9765b5ab7f
The docstrings have been reworded to make them more clear and concise. Some clarifying information is also added. I removed some examples for uncommon use cases in order to shorten the docstrings, and rewrote or shortened other examples to make them easier and faster to read. All references to experimental mixed precision APIs have been changed to use the non-experimental APIs. Examples now use the newly added attribute Layer.dtype_policy as well. The section "How to use float64 in a Keras model" has been removed. Float64 can be enabled by setting floatx to float64 so I don't think its necessary to mention it in the policy section. Also, it's fairly obvious after reading the Policy docstring how to use float64 using policies: Just set the global policy to "float64". I intend on cherrypicking this change into TF 2.4. PiperOrigin-RevId: 339780420 Change-Id: I5f6ad44f54964114c398c306d2d7a4da39bb1c54
3316 lines
129 KiB
Python
3316 lines
129 KiB
Python
# Copyright 2015 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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# pylint: disable=protected-access
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"""Contains the base Layer class, from which all layers inherit."""
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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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import collections
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import copy
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import functools
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import itertools
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import threading
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import warnings
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import weakref
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import numpy as np
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import six
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from six.moves import zip # pylint: disable=redefined-builtin
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from google.protobuf import json_format
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from tensorflow.core.framework import node_def_pb2
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from tensorflow.python import tf2
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from tensorflow.python.autograph.core import ag_ctx
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from tensorflow.python.autograph.impl import api as autograph
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from tensorflow.python.distribute import distribution_strategy_context as ds_context
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from tensorflow.python.eager import context
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from tensorflow.python.eager import def_function
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from tensorflow.python.eager import execute
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from tensorflow.python.eager import monitoring
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import errors
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from tensorflow.python.framework import func_graph
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import sparse_tensor
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from tensorflow.python.framework import tensor_spec
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from tensorflow.python.framework import tensor_util
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from tensorflow.python.keras import backend
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from tensorflow.python.keras import constraints
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from tensorflow.python.keras import initializers
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from tensorflow.python.keras import regularizers
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from tensorflow.python.keras.engine import base_layer_utils
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from tensorflow.python.keras.engine import input_spec
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from tensorflow.python.keras.engine import keras_tensor
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from tensorflow.python.keras.engine import node as node_module
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from tensorflow.python.keras.mixed_precision import autocast_variable
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from tensorflow.python.keras.mixed_precision import loss_scale_optimizer
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from tensorflow.python.keras.mixed_precision import policy
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from tensorflow.python.keras.saving.saved_model import layer_serialization
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from tensorflow.python.keras.utils import generic_utils
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from tensorflow.python.keras.utils import layer_utils
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from tensorflow.python.keras.utils import tf_inspect
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from tensorflow.python.keras.utils import tf_utils
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from tensorflow.python.keras.utils import version_utils
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# A module that only depends on `keras.layers` import these from here.
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from tensorflow.python.keras.utils.generic_utils import to_snake_case # pylint: disable=unused-import
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from tensorflow.python.keras.utils.tf_utils import is_tensor_or_tensor_list # pylint: disable=unused-import
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from tensorflow.python.module import module
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops import resource_variable_ops
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from tensorflow.python.ops import variables as tf_variables
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from tensorflow.python.ops.numpy_ops import np_arrays
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from tensorflow.python.ops.ragged import ragged_tensor
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from tensorflow.python.platform import tf_logging
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from tensorflow.python.training.tracking import base as trackable
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from tensorflow.python.training.tracking import data_structures
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from tensorflow.python.training.tracking import tracking
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from tensorflow.python.util import compat
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from tensorflow.python.util import nest
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from tensorflow.python.util import object_identity
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from tensorflow.python.util.tf_export import keras_export
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from tensorflow.tools.docs import doc_controls
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# pylint: disable=g-inconsistent-quotes
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metrics_mod = generic_utils.LazyLoader(
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"metrics_mod", globals(),
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"tensorflow.python.keras.metrics")
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# pylint: enable=g-inconsistent-quotes
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# Prefix that is added to the TF op layer names.
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_TF_OP_LAYER_NAME_PREFIX = 'tf_op_layer_'
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# TODO(mdan): Should we have a single generic type for types that can be passed
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# to tf.cast?
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_AUTOCAST_TYPES = (ops.Tensor, sparse_tensor.SparseTensor,
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ragged_tensor.RaggedTensor)
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keras_layers_gauge = monitoring.BoolGauge('/tensorflow/api/keras/layers',
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'keras layers usage', 'method')
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keras_models_gauge = monitoring.BoolGauge(
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'/tensorflow/api/keras/models', 'keras model usage', 'method')
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keras_api_gauge = monitoring.BoolGauge('/tensorflow/api/keras',
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'keras api usage', 'method')
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keras_premade_model_gauge = monitoring.BoolGauge(
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'/tensorflow/api/keras/premade_models', 'premade keras model usage', 'type')
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@keras_export('keras.layers.Layer')
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class Layer(module.Module, version_utils.LayerVersionSelector):
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"""This is the class from which all layers inherit.
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A layer is a callable object that takes as input one or more tensors and
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that outputs one or more tensors. It involves *computation*, defined
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in the `call()` method, and a *state* (weight variables), defined
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either in the constructor `__init__()` or in the `build()` method.
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Users will just instantiate a layer and then treat it as a callable.
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Arguments:
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trainable: Boolean, whether the layer's variables should be trainable.
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name: String name of the layer.
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dtype: The dtype of the layer's computations and weights. Can also be a
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`tf.keras.mixed_precision.Policy`, which allows the computation and weight
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dtype to differ. Default of `None` means to use
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`tf.keras.mixed_precision.global_policy()`, which is a float32 policy
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unless set to different value.
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dynamic: Set this to `True` if your layer should only be run eagerly, and
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should not be used to generate a static computation graph.
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This would be the case for a Tree-RNN or a recursive network,
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for example, or generally for any layer that manipulates tensors
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using Python control flow. If `False`, we assume that the layer can
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safely be used to generate a static computation graph.
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Attributes:
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name: The name of the layer (string).
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dtype: The dtype of the layer's weights.
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variable_dtype: Alias of `dtype`.
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compute_dtype: The dtype of the layer's computations. Layers automatically
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cast inputs to this dtype which causes the computations and output to also
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be in this dtype. When mixed precision is used with a
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`tf.keras.mixed_precision.Policy`, this will be different than
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`variable_dtype`.
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dtype_policy: The layer's dtype policy. See the
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`tf.keras.mixed_precision.Policy` documentation for details.
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trainable_weights: List of variables to be included in backprop.
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non_trainable_weights: List of variables that should not be
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included in backprop.
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weights: The concatenation of the lists trainable_weights and
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non_trainable_weights (in this order).
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trainable: Whether the layer should be trained (boolean), i.e. whether
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its potentially-trainable weights should be returned as part of
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`layer.trainable_weights`.
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input_spec: Optional (list of) `InputSpec` object(s) specifying the
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constraints on inputs that can be accepted by the layer.
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We recommend that descendants of `Layer` implement the following methods:
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* `__init__()`: Defines custom layer attributes, and creates layer state
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variables that do not depend on input shapes, using `add_weight()`.
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* `build(self, input_shape)`: This method can be used to create weights that
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depend on the shape(s) of the input(s), using `add_weight()`. `__call__()`
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will automatically build the layer (if it has not been built yet) by
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calling `build()`.
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* `call(self, *args, **kwargs)`: Called in `__call__` after making sure
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`build()` has been called. `call()` performs the logic of applying the
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layer to the input tensors (which should be passed in as argument).
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Two reserved keyword arguments you can optionally use in `call()` are:
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- `training` (boolean, whether the call is in
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inference mode or training mode)
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- `mask` (boolean tensor encoding masked timesteps in the input, used
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in RNN layers)
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* `get_config(self)`: Returns a dictionary containing the configuration used
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to initialize this layer. If the keys differ from the arguments
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in `__init__`, then override `from_config(self)` as well.
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This method is used when saving
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the layer or a model that contains this layer.
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Examples:
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Here's a basic example: a layer with two variables, `w` and `b`,
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that returns `y = w . x + b`.
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It shows how to implement `build()` and `call()`.
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Variables set as attributes of a layer are tracked as weights
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of the layers (in `layer.weights`).
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```python
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class SimpleDense(Layer):
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def __init__(self, units=32):
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super(SimpleDense, self).__init__()
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self.units = units
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def build(self, input_shape): # Create the state of the layer (weights)
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w_init = tf.random_normal_initializer()
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self.w = tf.Variable(
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initial_value=w_init(shape=(input_shape[-1], self.units),
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dtype='float32'),
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trainable=True)
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b_init = tf.zeros_initializer()
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self.b = tf.Variable(
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initial_value=b_init(shape=(self.units,), dtype='float32'),
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trainable=True)
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def call(self, inputs): # Defines the computation from inputs to outputs
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return tf.matmul(inputs, self.w) + self.b
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# Instantiates the layer.
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linear_layer = SimpleDense(4)
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# This will also call `build(input_shape)` and create the weights.
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y = linear_layer(tf.ones((2, 2)))
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assert len(linear_layer.weights) == 2
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# These weights are trainable, so they're listed in `trainable_weights`:
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assert len(linear_layer.trainable_weights) == 2
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```
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Note that the method `add_weight()` offers a shortcut to create weights:
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```python
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class SimpleDense(Layer):
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def __init__(self, units=32):
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super(SimpleDense, self).__init__()
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self.units = units
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def build(self, input_shape):
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self.w = self.add_weight(shape=(input_shape[-1], self.units),
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initializer='random_normal',
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trainable=True)
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self.b = self.add_weight(shape=(self.units,),
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initializer='random_normal',
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trainable=True)
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def call(self, inputs):
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return tf.matmul(inputs, self.w) + self.b
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```
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Besides trainable weights, updated via backpropagation during training,
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layers can also have non-trainable weights. These weights are meant to
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be updated manually during `call()`. Here's a example layer that computes
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the running sum of its inputs:
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```python
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class ComputeSum(Layer):
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def __init__(self, input_dim):
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super(ComputeSum, self).__init__()
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# Create a non-trainable weight.
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self.total = tf.Variable(initial_value=tf.zeros((input_dim,)),
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trainable=False)
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def call(self, inputs):
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self.total.assign_add(tf.reduce_sum(inputs, axis=0))
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return self.total
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my_sum = ComputeSum(2)
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x = tf.ones((2, 2))
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y = my_sum(x)
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print(y.numpy()) # [2. 2.]
