Move the _BaseFeatureLayer back to Keras.
All its subclasses are in keras/feature_column PiperOrigin-RevId: 312521092 Change-Id: Icba59f4be0299487df5e2fd86ab697ab9e7317b3
This commit is contained in:
Scott Zhu
TensorFlower Gardener
parent
07898e752c
commit
0992a65a5d
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@ -383,117 +383,6 @@ class _StateManagerImplV2(_StateManagerImpl):
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return var
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class _BaseFeaturesLayer(Layer):
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"""Base class for DenseFeatures and SequenceFeatures.
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Defines common methods and helpers.
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Args:
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feature_columns: An iterable containing the FeatureColumns to use as
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inputs to your model.
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expected_column_type: Expected class for provided feature columns.
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trainable: Boolean, whether the layer's variables will be updated via
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gradient descent during training.
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name: Name to give to the DenseFeatures.
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**kwargs: Keyword arguments to construct a layer.
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Raises:
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ValueError: if an item in `feature_columns` doesn't match
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`expected_column_type`.
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"""
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def __init__(self,
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feature_columns,
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expected_column_type,
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trainable,
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name,
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partitioner=None,
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**kwargs):
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super(_BaseFeaturesLayer, self).__init__(
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name=name, trainable=trainable, **kwargs)
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self._feature_columns = _normalize_feature_columns(feature_columns)
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self._state_manager = _StateManagerImpl(self, self.trainable)
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self._partitioner = partitioner
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for column in self._feature_columns:
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if not isinstance(column, expected_column_type):
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raise ValueError(
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'Items of feature_columns must be a {}. '
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'You can wrap a categorical column with an '
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'embedding_column or indicator_column. Given: {}'.format(
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expected_column_type, column))
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def build(self, _):
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for column in self._feature_columns:
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with variable_scope._pure_variable_scope( # pylint: disable=protected-access
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self.name,
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partitioner=self._partitioner):
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with variable_scope._pure_variable_scope( # pylint: disable=protected-access
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_sanitize_column_name_for_variable_scope(column.name)):
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column.create_state(self._state_manager)
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super(_BaseFeaturesLayer, self).build(None)
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def _output_shape(self, input_shape, num_elements):
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"""Computes expected output shape of the layer or a column's dense tensor.
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Args:
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input_shape: Tensor or array with batch shape.
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num_elements: Size of the last dimension of the output.
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Returns:
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Tuple with output shape.
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"""
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raise NotImplementedError('Calling an abstract method.')
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def compute_output_shape(self, input_shape):
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total_elements = 0
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for column in self._feature_columns:
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total_elements += column.variable_shape.num_elements()
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return self._target_shape(input_shape, total_elements)
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def _process_dense_tensor(self, column, tensor):
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"""Reshapes the dense tensor output of a column based on expected shape.
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Args:
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column: A DenseColumn or SequenceDenseColumn object.
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tensor: A dense tensor obtained from the same column.
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Returns:
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Reshaped dense tensor."""
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num_elements = column.variable_shape.num_elements()
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target_shape = self._target_shape(array_ops.shape(tensor), num_elements)
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return array_ops.reshape(tensor, shape=target_shape)
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def _verify_and_concat_tensors(self, output_tensors):
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"""Verifies and concatenates the dense output of several columns."""
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_verify_static_batch_size_equality(output_tensors, self._feature_columns)
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return array_ops.concat(output_tensors, -1)
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def get_config(self):
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# Import here to avoid circular imports.
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from tensorflow.python.feature_column import serialization # pylint: disable=g-import-not-at-top
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column_configs = serialization.serialize_feature_columns(
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self._feature_columns)
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config = {'feature_columns': column_configs}
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config['partitioner'] = generic_utils.serialize_keras_object(
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self._partitioner)
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base_config = super( # pylint: disable=bad-super-call
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_BaseFeaturesLayer, self).get_config()
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return dict(list(base_config.items()) + list(config.items()))
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@classmethod
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def from_config(cls, config, custom_objects=None):
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# Import here to avoid circular imports.
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from tensorflow.python.feature_column import serialization # pylint: disable=g-import-not-at-top
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config_cp = config.copy()
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config_cp['feature_columns'] = serialization.deserialize_feature_columns(
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config['feature_columns'], custom_objects=custom_objects)
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config_cp['partitioner'] = generic_utils.deserialize_keras_object(
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config['partitioner'], custom_objects)
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return cls(**config_cp)
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class _LinearModelLayer(Layer):
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"""Layer that contains logic for `LinearModel`."""
