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| # Lint as: python3 | |
| # Copyright 2020 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. | |
| # ============================================================================== | |
| """A Classification head layer which is common used with sequence encoders.""" | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| # from __future__ import google_type_annotations | |
| from __future__ import print_function | |
| import tensorflow as tf | |
| from official.modeling import tf_utils | |
| class ClassificationHead(tf.keras.layers.Layer): | |
| """Pooling head for sentence-level classification tasks.""" | |
| def __init__(self, | |
| inner_dim, | |
| num_classes, | |
| cls_token_idx=0, | |
| activation="tanh", | |
| dropout_rate=0.0, | |
| initializer="glorot_uniform", | |
| **kwargs): | |
| """Initializes the `ClassificationHead`. | |
| Args: | |
| inner_dim: The dimensionality of inner projection layer. | |
| num_classes: Number of output classes. | |
| cls_token_idx: The index inside the sequence to pool. | |
| activation: Dense layer activation. | |
| dropout_rate: Dropout probability. | |
| initializer: Initializer for dense layer kernels. | |
| **kwargs: Keyword arguments. | |
| """ | |
| super(ClassificationHead, self).__init__(**kwargs) | |
| self.dropout_rate = dropout_rate | |
| self.inner_dim = inner_dim | |
| self.num_classes = num_classes | |
| self.activation = tf_utils.get_activation(activation) | |
| self.initializer = tf.keras.initializers.get(initializer) | |
| self.cls_token_idx = cls_token_idx | |
| self.dense = tf.keras.layers.Dense( | |
| units=inner_dim, | |
| activation=self.activation, | |
| kernel_initializer=self.initializer, | |
| name="pooler_dense") | |
| self.dropout = tf.keras.layers.Dropout(rate=self.dropout_rate) | |
| self.out_proj = tf.keras.layers.Dense( | |
| units=num_classes, kernel_initializer=self.initializer, name="logits") | |
| def call(self, features): | |
| x = features[:, self.cls_token_idx, :] # take <CLS> token. | |
| x = self.dense(x) | |
| x = self.dropout(x) | |
| x = self.out_proj(x) | |
| return x | |
| def get_config(self): | |
| config = { | |
| "dropout_rate": self.dropout_rate, | |
| "num_classes": self.num_classes, | |
| "inner_dim": self.inner_dim, | |
| "activation": tf.keras.activations.serialize(self.activation), | |
| "initializer": tf.keras.initializers.serialize(self.initializer), | |
| } | |
| config.update(super(ClassificationHead, self).get_config()) | |
| return config | |
| def from_config(cls, config, custom_objects=None): | |
| return cls(**config) | |
| def checkpoint_items(self): | |
| return {self.dense.name: self.dense} | |