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y = my_sum(x)
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print(y.numpy()) # [4. 4.]
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assert my_sum.weights == [my_sum.total]
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assert my_sum.non_trainable_weights == [my_sum.total]
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assert my_sum.trainable_weights == []
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```
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For more information about creating layers, see the guide
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[Writing custom layers and models with Keras](
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https://www.tensorflow.org/guide/keras/custom_layers_and_models)
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"""
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# See tf.Module for the usage of this property.
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# The key for _obj_reference_counts_dict is a Trackable, which could be a
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# variable or layer etc. tf.Module._flatten will fail to flatten the key
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# since it is trying to convert Trackable to a string. This attribute can be
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# ignored even after the fix of nest lib, since the trackable object should
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# already been available as individual attributes. _obj_reference_counts_dict
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# just contains a copy of them.
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_TF_MODULE_IGNORED_PROPERTIES = frozenset(itertools.chain(
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('_obj_reference_counts_dict',),
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module.Module._TF_MODULE_IGNORED_PROPERTIES
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))
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# When loading from a SavedModel, Layers typically can be revived into a
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# generic Layer wrapper. Sometimes, however, layers may implement methods
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# that go beyond this wrapper, as in the case of PreprocessingLayers'
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# `adapt` method. When this is the case, layer implementers can override
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# must_restore_from_config to return True; layers with this property must
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# be restored into their actual objects (and will fail if the object is
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# not available to the restoration code).
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_must_restore_from_config = False
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def _instrument_layer_creation(self):
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self._instrumented_keras_api = False
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self._instrumented_keras_layer_class = False
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self._instrumented_keras_model_class = False
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if not getattr(self, '_disable_keras_instrumentation', False):
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keras_api_gauge.get_cell('layer').set(True)
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self._instrumented_keras_api = True
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if getattr(self, '_is_model_for_instrumentation', False):
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keras_models_gauge.get_cell(self.__class__.__name__).set(True)
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self._instrumented_keras_model_class = True
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else:
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keras_layers_gauge.get_cell(self.__class__.__name__).set(True)
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self._instrumented_keras_layer_class = True
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@trackable.no_automatic_dependency_tracking
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def __init__(self,
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trainable=True,
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name=None,
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dtype=None,
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dynamic=False,
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**kwargs):
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self._instrument_layer_creation()
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# These properties should be set by the user via keyword arguments.
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# note that 'dtype', 'input_shape' and 'batch_input_shape'
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# are only applicable to input layers: do not pass these keywords
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# to non-input layers.
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allowed_kwargs = {
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'input_dim',
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'input_shape',
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'batch_input_shape',
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'batch_size',
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'weights',
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'activity_regularizer',
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'autocast',
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'implementation',
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}
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# Validate optional keyword arguments.
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generic_utils.validate_kwargs(kwargs, allowed_kwargs)
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# Mutable properties
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# Indicates whether the layer's weights are updated during training
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# and whether the layer's updates are run during training.
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self._trainable = trainable
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# A stateful layer is a layer whose updates are run during inference too,
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# for instance stateful RNNs.
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self._stateful = False
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# Indicates whether `build` needs to be called upon layer call, to create
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# the layer's weights.
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self.built = False
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# Record the build input shape for loading purposes.
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# TODO(kathywu): Move this to Layer._set_save_spec once cl/290121460 is
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# submitted.
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self._build_input_shape = None
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self._saved_model_inputs_spec = None
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# Provides information about which inputs are compatible with the layer.
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self._input_spec = None
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# `Layer.compute_mask` will be called at the end of `Layer.__call__` if
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# `Layer.compute_mask` is overridden, or if the `Layer` subclass sets
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# `self.supports_masking=True`.
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self._supports_masking = not generic_utils.is_default(self.compute_mask)
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self._init_set_name(name)
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self._activity_regularizer = regularizers.get(
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kwargs.pop('activity_regularizer', None))
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self._maybe_create_attribute('_trainable_weights', [])
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self._maybe_create_attribute('_non_trainable_weights', [])
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self._updates = []
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# Object to store all thread local layer properties.
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self._thread_local = threading.local()
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# A list of zero-argument lambdas which return Tensors, used for variable
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# regularizers.
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self._callable_losses = []
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# A list of symbolic Tensors containing activity regularizers and losses
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# manually added through `add_loss` in graph-building mode.
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self._losses = []
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# A list of metric instances corresponding to the symbolic metric tensors
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# added using the `add_metric` API.
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self._metrics = []
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# Ensures the same metric is not added multiple times in `MirroredStrategy`.
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self._metrics_lock = threading.Lock()
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# Both graph and subclassed networks have a dtype policy. For graph
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# networks, the policy's compute and variable dtypes are ignored. Such
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# networks only use the policy if it is a PolicyV1, in which case it uses
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# the PolicyV1's loss_scale (Policy does not have a loss_scale). For
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# subclassed networks, the compute and variable dtypes are used as like any
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# ordinary layer.
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self._set_dtype_policy(dtype)
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# Boolean indicating whether the layer automatically casts its inputs to the
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# layer's compute_dtype.
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self._autocast = kwargs.get('autocast',
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base_layer_utils.v2_dtype_behavior_enabled())
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# Dependencies tracked via attribute assignment.
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# All layers in order of horizontal graph traversal.
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# Entries are unique. For models includes input and output layers.
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self._maybe_create_attribute('_layers', [])
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# These lists will be filled via successive calls
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# to self._add_inbound_node().
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# Used in symbolic mode only, only in conjunction with graph-networks
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self._inbound_nodes_value = []
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self._outbound_nodes_value = []
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self._init_call_fn_args()
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# Whether the `call` method can be used to build a TF graph without issues.
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# This attribute has no effect if the model is created using the Functional
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# API. Instead, `model.dynamic` is determined based on the internal layers.
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self._dynamic = dynamic
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# Manage input shape information if passed.
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if 'input_dim' in kwargs and 'input_shape' not in kwargs:
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# Backwards compatibility: alias 'input_dim' to 'input_shape'.
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kwargs['input_shape'] = (kwargs['input_dim'],)
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if 'input_shape' in kwargs or 'batch_input_shape' in kwargs:
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# In this case we will later create an input layer
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# to insert before the current layer
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if 'batch_input_shape' in kwargs:
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batch_input_shape = tuple(kwargs['batch_input_shape'])
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elif 'input_shape' in kwargs:
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if 'batch_size' in kwargs:
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batch_size = kwargs['batch_size']
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else:
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batch_size = None
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batch_input_shape = (batch_size,) + tuple(kwargs['input_shape'])
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self._batch_input_shape = batch_input_shape
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# Manage initial weight values if passed.
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self._initial_weights = kwargs.get('weights', None)
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# Whether the layer will track any layers that is set as attribute on itself
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# as sub-layers, the weights from the sub-layers will be included in the
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# parent layer's variables() as well.
|
|
# Default to True, which means auto tracking is turned on. Certain subclass
|
|
# might want to turn it off, like Sequential model.
|
|
self._auto_track_sub_layers = True
|
|
|
|
# For backwards compat reasons, most built-in layers do not guarantee
|
|
# That they will 100% preserve the structure of input args when saving
|
|
# / loading configs. E.g. they may un-nest an arg that is
|
|
# a list with one element.
|
|
self._preserve_input_structure_in_config = False
|
|
|
|
@trackable.no_automatic_dependency_tracking
|
|
@generic_utils.default
|
|
def build(self, input_shape):
|
|
"""Creates the variables of the layer (optional, for subclass implementers).
|
|
|
|
This is a method that implementers of subclasses of `Layer` or `Model`
|
|
can override if they need a state-creation step in-between
|
|
layer instantiation and layer call.
|
|
|
|
This is typically used to create the weights of `Layer` subclasses.
|
|
|
|
Arguments:
|
|
input_shape: Instance of `TensorShape`, or list of instances of
|
|
`TensorShape` if the layer expects a list of inputs
|
|
(one instance per input).
|
|
"""
|
|
# Only record the build input shapes of overridden build methods.
|
|
if not hasattr(self.build, '_is_default'):
|
|
self._build_input_shape = input_shape
|
|
self.built = True
|
|
|
|
@doc_controls.for_subclass_implementers
|
|
def call(self, inputs, **kwargs): # pylint: disable=unused-argument
|
|
"""This is where the layer's logic lives.
|
|
|
|
Note here that `call()` method in `tf.keras` is little bit different
|
|
from `keras` API. In `keras` API, you can pass support masking for
|
|
layers as additional arguments. Whereas `tf.keras` has `compute_mask()`
|
|
method to support masking.
|
|
|
|
Arguments:
|
|
inputs: Input tensor, or list/tuple of input tensors.
|
|
**kwargs: Additional keyword arguments. Currently unused.
|
|
|
|
Returns:
|
|
A tensor or list/tuple of tensors.
|
|
"""
|
|
return inputs
|
|
|
|
@doc_controls.for_subclass_implementers
|
|
def _add_trackable(self, trackable_object, trainable):
|
|
"""Adds a Trackable object to this layer's state.
|
|
|
|
Arguments:
|
|
trackable_object: The tf.tracking.Trackable object to add.
|
|
trainable: Boolean, whether the variable should be part of the layer's
|
|
"trainable_variables" (e.g. variables, biases) or
|
|
"non_trainable_variables" (e.g. BatchNorm mean and variance).
|
|
|
|
Returns:
|
|
The TrackableWeightHandler used to track this object.
|
|
"""
|
|
handler = base_layer_utils.TrackableWeightHandler(trackable_object)
|
|
if trainable:
|
|
self._trainable_weights.append(handler)
|
|
else:
|
|
self._non_trainable_weights.append(handler)
|
|
return handler
|
|
|
|
@doc_controls.for_subclass_implementers
|
|
def add_weight(self,
|
|
name=None,
|
|
shape=None,
|
|
dtype=None,
|
|
initializer=None,
|
|
regularizer=None,
|
|
trainable=None,
|
|
constraint=None,
|
|
use_resource=None,
|
|
synchronization=tf_variables.VariableSynchronization.AUTO,
|
|
aggregation=tf_variables.VariableAggregation.NONE,
|
|
**kwargs):
|
|
"""Adds a new variable to the layer.
|
|
|
|
Arguments:
|
|
name: Variable name.
|
|
shape: Variable shape. Defaults to scalar if unspecified.