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@ -14,18 +14,32 @@ py_library(
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name = "feature_column",
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srcs = ["__init__.py"],
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deps = [
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":base_feature_layer",
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":dense_features",
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":dense_features_v2",
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":sequence_feature_column",
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],
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)
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py_library(
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name = "base_feature_layer",
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srcs = ["base_feature_layer.py"],
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deps = [
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"//tensorflow/python:array_ops",
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"//tensorflow/python:variable_scope",
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"//tensorflow/python/feature_column:feature_column_v2",
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"//tensorflow/python/keras/engine:base_layer",
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"//tensorflow/python/keras/utils:generic_utils",
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],
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)
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py_library(
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name = "dense_features",
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srcs = [
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"dense_features.py",
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],
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deps = [
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":base_feature_layer",
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"//tensorflow/python:framework_ops",
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"//tensorflow/python:tf_export",
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"//tensorflow/python:util",
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@ -40,6 +54,7 @@ py_library(
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"dense_features_v2.py",
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],
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deps = [
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":base_feature_layer",
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":dense_features",
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"//tensorflow/python:framework_ops",
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"//tensorflow/python:tf_export",
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@ -98,6 +113,7 @@ py_library(
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name = "sequence_feature_column",
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srcs = ["sequence_feature_column.py"],
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deps = [
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":base_feature_layer",
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"//tensorflow/python:array_ops",
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"//tensorflow/python:check_ops",
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"//tensorflow/python:framework_ops",
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@ -0,0 +1,145 @@
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# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""This API defines FeatureColumn abstraction."""
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# This file was originally under tf/python/feature_column, and was moved to
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# Keras package in order to remove the reverse dependency from TF to Keras.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.python.feature_column import feature_column_v2
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from tensorflow.python.keras.engine.base_layer import Layer
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from tensorflow.python.keras.utils import generic_utils
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import variable_scope
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class _BaseFeaturesLayer(Layer):
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"""Base class for DenseFeatures and SequenceFeatures.
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Defines common methods and helpers.
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Args:
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feature_columns: An iterable containing the FeatureColumns to use as
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inputs to your model.
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expected_column_type: Expected class for provided feature columns.
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trainable: Boolean, whether the layer's variables will be updated via
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gradient descent during training.
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name: Name to give to the DenseFeatures.
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**kwargs: Keyword arguments to construct a layer.
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Raises:
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ValueError: if an item in `feature_columns` doesn't match
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`expected_column_type`.
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"""
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def __init__(self,
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feature_columns,
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expected_column_type,
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trainable,
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name,
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partitioner=None,
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**kwargs):
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super(_BaseFeaturesLayer, self).__init__(
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name=name, trainable=trainable, **kwargs)
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self._feature_columns = feature_column_v2._normalize_feature_columns( # pylint: disable=protected-access
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feature_columns)
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self._state_manager = feature_column_v2._StateManagerImpl( # pylint: disable=protected-access
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self, self.trainable)
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self._partitioner = partitioner
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for column in self._feature_columns:
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if not isinstance(column, expected_column_type):
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raise ValueError(
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'Items of feature_columns must be a {}. '
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'You can wrap a categorical column with an '
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'embedding_column or indicator_column. Given: {}'.format(
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expected_column_type, column))
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def build(self, _):
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for column in self._feature_columns:
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with variable_scope._pure_variable_scope( # pylint: disable=protected-access
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self.name,
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partitioner=self._partitioner):
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with variable_scope._pure_variable_scope( # pylint: disable=protected-access
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feature_column_v2._sanitize_column_name_for_variable_scope( # pylint: disable=protected-access
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column.name)):
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column.create_state(self._state_manager)
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super(_BaseFeaturesLayer, self).build(None)
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def _output_shape(self, input_shape, num_elements):
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"""Computes expected output shape of the layer or a column's dense tensor.
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Args:
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input_shape: Tensor or array with batch shape.
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num_elements: Size of the last dimension of the output.
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Returns:
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Tuple with output shape.
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"""
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raise NotImplementedError('Calling an abstract method.')
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def compute_output_shape(self, input_shape):
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total_elements = 0
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for column in self._feature_columns:
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total_elements += column.variable_shape.num_elements()
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return self._target_shape(input_shape, total_elements)
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def _process_dense_tensor(self, column, tensor):
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"""Reshapes the dense tensor output of a column based on expected shape.
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Args:
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column: A DenseColumn or SequenceDenseColumn object.
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tensor: A dense tensor obtained from the same column.
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Returns:
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Reshaped dense tensor.
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"""
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num_elements = column.variable_shape.num_elements()
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target_shape = self._target_shape(array_ops.shape(tensor), num_elements)
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return array_ops.reshape(tensor, shape=target_shape)
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def _verify_and_concat_tensors(self, output_tensors):
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"""Verifies and concatenates the dense output of several columns."""
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feature_column_v2._verify_static_batch_size_equality( # pylint: disable=protected-access
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output_tensors, self._feature_columns)
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return array_ops.concat(output_tensors, -1)
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def get_config(self):
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# Import here to avoid circular imports.
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from tensorflow.python.feature_column import serialization # pylint: disable=g-import-not-at-top
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column_configs = serialization.serialize_feature_columns(
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self._feature_columns)
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config = {'feature_columns': column_configs}
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config['partitioner'] = generic_utils.serialize_keras_object(
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self._partitioner)
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base_config = super( # pylint: disable=bad-super-call
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_BaseFeaturesLayer, self).get_config()
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return dict(list(base_config.items()) + list(config.items()))
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@classmethod
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def from_config(cls, config, custom_objects=None):
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# Import here to avoid circular imports.
