def test_trainable_variables(self):
"""Tests the functionality of automatically collecting trainable
variables.
"""
inputs = tf.placeholder(dtype=tf.int32, shape=[None, None])
# case 1: bert base
encoder = BERTEncoder()
_, _ = encoder(inputs)
self.assertEqual(len(encoder.trainable_variables), 3 + 2 + 12 * 16 + 2)
# case 2: bert large
hparams = {
"pretrained_model_name": "bert-large-uncased"
}
encoder = BERTEncoder(hparams=hparams)
_, _ = encoder(inputs)
self.assertEqual(len(encoder.trainable_variables), 3 + 2 + 24 * 16 + 2)
# case 3: self-designed bert
hparams = {
"encoder": {
"num_blocks": 6,
},
"pretrained_model_name": None
}
encoder = BERTEncoder(hparams=hparams)
_, _ = encoder(inputs)
self.assertEqual(len(encoder.trainable_variables), 3 + 2 + 6 * 16 + 2)
def test_model_loading(self):
r"""Tests model loading functionality."""
inputs = tf.placeholder(dtype=tf.int32, shape=[None, None])
for pretrained_model_name in BERTEncoder.available_checkpoints():
encoder = BERTEncoder(pretrained_model_name=pretrained_model_name)
_, _ = encoder(inputs)
def test_encode(self):
"""Tests encoding.
"""
# case 1: bert base
hparams = {
"pretrained_model_name": None
}
encoder = BERTEncoder(hparams=hparams)
max_time = 8
batch_size = 16
inputs = tf.random_uniform([batch_size, max_time],
maxval=30521, dtype=tf.int32)
outputs, pooled_output = encoder(inputs)
outputs_dim = encoder.hparams.encoder.dim
pooled_output_dim = encoder.hparams.hidden_size
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
outputs_, pooled_output_ = sess.run([outputs, pooled_output])
self.assertEqual(outputs_.shape, (batch_size,
max_time, outputs_dim))
self.assertEqual(pooled_output_.shape, (batch_size,
pooled_output_dim))
# case 2: self-designed bert
hparams = {
"hidden_size": 100,
"pretrained_model_name": None
}
encoder = BERTEncoder(hparams=hparams)
max_time = 8
batch_size = 16
inputs = tf.random_uniform([batch_size, max_time],
maxval=30521, dtype=tf.int32)
outputs, pooled_output = encoder(inputs)
outputs_dim = encoder.hparams.encoder.dim
pooled_output_dim = encoder.hparams.hidden_size
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
outputs_, pooled_output_ = sess.run([outputs, pooled_output])
self.assertEqual(outputs_.shape, (batch_size,
max_time, outputs_dim))
self.assertEqual(pooled_output_.shape,
(batch_size, pooled_output_dim))
def __init__(self,
pretrained_model_name=None,
cache_dir=None,
hparams=None):
super(BERTClassifier, self).__init__(hparams=hparams)
with tf.variable_scope(self.variable_scope):
# Creates the underlying encoder
encoder_hparams = dict_fetch(hparams,
BERTEncoder.default_hparams())
if encoder_hparams is not None:
encoder_hparams['name'] = None
self._encoder = BERTEncoder(
pretrained_model_name=pretrained_model_name,
cache_dir=cache_dir,
hparams=encoder_hparams)
# Creates an dropout layer
drop_kwargs = {"rate": self._hparams.dropout}
layer_hparams = {"type": "Dropout", "kwargs": drop_kwargs}
self._dropout_layer = get_layer(hparams=layer_hparams)
# Creates an additional classification layer if needed
self._num_classes = self._hparams.num_classes
if self._num_classes <= 0:
self._logit_layer = None
else:
logit_kwargs = self._hparams.logit_layer_kwargs
if logit_kwargs is None:
logit_kwargs = {}
elif not isinstance(logit_kwargs, HParams):
raise ValueError(
"hparams['logit_layer_kwargs'] must be a dict.")
else:
logit_kwargs = logit_kwargs.todict()
logit_kwargs.update({"units": self._num_classes})
if 'name' not in logit_kwargs:
logit_kwargs['name'] = "logit_layer"
layer_hparams = {"type": "Dense", "kwargs": logit_kwargs}
self._logit_layer = get_layer(hparams=layer_hparams)
def test_hparams(self):
"""Tests the priority of the encoder arch parameter.
"""
inputs = tf.placeholder(dtype=tf.int32, shape=[None, None])
# case 1: set "pretrained_mode_name" by constructor argument
hparams = {
"pretrained_model_name": "bert-large-uncased",
}
encoder = BERTEncoder(pretrained_model_name="bert-base-uncased",
hparams=hparams)
_, _ = encoder(inputs)
self.assertEqual(encoder.hparams.encoder.num_blocks, 12)
# case 2: set "pretrained_mode_name" by hparams
hparams = {
"pretrained_model_name": "bert-large-uncased",
"encoder": {
"num_blocks": 6
}
}
encoder = BERTEncoder(hparams=hparams)
_, _ = encoder(inputs)
self.assertEqual(encoder.hparams.encoder.num_blocks, 24)
# case 3: set to None in both hparams and constructor argument
hparams = {
"pretrained_model_name": None,
"encoder": {
"num_blocks": 6
},
}
encoder = BERTEncoder(hparams=hparams)
_, _ = encoder(inputs)
self.assertEqual(encoder.hparams.encoder.num_blocks, 6)
# case 4: using default hparams
encoder = BERTEncoder()
_, _ = encoder(inputs)
self.assertEqual(encoder.hparams.encoder.num_blocks, 12)
def default_hparams():
r"""Returns a dictionary of hyperparameters with default values.
.. code-block:: python
{
# (1) Same hyperparameters as in BertEncoder
...
# (2) Additional hyperparameters
"num_classes": 2,
"logit_layer_kwargs": None,
"clas_strategy": "cls_time",
"max_seq_length": None,
"dropout": 0.1,
"name": "bert_classifier"
}
Here:
1. Same hyperparameters as in
:class:`~texar.tf.modules.BertEncoder`.
See the :meth:`~texar.tf.modules.BertEncoder.default_hparams`.
An instance of BertEncoder is created for feature extraction.
2. Additional hyperparameters:
`"num_classes"`: int
Number of classes:
- If **> 0**, an additional :tf_main:`Dense <layers/Dense>`
layer is appended to the encoder to compute the logits over
classes.
- If **<= 0**, no dense layer is appended. The number of
classes is assumed to be the final dense layer size of the
encoder.
`"logit_layer_kwargs"`: dict
Keyword arguments for the logit Dense layer constructor,
except for argument "units" which is set to `num_classes`.
Ignored if no extra logit layer is appended.
`"clas_strategy"`: str
The classification strategy, one of:
- **cls_time**: Sequence-level classification based on the
output of the first time step (which is the `CLS` token).
Each sequence has a class.
- **all_time**: Sequence-level classification based on
the output of all time steps. Each sequence has a class.
- **time_wise**: Step-wise classification, i.e., make
classification for each time step based on its output.
`"max_seq_length"`: int, optional
Maximum possible length of input sequences. Required if
`clas_strategy` is `all_time`.
`"dropout"`: float
The dropout rate of the BERT encoder output.
`"name"`: str
Name of the classifier.
"""
hparams = BERTEncoder.default_hparams()
hparams.update({
"num_classes": 2,
"logit_layer_kwargs": None,
"clas_strategy": "cls_time",
"max_seq_length": None,
"dropout": 0.1,
"name": "bert_classifier"
})
return hparams