|
|
dtype: The type of the variable. Defaults to `self.dtype`.
|
|
initializer: Initializer instance (callable).
|
|
regularizer: Regularizer instance (callable).
|
|
trainable: Boolean, whether the variable should be part of the layer's
|
|
"trainable_variables" (e.g. variables, biases)
|
|
or "non_trainable_variables" (e.g. BatchNorm mean and variance).
|
|
Note that `trainable` cannot be `True` if `synchronization`
|
|
is set to `ON_READ`.
|
|
constraint: Constraint instance (callable).
|
|
use_resource: Whether to use `ResourceVariable`.
|
|
synchronization: Indicates when a distributed a variable will be
|
|
aggregated. Accepted values are constants defined in the class
|
|
`tf.VariableSynchronization`. By default the synchronization is set to
|
|
`AUTO` and the current `DistributionStrategy` chooses
|
|
when to synchronize. If `synchronization` is set to `ON_READ`,
|
|
`trainable` must not be set to `True`.
|
|
aggregation: Indicates how a distributed variable will be aggregated.
|
|
Accepted values are constants defined in the class
|
|
`tf.VariableAggregation`.
|
|
**kwargs: Additional keyword arguments. Accepted values are `getter`,
|
|
`collections`, `experimental_autocast` and `caching_device`.
|
|
|
|
Returns:
|
|
The variable created.
|
|
|
|
Raises:
|
|
ValueError: When giving unsupported dtype and no initializer or when
|
|
trainable has been set to True with synchronization set as `ON_READ`.
|
|
"""
|
|
if shape is None:
|
|
shape = ()
|
|
kwargs.pop('partitioner', None) # Ignored.
|
|
# Validate optional keyword arguments.
|
|
for kwarg in kwargs:
|
|
if kwarg not in ['collections', 'experimental_autocast',
|
|
'caching_device', 'getter']:
|
|
raise TypeError('Unknown keyword argument:', kwarg)
|
|
collections_arg = kwargs.pop('collections', None)
|
|
# 'experimental_autocast' can be set to False by the caller to indicate an
|
|
# AutoCastVariable should never be created.
|
|
autocast = kwargs.pop('experimental_autocast', True)
|
|
# See the docstring for tf.Variable about the details for caching_device.
|
|
caching_device = kwargs.pop('caching_device', None)
|
|
|
|
if dtype is None:
|
|
dtype = self.dtype or backend.floatx()
|
|
dtype = dtypes.as_dtype(dtype)
|
|
if self._dtype_policy.variable_dtype is None:
|
|
# The policy is "_infer", so we infer the policy from the variable dtype.
|
|
self._set_dtype_policy(policy.Policy(dtype.base_dtype.name))
|
|
initializer = initializers.get(initializer)
|
|
regularizer = regularizers.get(regularizer)
|
|
constraint = constraints.get(constraint)
|
|
|
|
if synchronization == tf_variables.VariableSynchronization.ON_READ:
|
|
if trainable:
|
|
raise ValueError(
|
|
'Synchronization value can be set to '
|
|
'VariableSynchronization.ON_READ only for non-trainable variables. '
|
|
'You have specified trainable=True and '
|
|
'synchronization=VariableSynchronization.ON_READ.')
|
|
else:
|
|
# Set trainable to be false when variable is to be synced on read.
|
|
trainable = False
|
|
elif trainable is None:
|
|
trainable = True
|
|
|
|
# Initialize variable when no initializer provided
|
|
if initializer is None:
|
|
# If dtype is DT_FLOAT, provide a uniform unit scaling initializer
|
|
if dtype.is_floating:
|
|
initializer = initializers.get('glorot_uniform')
|
|
# If dtype is DT_INT/DT_UINT, provide a default value `zero`
|
|
# If dtype is DT_BOOL, provide a default value `FALSE`
|
|
elif dtype.is_integer or dtype.is_unsigned or dtype.is_bool:
|
|
initializer = initializers.get('zeros')
|
|
# NOTES:Do we need to support for handling DT_STRING and DT_COMPLEX here?
|
|
else:
|
|
raise ValueError('An initializer for variable %s of type %s is required'
|
|
' for layer %s' % (name, dtype.base_dtype, self.name))
|
|
|
|
getter = kwargs.pop('getter', base_layer_utils.make_variable)
|
|
if (autocast and
|
|
self._dtype_policy.compute_dtype != self._dtype_policy.variable_dtype
|
|
and dtype.is_floating):
|
|
old_getter = getter
|
|
# Wrap variable constructor to return an AutoCastVariable.
|
|
def getter(*args, **kwargs): # pylint: disable=function-redefined
|
|
variable = old_getter(*args, **kwargs)
|
|
return autocast_variable.create_autocast_variable(variable)
|
|
# Also the caching_device does not work with the mixed precision API,
|
|
# disable it if it is specified.
|
|
# TODO(b/142020079): Reenable it once the bug is fixed.
|
|
if caching_device is not None:
|
|
tf_logging.warn('`caching_device` does not work with mixed precision '
|
|
'API. Ignoring user specified `caching_device`.')
|
|
caching_device = None
|
|
|
|
variable = self._add_variable_with_custom_getter(
|
|
name=name,
|
|
shape=shape,
|
|
# TODO(allenl): a `make_variable` equivalent should be added as a
|
|
# `Trackable` method.
|
|
getter=getter,
|
|
# Manage errors in Layer rather than Trackable.
|
|
overwrite=True,
|
|
initializer=initializer,
|
|
dtype=dtype,
|
|
constraint=constraint,
|
|
trainable=trainable,
|
|
use_resource=use_resource,
|
|
collections=collections_arg,
|
|
synchronization=synchronization,
|
|
aggregation=aggregation,
|
|
caching_device=caching_device)
|
|
if regularizer is not None:
|
|
# TODO(fchollet): in the future, this should be handled at the
|
|
# level of variable creation, and weight regularization losses
|
|
# should be variable attributes.
|
|
name_in_scope = variable.name[:variable.name.find(':')]
|
|
self._handle_weight_regularization(name_in_scope,
|
|
variable,
|
|
regularizer)
|
|
if base_layer_utils.is_split_variable(variable):
|
|
for v in variable:
|
|
backend.track_variable(v)
|
|
if trainable:
|
|
self._trainable_weights.append(v)
|
|
else:
|
|
self._non_trainable_weights.append(v)
|
|
else:
|
|
backend.track_variable(variable)
|
|
if trainable:
|
|
self._trainable_weights.append(variable)
|
|
else:
|
|
self._non_trainable_weights.append(variable)
|
|
return variable
|
|
|
|
@generic_utils.default
|
|
def get_config(self):
|
|
"""Returns the config of the layer.
|
|
|
|
A layer config is a Python dictionary (serializable)
|
|
containing the configuration of a layer.
|
|
The same layer can be reinstantiated later
|
|
(without its trained weights) from this configuration.
|
|
|
|
The config of a layer does not include connectivity
|
|
information, nor the layer class name. These are handled
|
|
by `Network` (one layer of abstraction above).
|
|
|
|
Returns:
|
|
Python dictionary.
|
|
"""
|
|
all_args = tf_inspect.getfullargspec(self.__init__).args
|
|
config = {
|
|
'name': self.name,
|
|
'trainable': self.trainable,
|
|
}
|
|
if hasattr(self, '_batch_input_shape'):
|
|
config['batch_input_shape'] = self._batch_input_shape
|
|
config['dtype'] = policy.serialize(self._dtype_policy)
|
|
if hasattr(self, 'dynamic'):
|
|
# Only include `dynamic` in the `config` if it is `True`
|
|
if self.dynamic:
|
|
config['dynamic'] = self.dynamic
|
|
elif 'dynamic' in all_args:
|
|
all_args.remove('dynamic')
|
|
expected_args = config.keys()
|
|
# Finds all arguments in the `__init__` that are not in the config:
|
|
extra_args = [arg for arg in all_args if arg not in expected_args]
|
|
# Check that either the only argument in the `__init__` is `self`,
|
|
# or that `get_config` has been overridden:
|
|
if len(extra_args) > 1 and hasattr(self.get_config, '_is_default'):
|
|
raise NotImplementedError('Layer %s has arguments in `__init__` and '
|
|
'therefore must override `get_config`.' %
|
|
self.__class__.__name__)
|
|
return config
|
|
|
|
@classmethod
|
|
def from_config(cls, config):
|
|
"""Creates a layer from its config.
|
|
|
|
This method is the reverse of `get_config`,
|
|
capable of instantiating the same layer from the config
|
|
dictionary. It does not handle layer connectivity
|
|
(handled by Network), nor weights (handled by `set_weights`).
|
|
|
|
Arguments:
|
|
config: A Python dictionary, typically the
|
|
output of get_config.
|
|
|
|
Returns:
|
|
A layer instance.
|
|
"""
|
|
return cls(**config)
|
|
|
|
def compute_output_shape(self, input_shape):
|
|
"""Computes the output shape of the layer.
|
|
|
|
If the layer has not been built, this method will call `build` on the
|
|
layer. This assumes that the layer will later be used with inputs that
|
|
match the input shape provided here.
|
|
|
|
Arguments:
|
|
input_shape: Shape tuple (tuple of integers)
|
|
or list of shape tuples (one per output tensor of the layer).
|
|
Shape tuples can include None for free dimensions,
|
|
instead of an integer.
|
|
|
|
Returns:
|
|
An input shape tuple.
|
|
"""
|
|
if context.executing_eagerly():
|
|
# In this case we build the model first in order to do shape inference.
|
|
# This is acceptable because the framework only calls
|
|
# `compute_output_shape` on shape values that the layer would later be
|
|
# built for. It would however cause issues in case a user attempts to
|
|
# use `compute_output_shape` manually with shapes that are incompatible
|
|
# with the shape the Layer will be called on (these users will have to
|
|
# implement `compute_output_shape` themselves).
|
|
self._maybe_build(input_shape)
|
|
with func_graph.FuncGraph(str(self.name) + '_scratch_graph').as_default():
|
|
input_shape = tf_utils.convert_shapes(input_shape, to_tuples=False)
|
|
def _make_placeholder_like(shape):
|
|
ph = backend.placeholder(shape=shape, dtype=self.dtype)
|
|
ph._keras_mask = None
|
|
return ph
|
|
inputs = nest.map_structure(_make_placeholder_like, input_shape)
|
|
try:
|
|
outputs = self(inputs, training=False)
|
|
except TypeError as e:
|
|
six.raise_from(
|
|
NotImplementedError(
|
|
'We could not automatically infer the static shape of the '
|
|
'layer\'s output. Please implement the '
|
|
'`compute_output_shape` method on your layer (%s).' %
|
|
self.__class__.__name__), e)
|
|
return nest.map_structure(lambda t: t.shape, outputs)
|
|
raise NotImplementedError(
|
|
'Please run in eager mode or implement the `compute_output_shape` '
|
|
'method on your layer (%s).' % self.__class__.__name__)
|
|
|
|
@doc_controls.for_subclass_implementers
|
|
def compute_output_signature(self, input_signature):
|
|
"""Compute the output tensor signature of the layer based on the inputs.