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from tensorflow.python.feature_column import serialization # pylint: disable=g-import-not-at-top
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config_cp = config.copy()
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config_cp['feature_columns'] = serialization.deserialize_feature_columns(
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config['feature_columns'], custom_objects=custom_objects)
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config_cp['partitioner'] = generic_utils.deserialize_keras_object(
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config['partitioner'], custom_objects)
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return cls(**config_cp)
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@ -23,12 +23,13 @@ import json
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from tensorflow.python.feature_column import feature_column_v2 as fc
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from tensorflow.python.framework import ops
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from tensorflow.python.keras import backend
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from tensorflow.python.keras.feature_column import base_feature_layer as kfc
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from tensorflow.python.util import serialization
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from tensorflow.python.util.tf_export import keras_export
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@keras_export(v1=['keras.layers.DenseFeatures'])
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class DenseFeatures(fc._BaseFeaturesLayer): # pylint: disable=protected-access
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class DenseFeatures(kfc._BaseFeaturesLayer): # pylint: disable=protected-access
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"""A layer that produces a dense `Tensor` based on given `feature_columns`.
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Generally a single example in training data is described with FeatureColumns.
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@ -20,6 +20,7 @@ from __future__ import print_function
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from tensorflow.python.feature_column import feature_column_v2 as fc
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from tensorflow.python.framework import ops
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from tensorflow.python.keras.feature_column import base_feature_layer as kfc
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from tensorflow.python.keras.feature_column import dense_features
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from tensorflow.python.util.tf_export import keras_export
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@ -92,4 +93,4 @@ class DenseFeatures(dense_features.DenseFeatures):
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column.create_state(self._state_manager)
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# We would like to call Layer.build and not _DenseFeaturesHelper.build.
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# pylint: disable=protected-access
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super(fc._BaseFeaturesLayer, self).build(None) # pylint: disable=bad-super-call
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super(kfc._BaseFeaturesLayer, self).build(None) # pylint: disable=bad-super-call
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@ -24,6 +24,7 @@ from __future__ import print_function
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from tensorflow.python.feature_column import feature_column_v2 as fc
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from tensorflow.python.framework import ops
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from tensorflow.python.keras import backend
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from tensorflow.python.keras.feature_column import base_feature_layer as kfc
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import check_ops
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from tensorflow.python.util.tf_export import keras_export
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@ -32,7 +33,7 @@ from tensorflow.python.util.tf_export import keras_export
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@keras_export('keras.experimental.SequenceFeatures')
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class SequenceFeatures(fc._BaseFeaturesLayer):
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class SequenceFeatures(kfc._BaseFeaturesLayer):
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"""A layer for sequence input.
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All `feature_columns` must be sequence dense columns with the same
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@ -1,7 +1,7 @@
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path: "tensorflow.keras.experimental.SequenceFeatures"
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tf_class {
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is_instance: "<class \'tensorflow.python.keras.feature_column.sequence_feature_column.SequenceFeatures\'>"
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is_instance: "<class \'tensorflow.python.feature_column.feature_column_v2._BaseFeaturesLayer\'>"
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is_instance: "<class \'tensorflow.python.keras.feature_column.base_feature_layer._BaseFeaturesLayer\'>"
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is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
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is_instance: "<class \'tensorflow.python.module.module.Module\'>"
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is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
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@ -1,7 +1,7 @@
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path: "tensorflow.keras.layers.DenseFeatures"
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tf_class {
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is_instance: "<class \'tensorflow.python.keras.feature_column.dense_features.DenseFeatures\'>"
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is_instance: "<class \'tensorflow.python.feature_column.feature_column_v2._BaseFeaturesLayer\'>"
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is_instance: "<class \'tensorflow.python.keras.feature_column.base_feature_layer._BaseFeaturesLayer\'>"
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is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
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is_instance: "<class \'tensorflow.python.module.module.Module\'>"
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is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
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@ -1,7 +1,7 @@
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path: "tensorflow.keras.experimental.SequenceFeatures"
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tf_class {
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is_instance: "<class \'tensorflow.python.keras.feature_column.sequence_feature_column.SequenceFeatures\'>"
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is_instance: "<class \'tensorflow.python.feature_column.feature_column_v2._BaseFeaturesLayer\'>"
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is_instance: "<class \'tensorflow.python.keras.feature_column.base_feature_layer._BaseFeaturesLayer\'>"
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is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
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is_instance: "<class \'tensorflow.python.module.module.Module\'>"
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is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
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@ -2,7 +2,7 @@ path: "tensorflow.keras.layers.DenseFeatures"
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tf_class {
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is_instance: "<class \'tensorflow.python.keras.feature_column.dense_features_v2.DenseFeatures\'>"
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is_instance: "<class \'tensorflow.python.keras.feature_column.dense_features.DenseFeatures\'>"
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is_instance: "<class \'tensorflow.python.feature_column.feature_column_v2._BaseFeaturesLayer\'>"
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is_instance: "<class \'tensorflow.python.keras.feature_column.base_feature_layer._BaseFeaturesLayer\'>"
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is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
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is_instance: "<class \'tensorflow.python.module.module.Module\'>"
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is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
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