def testDecoderFPropDouble(self):
with self.session(use_gpu=False):
tf.set_random_seed(8372749040)
np.random.seed(827374)
p = self._DecoderParams(
vn_config=py_utils.VariationalNoiseParams(None, False, False))
p.dtype = tf.float64
dec = decoder.AsrDecoder(p)
src_seq_len = 5
src_enc = tf.constant(np.random.uniform(size=(src_seq_len, 2, 8)),
tf.float64)
src_enc_padding = tf.constant(
[[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 1.0], [1.0, 1.0]],
dtype=tf.float64)
target_ids = tf.transpose(
tf.constant([[0, 1, 2, 3], [1, 2, 3, 4], [10, 11, 12, 15],
[5, 6, 7, 8], [10, 5, 2, 5]],
dtype=tf.int32))
target_labels = tf.transpose(
tf.constant([[0, 1, 2, 3], [1, 2, 3, 4], [10, 11, 12, 13],
[5, 7, 8, 10], [10, 5, 2, 4]],
dtype=tf.int32))
target_paddings = tf.transpose(
tf.constant([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 1, 0],
[0, 1, 0, 0], [1, 1, 1, 1]],
dtype=tf.float64))
target_transcripts = tf.constant(
['abcd', 'bcde', 'klmp', 'fghi', 'kfcf'])
target_weights = 1.0 - target_paddings
targets = py_utils.NestedMap({
'ids': target_ids,
'labels': target_labels,
'weights': target_weights,
'paddings': target_paddings,
'transcripts': target_transcripts,
})
encoder_outputs = py_utils.NestedMap(encoded=src_enc,
padding=src_enc_padding)
metrics = dec.FPropDefaultTheta(encoder_outputs, targets)
loss = metrics['loss'][0]
tf.global_variables_initializer().run()
test_utils.CompareToGoldenSingleFloat(self, 3.483581, loss.eval())
# Second run to make sure the function is determistic.
test_utils.CompareToGoldenSingleFloat(self, 3.483581, loss.eval())
def _testDecoderFPropHelper(self, params):
"""Computes decoder from params and computes loss with random inputs."""
dec = decoder.AsrDecoder(params)
src_seq_len = 5
src_enc = tf.random_normal([src_seq_len, 2, 8],
seed=982774838,
dtype=py_utils.FPropDtype(params))
src_enc_padding = tf.constant(
[[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 1.0], [1.0, 1.0]],
dtype=py_utils.FPropDtype(params))
encoder_outputs = py_utils.NestedMap(encoded=src_enc,
padding=src_enc_padding)
# shape=[4, 5]
target_ids = tf.transpose(
tf.constant([[0, 1, 2, 3], [1, 2, 3, 4], [10, 11, 12, 15],
[5, 6, 7, 8], [10, 5, 2, 5]],
dtype=tf.int32))
# shape=[4, 5]
target_labels = tf.transpose(
tf.constant([[0, 1, 2, 3], [1, 2, 3, 4], [10, 11, 12, 13],
[5, 7, 8, 10], [10, 5, 2, 4]],
dtype=tf.int32))
# shape=[4, 5]
target_paddings = tf.transpose(
tf.constant([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 1, 0],
[0, 1, 0, 0], [1, 1, 1, 0]],
dtype=py_utils.FPropDtype(params)))
target_transcripts = tf.constant(
['abcd', 'bcde', 'klmp', 'fghi', 'kfcf'])
target_weights = 1.0 - target_paddings
# ids/labels/weights/paddings are all in [batch, time] shape.
targets = py_utils.NestedMap({
'ids': target_ids,
'labels': target_labels,
'weights': target_weights,
'paddings': target_paddings,
'transcripts': target_transcripts,
})
metrics, per_sequence_loss = dec.FPropWithPerExampleLoss(
encoder_outputs, targets)
loss = metrics['loss']
return loss, per_sequence_loss
def testDecoderConstruction(self):
"""Test that decoder can be constructed from params."""
p = self._DecoderParams(
vn_config=py_utils.VariationalNoiseParams(None, True, False))
_ = decoder.AsrDecoder(p)