|
|
|
|
Unlike a TensorShape object, a TensorSpec object contains both shape
|
|
and dtype information for a tensor. This method allows layers to provide
|
|
output dtype information if it is different from the input dtype.
|
|
For any layer that doesn't implement this function,
|
|
the framework will fall back to use `compute_output_shape`, and will
|
|
assume that the output dtype matches the input dtype.
|
|
|
|
Args:
|
|
input_signature: Single TensorSpec or nested structure of TensorSpec
|
|
objects, describing a candidate input for the layer.
|
|
|
|
Returns:
|
|
Single TensorSpec or nested structure of TensorSpec objects, describing
|
|
how the layer would transform the provided input.
|
|
|
|
Raises:
|
|
TypeError: If input_signature contains a non-TensorSpec object.
|
|
"""
|
|
def check_type_return_shape(s):
|
|
if not isinstance(s, tensor_spec.TensorSpec):
|
|
raise TypeError(
|
|
'Only TensorSpec signature types are supported, '
|
|
'but saw signature signature entry: {}.'.format(s))
|
|
return s.shape
|
|
input_shape = nest.map_structure(check_type_return_shape, input_signature)
|
|
output_shape = self.compute_output_shape(input_shape)
|
|
dtype = self._compute_dtype
|
|
if dtype is None:
|
|
input_dtypes = [s.dtype for s in nest.flatten(input_signature)]
|
|
# Default behavior when self.dtype is None, is to use the first input's
|
|
# dtype.
|
|
dtype = input_dtypes[0]
|
|
return nest.map_structure(
|
|
lambda s: tensor_spec.TensorSpec(dtype=dtype, shape=s),
|
|
output_shape)
|
|
|
|
def _keras_tensor_symbolic_call(self, inputs, input_masks, args, kwargs):
|
|
if self.dynamic:
|
|
# We will use static shape inference to return symbolic tensors
|
|
# matching the specifications of the layer outputs.
|
|
# Since `self.dynamic` is True, we will never attempt to
|
|
# run the underlying TF graph (which is disconnected).
|
|
# TODO(fchollet): consider py_func as an alternative, which
|
|
# would enable us to run the underlying graph if needed.
|
|
input_signature = nest.map_structure(
|
|
lambda x: tensor_spec.TensorSpec(shape=x.shape, dtype=x.dtype),
|
|
inputs)
|
|
output_signature = self.compute_output_signature(input_signature)
|
|
return nest.map_structure(keras_tensor.KerasTensor, output_signature)
|
|
else:
|
|
return self._infer_output_signature(inputs, args, kwargs, input_masks)
|
|
|
|
def _infer_output_signature(self, inputs, args, kwargs, input_masks):
|
|
"""TODO(kaftan): Docstring."""
|
|
|
|
call_fn = self.call
|
|
# Wrapping `call` function in autograph to allow for dynamic control
|
|
# flow and control dependencies in call. We are limiting this to
|
|
# subclassed layers as autograph is strictly needed only for
|
|
# subclassed layers and models.
|
|
# tf_convert will respect the value of autograph setting in the
|
|
# enclosing tf.function, if any.
|
|
if (base_layer_utils.is_subclassed(self) and
|
|
not base_layer_utils.from_saved_model(self)):
|
|
call_fn = autograph.tf_convert(self.call, ag_ctx.control_status_ctx())
|
|
|
|
# We enter a scratch graph and build placeholder inputs inside of it that
|
|
# match the input args.
|
|
# We then call the layer inside of the scratch graph to identify the
|
|
# output signatures, then we build KerasTensors corresponding to those
|
|
# outputs.
|
|
scratch_graph = func_graph.FuncGraph(str(self.name) + '_scratch_graph')
|
|
with scratch_graph.as_default():
|
|
inputs = nest.map_structure(
|
|
keras_tensor.keras_tensor_to_placeholder, inputs)
|
|
args = nest.map_structure(
|
|
keras_tensor.keras_tensor_to_placeholder, args)
|
|
kwargs = nest.map_structure(
|
|
keras_tensor.keras_tensor_to_placeholder, kwargs)
|
|
input_masks = nest.map_structure(
|
|
keras_tensor.keras_tensor_to_placeholder, input_masks)
|
|
|
|
inputs = self._maybe_cast_inputs(inputs)
|
|
|
|
with backend.name_scope(self._name_scope()):
|
|
with autocast_variable.enable_auto_cast_variables(
|
|
self._compute_dtype_object):
|
|
# Build layer if applicable (if the `build` method has been
|
|
# overridden).
|
|
# TODO(kaftan): do we maybe_build here, or have we already done it?
|
|
self._maybe_build(inputs)
|
|
outputs = call_fn(inputs, *args, **kwargs)
|
|
|
|
self._handle_activity_regularization(inputs, outputs)
|
|
self._set_mask_metadata(inputs, outputs, input_masks,
|
|
build_graph=False)
|
|
outputs = nest.map_structure(
|
|
keras_tensor.keras_tensor_from_tensor, outputs)
|
|
|
|
if hasattr(self, '_set_inputs') and not self.inputs:
|
|
# TODO(kaftan): figure out if we ned to do this at all
|
|
# Subclassed network: explicitly set metadata normally set by
|
|
# a call to self._set_inputs().
|
|
self._set_inputs(inputs, outputs)
|
|
del scratch_graph
|
|
return outputs
|
|
|
|
@generic_utils.default
|
|
def compute_mask(self, inputs, mask=None): # pylint: disable=unused-argument
|
|
"""Computes an output mask tensor.
|
|
|
|
Arguments:
|
|
inputs: Tensor or list of tensors.
|
|
mask: Tensor or list of tensors.
|
|
|
|
Returns:
|
|
None or a tensor (or list of tensors,
|
|
one per output tensor of the layer).
|
|
"""
|
|
if not self._supports_masking:
|
|
if any(m is not None for m in nest.flatten(mask)):
|
|
raise TypeError('Layer ' + self.name + ' does not support masking, '
|
|
'but was passed an input_mask: ' + str(mask))
|
|
# masking not explicitly supported: return None as mask.
|
|
return None
|
|
# if masking is explicitly supported, by default
|
|
# carry over the input mask
|
|
return mask
|
|
|
|
def __call__(self, *args, **kwargs):
|
|
"""Wraps `call`, applying pre- and post-processing steps.
|
|
|
|
Arguments:
|
|
*args: Positional arguments to be passed to `self.call`.
|
|
**kwargs: Keyword arguments to be passed to `self.call`.
|
|
|
|
Returns:
|
|
Output tensor(s).
|
|
|
|
Note:
|
|
- The following optional keyword arguments are reserved for specific uses:
|
|
* `training`: Boolean scalar tensor of Python boolean indicating
|
|
whether the `call` is meant for training or inference.
|
|
* `mask`: Boolean input mask.
|
|
- If the layer's `call` method takes a `mask` argument (as some Keras
|
|
layers do), its default value will be set to the mask generated
|
|
for `inputs` by the previous layer (if `input` did come from
|
|
a layer that generated a corresponding mask, i.e. if it came from
|
|
a Keras layer with masking support.
|
|
|
|
Raises:
|
|
ValueError: if the layer's `call` method returns None (an invalid value).
|
|
RuntimeError: if `super().__init__()` was not called in the constructor.
|
|
"""
|
|
if not hasattr(self, '_thread_local'):
|
|
raise RuntimeError(
|
|
'You must call `super().__init__()` in the layer constructor.')
|
|
|
|
# `inputs` (the first arg in the method spec) is special cased in
|
|
# layer call due to historical reasons.
|
|
# This special casing currently takes the form of:
|
|
# - 'inputs' must be explicitly passed. A layer cannot have zero arguments,
|
|
# and inputs cannot have been provided via the default value of a kwarg.
|
|
# - numpy/scalar values in `inputs` get converted to tensors
|
|
# - implicit masks / mask metadata are only collected from 'inputs`
|
|
# - Layers are built using shape info from 'inputs' only
|
|
# - input_spec compatibility is only checked against `inputs`
|
|
# - mixed precision casting (autocast) is only applied to `inputs`,
|
|
# not to any other argument.
|
|
# - setting the SavedModel saving spec.
|
|
inputs, args, kwargs = self._split_out_first_arg(args, kwargs)
|
|
input_list = nest.flatten(inputs)
|
|
|
|
# Functional Model construction mode is invoked when `Layer`s are called on
|
|
# symbolic `KerasTensor`s, i.e.:
|
|
# >> inputs = tf.keras.Input(10)
|
|
# >> outputs = MyLayer()(inputs) # Functional construction mode.
|
|
# >> model = tf.keras.Model(inputs, outputs)
|
|
if _in_functional_construction_mode(self, inputs, args, kwargs, input_list):
|
|
return self._functional_construction_call(inputs, args, kwargs,
|
|
input_list)
|
|
|
|
# Maintains info about the `Layer.call` stack.
|
|
call_context = base_layer_utils.call_context()
|
|
|
|
# Accept NumPy and scalar inputs by converting to Tensors.
|
|
if any(isinstance(x, (
|
|
np_arrays.ndarray, np.ndarray, float, int)) for x in input_list):
|
|
inputs = nest.map_structure(_convert_numpy_or_python_types, inputs)
|
|
input_list = nest.flatten(inputs)
|
|
|
|
# Handle `mask` propagation from previous layer to current layer. Masks can
|
|
# be propagated explicitly via the `mask` argument, or implicitly via
|
|
# setting the `_keras_mask` attribute on the inputs to a Layer. Masks passed
|
|
# explicitly take priority.
|
|
input_masks, mask_is_implicit = self._get_input_masks(
|
|
inputs, input_list, args, kwargs)
|
|
if self._expects_mask_arg and mask_is_implicit:
|
|
kwargs['mask'] = input_masks
|
|
|
|
# Training mode for `Layer.call` is set via (in order of priority):
|
|
# (1) The `training` argument passed to this `Layer.call`, if it is not None
|
|
# (2) The training mode of an outer `Layer.call`.
|
|
# (3) The default mode set by `tf.keras.backend.set_learning_phase` (if set)
|
|
# (4) Any non-None default value for `training` specified in the call
|
|
# signature
|
|
# (5) False (treating the layer as if it's in inference)
|
|
args, kwargs, training_mode = self._set_training_mode(
|
|
args, kwargs, call_context)
|
|
|
|
# Losses are cleared for all sublayers on the outermost `Layer.call`.
|
|
# Losses are not cleared on inner `Layer.call`s, because sublayers can be
|
|
# called multiple times.
|
|
if not call_context.in_call:
|
|
self._clear_losses()
|
|
|
|
eager = context.executing_eagerly()
|
|
with call_context.enter(
|
|
layer=self,
|
|
inputs=inputs,
|
|
build_graph=not eager,
|
|
training=training_mode):
|
|
|
|
if self._autocast:
|
|
inputs = self._maybe_cast_inputs(inputs, input_list)
|
|
|
|
input_spec.assert_input_compatibility(self.input_spec, inputs, self.name)
|
|
if eager:
|
|
call_fn = self.call
|
|
name_scope = self._name
|
|
else:
|
|
name_scope = self._name_scope() # Avoid autoincrementing.
