:26
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BERT - Model Fn Builder
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# 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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