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model_helper.py
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model_helper.py
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import os
import tensorflow as tf
import las
import utils
__all__ = [
'las_model_fn',
]
def compute_loss(logits, targets, final_sequence_length, target_sequence_length, mode):
assert mode != tf.estimator.ModeKeys.PREDICT
if mode == tf.estimator.ModeKeys.TRAIN:
target_weights = tf.sequence_mask(
target_sequence_length, dtype=tf.float32)
loss = tf.contrib.seq2seq.sequence_loss(
logits, targets, target_weights)
else:
'''
# Reference: https://github.com/tensorflow/nmt/issues/2
# Note that this method always trim the tensor with larger length to shorter one,
# and I think it is unfair.
# Consider targets = [[3, 3, 2]], and logits with shape [1, 2, VOCAB_SIZE].
# This method will trim targets to [[3, 3]] and compute sequence_loss on new targets and logits.
# However, I think the truth is that the model predicts less word than ground truth does,
# and hence, both targets and logits should be padded to the same sequence length (dimension 1)
# to compute loss.
current_sequence_length = tf.to_int32(
tf.minimum(tf.shape(targets)[1], tf.shape(logits)[1]))
targets = tf.slice(targets, begin=[0, 0],
size=[-1, current_sequence_length])
logits = tf.slice(logits, begin=[0, 0, 0],
size=[-1, current_sequence_length, -1])
target_weights = tf.sequence_mask(
target_sequence_length, maxlen=current_sequence_length, dtype=tf.float32)
loss = tf.contrib.seq2seq.sequence_loss(
logits, targets, target_weights)
'''
max_ts = tf.reduce_max(target_sequence_length)
max_fs = tf.reduce_max(final_sequence_length)
max_sequence_length = tf.to_int32(
tf.maximum(max_ts, max_fs))
logits = tf.slice(logits, begin=[0, 0, 0], size=[-1, max_fs, -1])
# pad EOS to make targets and logits have same shape
targets = tf.pad(targets, [[0, 0], [0, tf.maximum(
0, max_sequence_length - tf.shape(targets)[1])]], constant_values=utils.EOS_ID)
logits = tf.pad(logits, [[0, 0], [0, tf.maximum(
0, max_sequence_length - tf.shape(logits)[1])], [0, 0]], constant_values=0)
# find larger length between predictions and targets
sequence_length = tf.reduce_max(
[target_sequence_length, final_sequence_length], 0)
target_weights = tf.sequence_mask(
sequence_length, maxlen=max_sequence_length, dtype=tf.float32)
loss = tf.contrib.seq2seq.sequence_loss(
logits, targets, target_weights)
return loss
def las_model_fn(features,
labels,
mode,
config,
params):
encoder_inputs = features['encoder_inputs']
source_sequence_length = features['source_sequence_length']
decoder_inputs = None
targets = None
target_sequence_length = None
if mode != tf.estimator.ModeKeys.PREDICT:
decoder_inputs = labels['targets_inputs']
targets = labels['targets_outputs']
target_sequence_length = labels['target_sequence_length']
tf.logging.info('Building listener')
with tf.variable_scope('listener'):
(encoder_outputs, source_sequence_length), encoder_state = las.model.listener(
encoder_inputs, source_sequence_length, mode, params.encoder)
tf.logging.info('Building speller')
with tf.variable_scope('speller'):
decoder_outputs, final_context_state, final_sequence_length = las.model.speller(
encoder_outputs, encoder_state, decoder_inputs,
source_sequence_length, target_sequence_length,
mode, params.decoder)
with tf.name_scope('prediction'):
if mode == tf.estimator.ModeKeys.PREDICT and params.decoder.beam_width > 0:
logits = tf.no_op()
sample_ids = decoder_outputs.predicted_ids
else:
logits = decoder_outputs.rnn_output
sample_ids = tf.to_int32(tf.argmax(logits, -1))
if mode == tf.estimator.ModeKeys.PREDICT:
predictions = {
'sample_ids': sample_ids,
}
return tf.estimator.EstimatorSpec(mode, predictions=predictions)
with tf.name_scope('metrics'):
edit_distance = utils.edit_distance(
sample_ids, targets, utils.EOS_ID, params.mapping)
metrics = {
'edit_distance': tf.metrics.mean(edit_distance),
}
tf.summary.scalar('edit_distance', metrics['edit_distance'][1])
with tf.name_scope('cross_entropy'):
loss = compute_loss(
logits, targets, final_sequence_length, target_sequence_length, mode)
if mode == tf.estimator.ModeKeys.EVAL:
with tf.name_scope('alignment'):
attention_images = utils.create_attention_images(
final_context_state)
attention_summary = tf.summary.image(
'attention_images', attention_images)
eval_summary_hook = tf.train.SummarySaverHook(
save_steps=10,
output_dir=os.path.join(config.model_dir, 'eval'),
summary_op=attention_summary)
logging_hook = tf.train.LoggingTensorHook({
'edit_distance': tf.reduce_mean(edit_distance),
'max_edit_distance': tf.reduce_max(edit_distance),
'max_predictions': sample_ids[tf.argmax(edit_distance)],
'max_targets': targets[tf.argmax(edit_distance)],
'min_edit_distance': tf.reduce_min(edit_distance),
'min_predictions': sample_ids[tf.argmin(edit_distance)],
'min_targets': targets[tf.argmin(edit_distance)],
}, every_n_iter=10)
return tf.estimator.EstimatorSpec(mode, loss=loss, eval_metric_ops=metrics, evaluation_hooks=[logging_hook, eval_summary_hook])
with tf.name_scope('train'):
optimizer = tf.train.AdamOptimizer(params.learning_rate)
train_op = optimizer.minimize(
loss, global_step=tf.train.get_global_step())
logging_hook = tf.train.LoggingTensorHook({
'loss': loss,
'edit_distance': tf.reduce_mean(edit_distance),
#'max_edit_distance': tf.reduce_max(edit_distance),
#'predictions': sample_ids[tf.argmax(edit_distance)],
#'targets': targets[tf.argmax(edit_distance)],
}, every_n_secs=10)
return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op, training_hooks=[logging_hook])