|
|
call_fn = self._autographed_call()
|
|
|
|
with ops.name_scope_v2(name_scope):
|
|
if not self.built:
|
|
self._maybe_build(inputs)
|
|
|
|
with autocast_variable.enable_auto_cast_variables(
|
|
self._compute_dtype_object):
|
|
outputs = call_fn(inputs, *args, **kwargs)
|
|
|
|
if self._activity_regularizer:
|
|
self._handle_activity_regularization(inputs, outputs)
|
|
if self._supports_masking:
|
|
self._set_mask_metadata(inputs, outputs, input_masks, not eager)
|
|
if self._saved_model_inputs_spec is None:
|
|
self._set_save_spec(inputs)
|
|
|
|
return outputs
|
|
|
|
def _functional_construction_call(self, inputs, args, kwargs, input_list):
|
|
call_context = base_layer_utils.call_context()
|
|
|
|
# Accept NumPy and scalar inputs by converting to Tensors.
|
|
if any(isinstance(x, (
|
|
np_arrays.ndarray, np.ndarray, float, int)) for x in input_list):
|
|
|
|
def _convert_non_tensor(x):
|
|
# Don't call `ops.convert_to_tensor` on all `inputs` because
|
|
# `SparseTensors` can't be converted to `Tensor`.
|
|
if isinstance(x, (np_arrays.ndarray, np.ndarray, float, int)):
|
|
return ops.convert_to_tensor_v2_with_dispatch(x)
|
|
return x
|
|
|
|
inputs = nest.map_structure(_convert_non_tensor, inputs)
|
|
input_list = nest.flatten(inputs)
|
|
|
|
# Handle `mask` propagation from previous layer to current layer. Masks can
|
|
# be propagated explicitly via the `mask` argument, or implicitly via
|
|
# setting the `_keras_mask` attribute on the inputs to a Layer. Masks passed
|
|
# explicitly take priority.
|
|
mask_arg_passed_by_framework = False
|
|
input_masks, mask_is_implicit = self._get_input_masks(
|
|
inputs, input_list, args, kwargs)
|
|
if self._expects_mask_arg and mask_is_implicit:
|
|
kwargs['mask'] = input_masks
|
|
mask_arg_passed_by_framework = True
|
|
|
|
# If `training` argument is None or not explicitly passed,
|
|
# propagate `training` value from this layer's calling layer.
|
|
training_value = None
|
|
training_arg_passed_by_framework = False
|
|
# Priority 1: `training` was explicitly passed a non-None value.
|
|
if self._call_arg_was_passed('training', args, kwargs):
|
|
training_value = self._get_call_arg_value('training', args, kwargs)
|
|
if not self._expects_training_arg:
|
|
kwargs.pop('training')
|
|
|
|
if training_value is None:
|
|
# Priority 2: `training` was passed to a parent layer.
|
|
if call_context.training is not None:
|
|
training_value = call_context.training
|
|
# Priority 3: `learning_phase()` has been set.
|
|
elif backend.global_learning_phase_is_set():
|
|
training_value = backend.learning_phase()
|
|
# Force the training_value to be bool type which matches to the contract
|
|
# for layer/model call args.
|
|
if tensor_util.is_tensor(training_value):
|
|
training_value = math_ops.cast(training_value, dtypes.bool)
|
|
else:
|
|
training_value = bool(training_value)
|
|
# Priority 4: trace layer with the default training argument specified
|
|
# in the `call` signature (or in inference mode if the `call` signature
|
|
# specifies no non-None default).
|
|
else:
|
|
training_value = self._default_training_arg
|
|
# In cases (2), (3), (4) the training argument is passed automatically
|
|
# by the framework, and will not be hard-coded into the model.
|
|
if self._expects_training_arg:
|
|
args, kwargs = self._set_call_arg_value('training', training_value,
|
|
args, kwargs)
|
|
training_arg_passed_by_framework = True
|
|
|
|
if keras_tensor.keras_tensors_enabled():
|
|
with call_context.enter(
|
|
layer=self, inputs=inputs, build_graph=True, training=training_value):
|
|
# Check input assumptions set after layer building, e.g. input shape.
|
|
outputs = self._keras_tensor_symbolic_call(
|
|
inputs, input_masks, args, kwargs)
|
|
|
|
if outputs is None:
|
|
raise ValueError('A layer\'s `call` method should return a '
|
|
'Tensor or a list of Tensors, not None '
|
|
'(layer: ' + self.name + ').')
|
|
if training_arg_passed_by_framework:
|
|
args, kwargs = self._set_call_arg_value(
|
|
'training', None, args, kwargs, pop_kwarg_if_none=True)
|
|
if mask_arg_passed_by_framework:
|
|
kwargs.pop('mask')
|
|
# Node connectivity does not special-case the first argument.
|
|
outputs = self._set_connectivity_metadata((inputs,) + args, kwargs,
|
|
outputs)
|
|
return outputs
|
|
|
|
# Only create Keras history if at least one tensor originates from a
|
|
# `keras.Input`. Otherwise this Layer may be being used outside the Keras
|
|
# framework.
|
|
# TODO(kaftan): make this not special case inputs
|
|
if base_layer_utils.needs_keras_history(inputs):
|
|
base_layer_utils.create_keras_history(inputs)
|
|
|
|
with call_context.enter(
|
|
layer=self, inputs=inputs, build_graph=True, training=training_value):
|
|
# Symbolic execution on symbolic tensors. We will attempt to build
|
|
# the corresponding TF subgraph inside `backend.get_graph()`
|
|
# TODO(reedwm): We should assert input compatibility after the inputs
|
|
# are casted, not before.
|
|
input_spec.assert_input_compatibility(self.input_spec, inputs, self.name)
|
|
graph = backend.get_graph()
|
|
# Use `self._name_scope()` to avoid auto-incrementing the name.
|
|
with graph.as_default(), backend.name_scope(self._name_scope()):
|
|
# Build layer if applicable (if the `build` method has been
|
|
# overridden).
|
|
self._maybe_build(inputs)
|
|
cast_inputs = self._maybe_cast_inputs(inputs, input_list)
|
|
|
|
if not self.dynamic:
|
|
# Wrapping `call` function in autograph to allow for dynamic control
|
|
# flow and control dependencies in call. We are limiting this to
|
|
# subclassed layers as autograph is strictly needed only for
|
|
# subclassed layers and models.
|
|
# tf_convert will respect the value of autograph setting in the
|
|
# enclosing tf.function, if any.
|
|
if (base_layer_utils.is_subclassed(self) and
|
|
not base_layer_utils.from_saved_model(self)):
|
|
call_fn = autograph.tf_convert(self.call,
|
|
ag_ctx.control_status_ctx())
|
|
else:
|
|
call_fn = self.call
|
|
|
|
try:
|
|
with autocast_variable.enable_auto_cast_variables(
|
|
self._compute_dtype_object):
|
|
outputs = call_fn(cast_inputs, *args, **kwargs)
|
|
|
|
except errors.OperatorNotAllowedInGraphError as e:
|
|
raise TypeError('You are attempting to use Python control '
|
|
'flow in a layer that was not declared to be '
|
|
'dynamic. Pass `dynamic=True` to the class '
|
|
'constructor.\nEncountered error:\n"""\n' + str(e) +
|
|
'\n"""')
|
|
else:
|
|
# We will use static shape inference to return symbolic tensors
|
|
# matching the specifications of the layer outputs.
|
|
# Since `self.dynamic` is True, we will never attempt to
|
|
# run the underlying TF graph (which is disconnected).
|
|
# TODO(fchollet): consider py_func as an alternative, which
|
|
# would enable us to run the underlying graph if needed.
|
|
outputs = self._symbolic_call(inputs)
|
|
|
|
if outputs is None:
|
|
raise ValueError('A layer\'s `call` method should return a '
|
|
'Tensor or a list of Tensors, not None '
|
|
'(layer: ' + self.name + ').')
