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add predict mode to get the representation of user #6

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add predict mode to get the representation of user #6

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36 changes: 35 additions & 1 deletion run.py
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Original file line number Diff line number Diff line change
Expand Up @@ -262,7 +262,8 @@ def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
# all_user_and_item = model.get_embedding_table()
# item_ids = [i for i in range(0, item_size + 1)]
# softmax_output_embedding = tf.nn.embedding_lookup(all_user_and_item, item_ids)

encoder_last_layer = model.get_sequence_output()
encoder_last2_layer = model.all_encoder_layers[-2]
(masked_lm_loss,
masked_lm_example_loss, masked_lm_log_probs) = get_masked_lm_output(
bert_config,
Expand Down Expand Up @@ -348,6 +349,15 @@ def metric_fn(masked_lm_example_loss, masked_lm_log_probs,
loss=total_loss,
eval_metric_ops=eval_metrics,
scaffold=scaffold_fn)

elif mode == tf.estimator.ModeKeys.PREDICT:
predictions = {"input_ids": input_ids,"info":info}
predictions['last_layer_output'] = encoder_last_layer
predictions['last2_layer_output'] = encoder_last2_layer
output_spec = tf.estimator.EstimatorSpec(
mode=mode,
predictions=predictions,
scaffold=scaffold_fn)
else:
raise ValueError("Only TRAIN and EVAL modes are supported: %s" %
(mode))
Expand Down Expand Up @@ -594,6 +604,30 @@ def main(_):
tf.logging.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))

if FLAGS.do_test:
tf.logging.info("***** Running evaluation *****")
tf.logging.info(" Batch size = %d", FLAGS.batch_size)

test_input_fn = input_fn_builder(
input_files=test_input_files,
max_seq_length=FLAGS.max_seq_length,
max_predictions_per_seq=FLAGS.max_predictions_per_seq,
is_training=False)
output_test_file = os.path.join(FLAGS.checkpointDir,
"test_results.txt")
with tf.gfile.Open(output_test_file, 'w') as writer:
# print('result',next(estimator.predict(test_input_fn,yield_single_examples=True)))
for result in estimator.predict(test_input_fn, yield_single_examples=True):
print('result', result['info'], result['input_ids'])
avg = np.array(len(result['last2_layer_output'][0]))
for i in range(len(result['input_ids'])):
if result['input_ids'][i] == 0:
print('early stop', i)
break
avg = (avg * i + result['last_layer_output'][i]) / (i + 1)
print('avg', avg)
writer.write(str(result['info'][0]) + ' ' + ' '.join(str(x) for x in avg.tolist()) + '\n')


if __name__ == "__main__":
flags.mark_flag_as_required("bert_config_file")
Expand Down