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| # Copyright 2019 The TensorFlow Authors. All Rights Reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| # ============================================================================== | |
| """Classification network.""" | |
| # pylint: disable=g-classes-have-attributes | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| # from __future__ import google_type_annotations | |
| from __future__ import print_function | |
| import tensorflow as tf | |
| class TokenClassification(tf.keras.Model): | |
| """TokenClassification network head for BERT modeling. | |
| This network implements a simple token classifier head based on a dense layer. | |
| Arguments: | |
| input_width: The innermost dimension of the input tensor to this network. | |
| num_classes: The number of classes that this network should classify to. | |
| activation: The activation, if any, for the dense layer in this network. | |
| initializer: The intializer for the dense layer in this network. Defaults to | |
| a Glorot uniform initializer. | |
| output: The output style for this network. Can be either 'logits' or | |
| 'predictions'. | |
| """ | |
| def __init__(self, | |
| input_width, | |
| num_classes, | |
| initializer='glorot_uniform', | |
| output='logits', | |
| **kwargs): | |
| self._self_setattr_tracking = False | |
| self._config_dict = { | |
| 'input_width': input_width, | |
| 'num_classes': num_classes, | |
| 'initializer': initializer, | |
| 'output': output, | |
| } | |
| sequence_data = tf.keras.layers.Input( | |
| shape=(None, input_width), name='sequence_data', dtype=tf.float32) | |
| self.logits = tf.keras.layers.Dense( | |
| num_classes, | |
| activation=None, | |
| kernel_initializer=initializer, | |
| name='predictions/transform/logits')( | |
| sequence_data) | |
| predictions = tf.keras.layers.Activation(tf.nn.log_softmax)(self.logits) | |
| if output == 'logits': | |
| output_tensors = self.logits | |
| elif output == 'predictions': | |
| output_tensors = predictions | |
| else: | |
| raise ValueError( | |
| ('Unknown `output` value "%s". `output` can be either "logits" or ' | |
| '"predictions"') % output) | |
| super(TokenClassification, self).__init__( | |
| inputs=[sequence_data], outputs=output_tensors, **kwargs) | |
| def get_config(self): | |
| return self._config_dict | |
| def from_config(cls, config, custom_objects=None): | |
| return cls(**config) | |