|
|
# TODO(kaftan): This should be 'any' and check all args
|
|
if base_layer_utils.have_all_keras_metadata(inputs):
|
|
if training_arg_passed_by_framework:
|
|
args, kwargs = self._set_call_arg_value(
|
|
'training', None, args, kwargs, pop_kwarg_if_none=True)
|
|
if mask_arg_passed_by_framework:
|
|
kwargs.pop('mask')
|
|
# Node connectivity does not special-case the first argument.
|
|
outputs = self._set_connectivity_metadata((inputs,) + args, kwargs,
|
|
outputs)
|
|
self._handle_activity_regularization(inputs, outputs)
|
|
self._set_mask_metadata(inputs, outputs, input_masks, True)
|
|
if hasattr(self, '_set_inputs') and not self.inputs:
|
|
# Subclassed network: explicitly set metadata normally set by
|
|
# a call to self._set_inputs().
|
|
self._set_inputs(cast_inputs, outputs)
|
|
|
|
return outputs
|
|
|
|
def _set_training_mode(self, args, kwargs, call_context):
|
|
training_mode = None
|
|
if self._expects_training_arg:
|
|
# (1) `training` was passed to this `Layer.call`.
|
|
if self._call_arg_was_passed('training', args, kwargs):
|
|
training_mode = self._get_call_arg_value('training', args, kwargs)
|
|
# If no `training` arg was passed, or `None` was explicitly passed,
|
|
# the framework will make a decision about the training mode is.
|
|
if training_mode is None:
|
|
call_ctx_training = call_context.training
|
|
# (2) `training` mode is inferred from an outer `Layer.call`.
|
|
if call_ctx_training is not None:
|
|
training_mode = call_ctx_training
|
|
# (3) User set `tf.keras.backend.set_learning_phase`.
|
|
elif backend.global_learning_phase_is_set():
|
|
training_mode = backend.learning_phase()
|
|
# Ensure value is a `bool` or `tf.bool`.
|
|
if isinstance(training_mode, bool):
|
|
pass
|
|
elif tensor_util.is_tensor(training_mode):
|
|
training_mode = math_ops.cast(training_mode, dtypes.bool)
|
|
else:
|
|
training_mode = bool(training_mode)
|
|
# (4) We default to using `call`'s default value for `training`,
|
|
# or treating the layer as if it is in inference if no non-None default
|
|
# is specified in the `call` signature.
|
|
else:
|
|
training_mode = self._default_training_arg
|
|
|
|
# For case (2), (3), (4) `training` arg is passed by framework.
|
|
args, kwargs = self._set_call_arg_value('training', training_mode, args,
|
|
kwargs)
|
|
else:
|
|
if 'training' in kwargs:
|
|
# `training` was passed to this `Layer` but is not needed for
|
|
# `Layer.call`. It will set the default mode for inner `Layer.call`s.
|
|
training_mode = kwargs.pop('training')
|
|
else:
|
|
# Grab the current `training` mode from any outer `Layer.call`.
|
|
training_mode = call_context.training
|
|
|
|
return args, kwargs, training_mode
|
|
|
|
def _autographed_call(self):
|
|
# Wrapping `call` function in autograph to allow for dynamic control
|
|
# flow and control dependencies in call. We are limiting this to
|
|
# subclassed layers as autograph is strictly needed only for
|
|
# subclassed layers and models.
|
|
# tf_convert will respect the value of autograph setting in the
|
|
# enclosing tf.function, if any.
|
|
if (base_layer_utils.is_subclassed(self) and
|
|
not base_layer_utils.from_saved_model(self)):
|
|
return autograph.tf_convert(self.call, ag_ctx.control_status_ctx())
|
|
else:
|
|
return self.call
|
|
|
|
@property
|
|
def dtype(self):
|
|
"""The dtype of the layer weights.
|
|
|
|
This is equivalent to `Layer.dtype_policy.variable_dtype`. Unless
|
|
mixed precision is used, this is the same as `Layer.compute_dtype`, the
|
|
dtype of the layer's computations.
|
|
"""
|
|
return self._dtype_policy.variable_dtype
|
|
|
|
@property
|
|
def name(self):
|
|
"""Name of the layer (string), set in the constructor."""
|
|
return self._name
|
|
|
|
@property
|
|
def supports_masking(self):
|
|
"""Whether this layer supports computing a mask using `compute_mask`."""
|
|
return self._supports_masking
|
|
|
|
@supports_masking.setter
|
|
def supports_masking(self, value):
|
|
self._supports_masking = value
|
|
|
|
@property
|
|
def dynamic(self):
|
|
"""Whether the layer is dynamic (eager-only); set in the constructor."""
|
|
return any(layer._dynamic for layer in self._flatten_layers())
|
|
|
|
@property
|
|
@doc_controls.do_not_doc_inheritable
|
|
def stateful(self):
|
|
return any(layer._stateful for layer in self._flatten_layers())
|
|
|
|
@stateful.setter
|
|
def stateful(self, value):
|
|
self._stateful = value
|
|
|
|
@property
|
|
def trainable(self):
|
|
return self._trainable
|
|
|
|
@trainable.setter
|
|
def trainable(self, value):
|
|
for layer in self._flatten_layers():
|
|
layer._trainable = value
|
|
|
|
@property
|
|
def activity_regularizer(self):
|
|
"""Optional regularizer function for the output of this layer."""
|
|
return self._activity_regularizer
|
|
|
|
@activity_regularizer.setter
|
|
def activity_regularizer(self, regularizer):
|
|
"""Optional regularizer function for the output of this layer."""
|
|
self._activity_regularizer = regularizer
|
|
|
|
@property
|
|
def input_spec(self):
|
|
"""`InputSpec` instance(s) describing the input format for this layer.
|
|
|
|
When you create a layer subclass, you can set `self.input_spec` to enable
|
|
the layer to run input compatibility checks when it is called.
|
|
Consider a `Conv2D` layer: it can only be called on a single input tensor
|
|
of rank 4. As such, you can set, in `__init__()`:
|
|
|
|
```python
|
|
self.input_spec = tf.keras.layers.InputSpec(ndim=4)
|
|
```
|
|
|
|
Now, if you try to call the layer on an input that isn't rank 4
|
|
(for instance, an input of shape `(2,)`, it will raise a nicely-formatted
|
|
error:
|
|
|
|
```
|
|
ValueError: Input 0 of layer conv2d is incompatible with the layer:
|
|
expected ndim=4, found ndim=1. Full shape received: [2]
|
|
```
|
|
|
|
Input checks that can be specified via `input_spec` include:
|
|
- Structure (e.g. a single input, a list of 2 inputs, etc)
|
|
- Shape
|
|
- Rank (ndim)
|
|
- Dtype
|
|
|
|
For more information, see `tf.keras.layers.InputSpec`.
|
|
|
|
Returns:
|
|
A `tf.keras.layers.InputSpec` instance, or nested structure thereof.
|
|
"""
|
|
return self._input_spec
|
|
|
|
@input_spec.setter
|
|
# Must be decorated to prevent tracking, since the input_spec can be nested
|
|
# InputSpec objects.
|
|
@trackable.no_automatic_dependency_tracking
|
|
def input_spec(self, value):
|
|
for v in nest.flatten(value):
|
|
if v is not None and not isinstance(v, InputSpec):
|
|
raise TypeError('Layer input_spec must be an instance of InputSpec. '
|
|
'Got: {}'.format(v))
|
|
self._input_spec = value
|
|
|
|
@property
|
|
def trainable_weights(self):
|
|
"""List of all trainable weights tracked by this layer.
|
|
|
|
Trainable weights are updated via gradient descent during training.
|
|
|
|
Note: This will not track the weights of nested `tf.Modules` that are not
|
|
themselves Keras layers.
|
|
|
|
Returns:
|
|
A list of trainable variables.
|
|
"""
|
|
if self.trainable:
|
|
children_weights = self._gather_children_attribute('trainable_weights')
|
|
return self._dedup_weights(self._trainable_weights + children_weights)
|
|
else:
|
|
return []
|
|
|
|
@property
|
|
def non_trainable_weights(self):
|
|
"""List of all non-trainable weights tracked by this layer.
|
|
|
|
Non-trainable weights are *not* updated during training. They are expected
|
|
to be updated manually in `call()`.
|
|
|
|
Note: This will not track the weights of nested `tf.Modules` that are not
|
|
themselves Keras layers.
|
|
|
|
Returns:
|
|
A list of non-trainable variables.
|
|
"""
|
|
if self.trainable:
|
|
children_weights = self._gather_children_attribute(
|
|
'non_trainable_weights')
|
|
non_trainable_weights = self._non_trainable_weights + children_weights
|
|
else:
|
|
children_weights = self._gather_children_attribute('weights')
|
|
non_trainable_weights = (
|
|
self._trainable_weights + self._non_trainable_weights +
|
|
children_weights)
|
|
return self._dedup_weights(non_trainable_weights)
|
|
|
|
@property
|
|
def weights(self):
|
|
"""Returns the list of all layer variables/weights.
|
|
|
|
Note: This will not track the weights of nested `tf.Modules` that are not
|
|
themselves Keras layers.
|
|
|
|
Returns:
|
|
A list of variables.
|
|
"""
|
|
return self.trainable_weights + self.non_trainable_weights
|
|
|
|
@property
|
|
@doc_controls.do_not_generate_docs
|
|
def updates(self):
|
|
warnings.warn('`layer.updates` will be removed in a future version. '
|
|
'This property should not be used in TensorFlow 2.0, '
|
|
'as `updates` are applied automatically.')
|
|
if keras_tensor.keras_tensors_enabled():
|
|
return []
|
|
|
|
collected_updates = []
|
|
all_layers = self._flatten_layers()
|
|
with backend.get_graph().as_default():
|
|
for layer in all_layers:
|
|
if not layer.trainable and not layer.stateful:
|
|
continue
|
|
for u in layer._updates:
|
|
if callable(u):
|
|
u = u()
|
|
collected_updates.append(u)
|
|
return collected_updates
|
|
|
|
@property
|
|
def losses(self):
|
|
"""List of losses added using the `add_loss()` API.
|
|
|
|
Variable regularization tensors are created when this property is accessed,
|
|
so it is eager safe: accessing `losses` under a `tf.GradientTape` will
|
|
propagate gradients back to the corresponding variables.
|
|
|
|
Examples:
|
|
|
|
>>> class MyLayer(tf.keras.layers.Layer):
|
|
... def call(self, inputs):
|
|
... self.add_loss(tf.abs(tf.reduce_mean(inputs)))
|
|
... return inputs
|
|
>>> l = MyLayer()
|
|
>>> l(np.ones((10, 1)))
|
|
>>> l.losses
|
|
[1.0]
|
|
|
|
>>> inputs = tf.keras.Input(shape=(10,))
|
|
>>> x = tf.keras.layers.Dense(10)(inputs)
|
|
>>> outputs = tf.keras.layers.Dense(1)(x)
|
|
>>> model = tf.keras.Model(inputs, outputs)
|
|
>>> # Activity regularization.
|
|
>>> len(model.losses)
|
|
0
|
|
>>> model.add_loss(tf.abs(tf.reduce_mean(x)))
|
|
>>> len(model.losses)
|
|
1
|
|
|
|
>>> inputs = tf.keras.Input(shape=(10,))
|
|
>>> d = tf.keras.layers.Dense(10, kernel_initializer='ones')
|
|
>>> x = d(inputs)
|
|
>>> outputs = tf.keras.layers.Dense(1)(x)
|
|
>>> model = tf.keras.Model(inputs, outputs)
|
|
>>> # Weight regularization.
|
|
>>> model.add_loss(lambda: tf.reduce_mean(d.kernel))
|
|
>>> model.losses
|
|
[<tf.Tensor: shape=(), dtype=float32, numpy=1.0>]
|
|
|
|
Returns:
|
|
A list of tensors.
|
|
"""
|
|
collected_losses = []
|
|
for layer in self._flatten_layers():
|
|
# If any eager losses are present, we assume the model to be part of an
|
|
# eager training loop (either a custom one or the one used when
|
|
# `run_eagerly=True`) and so we always return just the eager losses.
|
|
if layer._eager_losses:
|
|
# Filter placeholder losses that may have been added by revived layers.
|
|
# (see base_layer_utils for details).
|
|
if (layer._eager_losses[0] is
|
|
not base_layer_utils.REVIVED_LOSS_PLACEHOLDER):
|
|
collected_losses.extend(layer._eager_losses)
|
|
else:
|
|
collected_losses.extend(layer._losses)
|
|
for regularizer in layer._callable_losses:
|
|
loss_tensor = regularizer()
|
|
if loss_tensor is not None:
|
|
collected_losses.append(loss_tensor)
|
|
return collected_losses
|
|
|
|
def add_loss(self, losses, **kwargs):
|
|
"""Add loss tensor(s), potentially dependent on layer inputs.
|
|
|
|
Some losses (for instance, activity regularization losses) may be dependent
|
|
on the inputs passed when calling a layer. Hence, when reusing the same
|
|
layer on different inputs `a` and `b`, some entries in `layer.losses` may
|
|
be dependent on `a` and some on `b`. This method automatically keeps track
|
|
of dependencies.
|
|
|
|
This method can be used inside a subclassed layer or model's `call`
|
|
function, in which case `losses` should be a Tensor or list of Tensors.
|
|
|
|
Example:
|
|
|
|
```python
|
|
class MyLayer(tf.keras.layers.Layer):
|
|
def call(self, inputs):
|
|
self.add_loss(tf.abs(tf.reduce_mean(inputs)))
|
|
return inputs
|
|
```
|
|
|
|
This method can also be called directly on a Functional Model during
|
|
construction. In this case, any loss Tensors passed to this Model must
|
|
be symbolic and be able to be traced back to the model's `Input`s. These
|
|
losses become part of the model's topology and are tracked in `get_config`.
|
|
|
|
Example:
|
|
|
|
```python
|
|
inputs = tf.keras.Input(shape=(10,))
|
|
x = tf.keras.layers.Dense(10)(inputs)
|
|
outputs = tf.keras.layers.Dense(1)(x)
|
|
model = tf.keras.Model(inputs, outputs)
|
|
# Activity regularization.
|
|
model.add_loss(tf.abs(tf.reduce_mean(x)))
|
|
```
|
|
|
|
If this is not the case for your loss (if, for example, your loss references
|
|
a `Variable` of one of the model's layers), you can wrap your loss in a
|
|
zero-argument lambda. These losses are not tracked as part of the model's
|
|
topology since they can't be serialized.
|
|
|
|
Example:
|
|
|
|
```python
|
|
inputs = tf.keras.Input(shape=(10,))
|
|
d = tf.keras.layers.Dense(10)
|
|
x = d(inputs)
|
|
outputs = tf.keras.layers.Dense(1)(x)
|
|
model = tf.keras.Model(inputs, outputs)
|
|
# Weight regularization.
|
|
model.add_loss(lambda: tf.reduce_mean(d.kernel))
|
|
```
|
|
|
|
Arguments:
|
|
losses: Loss tensor, or list/tuple of tensors. Rather than tensors, losses
|
|
may also be zero-argument callables which create a loss tensor.
|
|
**kwargs: Additional keyword arguments for backward compatibility.
|
|
Accepted values:
|
|
inputs - Deprecated, will be automatically inferred.
|
|
"""
|
|
kwargs.pop('inputs', None)
|
|
if kwargs:
|
|
raise TypeError('Unknown keyword arguments: %s' % (kwargs.keys(),))
|
|
|
|
def _tag_callable(loss):
|
|
"""Tags callable loss tensor as `_unconditional_loss`."""
|
|
if callable(loss):
|
|
# We run the loss without autocasting, as regularizers are often
|
|
# numerically unstable in float16.
|
|
with autocast_variable.enable_auto_cast_variables(None):
|
|
loss = loss()
|
|
if loss is None:
|
|
return None # Will be filtered out when computing the .losses property
|
|
if not tensor_util.is_tensor(loss):
|
|
loss = ops.convert_to_tensor_v2_with_dispatch(
|
|
loss, dtype=backend.floatx())
|
|
loss._unconditional_loss = True # pylint: disable=protected-access
|
|
return loss
|
|
|
|
losses = nest.flatten(losses)
|
|
|
|
callable_losses = []
|
|
eager_losses = []
|
|
symbolic_losses = []
|
|
for loss in losses:
|
|
if callable(loss):
|
|
callable_losses.append(functools.partial(_tag_callable, loss))
|
|
continue
|
|
if loss is None:
|
|
continue
|
|
if not tensor_util.is_tensor(loss) and not isinstance(
|
|
loss, keras_tensor.KerasTensor):
|
|
loss = ops.convert_to_tensor_v2_with_dispatch(
|
|
loss, dtype=backend.floatx())
|
|
# TF Functions should take the eager path.
|
|
if ((tf_utils.is_symbolic_tensor(loss) or
|
|
isinstance(loss, keras_tensor.KerasTensor)) and
|
|
not base_layer_utils.is_in_tf_function()):
|
|
symbolic_losses.append(loss)
|
|
elif tensor_util.is_tensor(loss):
|
|
eager_losses.append(loss)
|
|
|
|
self._callable_losses.extend(callable_losses)
|
|
|
|
in_call_context = base_layer_utils.call_context().in_call
|
|
if eager_losses and not in_call_context:
|
|
raise ValueError(
|
|
'Expected a symbolic Tensors or a callable for the loss value. '
|
|
'Please wrap your loss computation in a zero argument `lambda`.')
|
|
|
|
self._eager_losses.extend(eager_losses)
|
|
|
|
if in_call_context and not keras_tensor.keras_tensors_enabled():
|
|
for symbolic_loss in symbolic_losses:
|
|
self._losses.append(symbolic_loss)
|
|
else:
|
|
for symbolic_loss in symbolic_losses:
|
|
if getattr(self, '_is_graph_network', False):
|
|
self._graph_network_add_loss(symbolic_loss)
|
|
else:
|
|
# Possible a loss was added in a Layer's `build`.
|
|
self._losses.append(symbolic_loss)
|
|
|
|
def _clear_losses(self):
|
|
"""Used every step in eager to reset losses."""
|
|
# Set to thread local directly to avoid Layer.__setattr__ overhead.
|
|
if not getattr(self, '_layers', None): # Fast path for single Layer.
|
|
self._thread_local._eager_losses = []
|
|
else:
|
|
for layer in self._flatten_layers():
|
|
layer._thread_local._eager_losses = []
|
|
|
|
@property
|
|
def metrics(self):
|
|
"""List of metrics added using the `add_metric()` API.
|
|
|
|
Example:
|
|
|
|
>>> input = tf.keras.layers.Input(shape=(3,))
|
|
>>> d = tf.keras.layers.Dense(2)
|
|
>>> output = d(input)
|
|
>>> d.add_metric(tf.reduce_max(output), name='max')
|
|
>>> d.add_metric(tf.reduce_min(output), name='min')
|
|
>>> [m.name for m in d.metrics]
|
|
['max', 'min']
|
|
|
|
Returns:
|
|
A list of `Metric` objects.
|
|
"""
|
|
collected_metrics = []
|
|
for layer in self._flatten_layers():
|
|
with layer._metrics_lock:
|
|
collected_metrics.extend(layer._metrics)
|
|
return collected_metrics
|
|
|
|
def add_metric(self, value, name=None, **kwargs):
|
|
"""Adds metric tensor to the layer.
|
|
|
|
This method can be used inside the `call()` method of a subclassed layer
|
|
or model.
|
|
|
|
```python
|
|
class MyMetricLayer(tf.keras.layers.Layer):
|
|
def __init__(self):
|
|
super(MyMetricLayer, self).__init__(name='my_metric_layer')
|
|
self.mean = tf.keras.metrics.Mean(name='metric_1')
|
|
|
|
def call(self, inputs):
|
|
self.add_metric(self.mean(x))
|
|
self.add_metric(tf.reduce_sum(x), name='metric_2')
|
|
return inputs
|
|
```
|
|
|
|
This method can also be called directly on a Functional Model during
|
|
construction. In this case, any tensor passed to this Model must
|
|
be symbolic and be able to be traced back to the model's `Input`s. These
|
|
metrics become part of the model's topology and are tracked when you
|
|
save the model via `save()`.
|
|
|
|
```python
|
|
inputs = tf.keras.Input(shape=(10,))
|
|
x = tf.keras.layers.Dense(10)(inputs)
|
|
outputs = tf.keras.layers.Dense(1)(x)
|
|
model = tf.keras.Model(inputs, outputs)
|
|
model.add_metric(math_ops.reduce_sum(x), name='metric_1')
|
|
```
|
|
|
|
Note: Calling `add_metric()` with the result of a metric object on a
|
|
Functional Model, as shown in the example below, is not supported. This is
|
|
because we cannot trace the metric result tensor back to the model's inputs.
|
|
|
|
```python
|
|
inputs = tf.keras.Input(shape=(10,))
|
|
x = tf.keras.layers.Dense(10)(inputs)
|
|
outputs = tf.keras.layers.Dense(1)(x)
|
|
model = tf.keras.Model(inputs, outputs)
|
|
model.add_metric(tf.keras.metrics.Mean()(x), name='metric_1')
|
|
```
|
|
|
|
Args:
|
|
value: Metric tensor.
|
|
name: String metric name.
|
|
**kwargs: Additional keyword arguments for backward compatibility.
|
|
Accepted values:
|
|
`aggregation` - When the `value` tensor provided is not the result of
|
|
calling a `keras.Metric` instance, it will be aggregated by default
|
|
using a `keras.Metric.Mean`.
|
|
"""
|
|
kwargs_keys = list(kwargs.keys())
|
|
if (len(kwargs_keys) > 1 or
|
|
(len(kwargs_keys) == 1 and kwargs_keys[0] != 'aggregation')):
|
|
raise TypeError('Unknown keyword arguments: ', str(kwargs.keys()))
|
|
|
|
from_metric_obj = hasattr(value, '_metric_obj')
|
|
if keras_tensor.keras_tensors_enabled():
|
|
is_symbolic = isinstance(value, keras_tensor.KerasTensor)
|
|
else:
|
|
is_symbolic = tf_utils.is_symbolic_tensor(value)
|
|
in_call_context = base_layer_utils.call_context().in_call
|
|
|
|
if name is None and not from_metric_obj:
|
|
# Eg. `self.add_metric(math_ops.reduce_sum(x))`
|
|
# In eager mode, we use metric name to lookup a metric. Without a name,
|
|
# a new Mean metric wrapper will be created on every model/layer call.
|
|
# So, we raise an error when no name is provided.
|
|
# We will do the same for symbolic mode for consistency although a name
|
|
# will be generated if no name is provided.
|
|
|
|
# We will not raise this error in the foll use case for the sake of
|
|
# consistency as name in provided in the metric constructor.
|
|
# mean = metrics.Mean(name='my_metric')
|
|
# model.add_metric(mean(outputs))
|
|
raise ValueError('Please provide a name for your metric like '
|
|
'`self.add_metric(tf.reduce_sum(inputs), '
|
|
'name=\'mean_activation\')`')
|
|
elif from_metric_obj:
|
|
name = value._metric_obj.name
|
|
|
|
if not in_call_context and not is_symbolic:
|
|
raise ValueError('Expected a symbolic Tensor for the metric value, '
|
|
'received: ' + str(value))
|
|
|
|
# If a metric was added in a Layer's `call` or `build`.
|
|
if in_call_context or not getattr(self, '_is_graph_network', False):
|
|
# TF Function path should take the eager path.
|
|
|
|
# If the given metric is available in `metrics` list we just update state
|
|
# on it, otherwise we create a new metric instance and
|
|
# add it to the `metrics` list.
|
|
metric_obj = getattr(value, '_metric_obj', None)
|
|
# Tensors that come from a Metric object already updated the Metric state.
|
|
should_update_state = not metric_obj
|
|
name = metric_obj.name if metric_obj else name
|
|
|
|
with self._metrics_lock:
|
|
match = self._get_existing_metric(name)
|
|
if match:
|
|
metric_obj = match
|
|
elif metric_obj:
|
|
self._metrics.append(metric_obj)
|
|
else:
|
|
# Build the metric object with the value's dtype if it defines one
|
|
metric_obj = metrics_mod.Mean(
|
|
name=name, dtype=getattr(value, 'dtype', None))
|
|
self._metrics.append(metric_obj)
|
|
|
|
if should_update_state:
|
|
metric_obj(value)
|
|
else:
|
|
if from_metric_obj:
|
|
raise ValueError('Using the result of calling a `Metric` object '
|
|
'when calling `add_metric` on a Functional '
|
|
'Model is not supported. Please pass the '
|
|
'Tensor to monitor directly.')
|
|
|
|
# Insert layers into the Keras Graph Network.
|
|
aggregation = None if from_metric_obj else 'mean'
|
|
self._graph_network_add_metric(value, aggregation, name)
|
|
|
|
@doc_controls.do_not_doc_inheritable
|
|
def add_update(self, updates, inputs=None):
|
|
"""Add update op(s), potentially dependent on layer inputs.
|
|
|
|
Weight updates (for instance, the updates of the moving mean and variance
|
|
in a BatchNormalization layer) may be dependent on the inputs passed
|
|
when calling a layer. Hence, when reusing the same layer on
|
|
different inputs `a` and `b`, some entries in `layer.updates` may be
|
|
dependent on `a` and some on `b`. This method automatically keeps track
|
|
of dependencies.
|
|
|
|
This call is ignored when eager execution is enabled (in that case, variable
|
|
updates are run on the fly and thus do not need to be tracked for later
|
|
execution).
|
|
|
|
Arguments:
|
|
updates: Update op, or list/tuple of update ops, or zero-arg callable
|
|
that returns an update op. A zero-arg callable should be passed in
|
|
order to disable running the updates by setting `trainable=False`
|
|
on this Layer, when executing in Eager mode.
|
|
inputs: Deprecated, will be automatically inferred.
|
|
"""
|
|
if inputs is not None:
|
|
tf_logging.warning(
|
|
'`add_update` `inputs` kwarg has been deprecated. You no longer need '
|
|
'to pass a value to `inputs` as it is being automatically inferred.')
|
|
call_context = base_layer_utils.call_context()
|
|
# No need to run updates during Functional API construction.
|
|
if call_context.in_keras_graph:
|
|
return
|
|
|
|
# Callable updates are disabled by setting `trainable=False`.
|
|
if not call_context.frozen:
|
|
for update in nest.flatten(updates):
|
|
if callable(update):
|
|
update() # pylint: disable=not-callable
|
|
|
|
def set_weights(self, weights):
|
|
"""Sets the weights of the layer, from Numpy arrays.
|
|
|
|
The weights of a layer represent the state of the layer. This function
|
|
sets the weight values from numpy arrays. The weight values should be
|
|
passed in the order they are created by the layer. Note that the layer's
|
|
weights must be instantiated before calling this function by calling
|
|
the layer.
|
|
|
|
For example, a Dense layer returns a list of two values-- per-output
|
|
weights and the bias value. These can be used to set the weights of another
|
|
Dense layer:
|
|
|
|
>>> a = tf.keras.layers.Dense(1,
|
|
... kernel_initializer=tf.constant_initializer(1.))
|
|
>>> a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
|
|
>>> a.get_weights()
|
|
[array([[1.],
|
|
[1.],
|
|
[1.]], dtype=float32), array([0.], dtype=float32)]
|
|
>>> b = tf.keras.layers.Dense(1,
|
|
... kernel_initializer=tf.constant_initializer(2.))
|
|
>>> b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
|
|
>>> b.get_weights()
|
|
[array([[2.],
|
|
[2.],
|
|
[2.]], dtype=float32), array([0.], dtype=float32)]
|
|
>>> b.set_weights(a.get_weights())
|
|
>>> b.get_weights()
|
|
[array([[1.],
|
|
[1.],
|
|
[1.]], dtype=float32), array([0.], dtype=float32)]
|
|
|
|
Arguments:
|
|
weights: a list of Numpy arrays. The number
|
|
of arrays and their shape must match
|
|
number of the dimensions of the weights
|
|
of the layer (i.e. it should match the
|
|
output of `get_weights`).
|
|
|
|
Raises:
|
|
ValueError: If the provided weights list does not match the
|
|
layer's specifications.
|
|
"""
|
|
params = self.weights
|
|
|
|
expected_num_weights = 0
|
|
for param in params:
|
|
if isinstance(param, base_layer_utils.TrackableWeightHandler):
|
|
expected_num_weights += param.num_tensors
|
|
else:
|
|
expected_num_weights += 1
|
|
|
|
if expected_num_weights != len(weights):
|
|
raise ValueError(
|
|
'You called `set_weights(weights)` on layer "%s" '
|
|
'with a weight list of length %s, but the layer was '
|
|
'expecting %s weights. Provided weights: %s...' %
|
|
(self.name, len(weights), expected_num_weights, str(weights)[:50]))
|
|
|
|
weight_index = 0
|
|
weight_value_tuples = []
|
|
for param in params:
|
|
if isinstance(param, base_layer_utils.TrackableWeightHandler):
|
|
num_tensors = param.num_tensors
|
|
tensors = weights[weight_index:weight_index + num_tensors]
|
|
param.set_weights(tensors)
|
|
weight_index += num_tensors
|
|
else:
|
|
weight = weights[weight_index]
|
|
ref_shape = param.shape
|
|
if not ref_shape.is_compatible_with(weight.shape):
|
|
raise ValueError(
|
|
'Layer weight shape %s not compatible with provided weight '
|
|
'shape %s' % (ref_shape, weight.shape))
|
|
weight_value_tuples.append((param, weight))
|
|
weight_index += 1
|
|
|
|
backend.batch_set_value(weight_value_tuples)
|
|
|
|
def get_weights(self):
|
|
"""Returns the current weights of the layer.
|
|
|
|
The weights of a layer represent the state of the layer. This function
|
|
returns both trainable and non-trainable weight values associated with this
|
|
layer as a list of Numpy arrays, which can in turn be used to load state
|
|
into similarly parameterized layers.
|
|
|
|
For example, a Dense layer returns a list of two values-- per-output
|
|
weights and the bias value. These can be used to set the weights of another
|
|
Dense layer:
|
|
|
|
>>> a = tf.keras.layers.Dense(1,
|
|
... kernel_initializer=tf.constant_initializer(1.))
|
|
>>> a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
|
|
>>> a.get_weights()
|
|
[array([[1.],
|
|
[1.],
|
|
[1.]], dtype=float32), array([0.], dtype=float32)]
|
|
>>> b = tf.keras.layers.Dense(1,
|
|
... kernel_initializer=tf.constant_initializer(2.))
|
|
>>> b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
|
|
>>> b.get_weights()
|
|
[array([[2.],
|
|
[2.],
|
|
[2.]], dtype=float32), array([0.], dtype=float32)]
|
|
>>> b.set_weights(a.get_weights())
|
|
>>> b.get_weights()
|
|
[array([[1.],
|
|
[1.],
|
|
[1.]], dtype=float32), array([0.], dtype=float32)]
|
|
|
|
Returns:
|
|
Weights values as a list of numpy arrays.
|
|
"""
|
|
weights = self.weights
|
|
output_weights = []
|
|
for weight in weights:
|
|
if isinstance(weight, base_layer_utils.TrackableWeightHandler):
|
|
output_weights.extend(weight.get_tensors())
|
|
else:
|
|
output_weights.append(weight)
|
|
return backend.batch_get_value(output_weights)
|
|
|
|
@doc_controls.do_not_generate_docs
|
|
def get_updates_for(self, inputs):
|
|
"""Deprecated, do NOT use!
|
|
|
|
Retrieves updates relevant to a specific set of inputs.
|
|
|
|
Arguments:
|
|
inputs: Input tensor or list/tuple of input tensors.
|
|
|
|
Returns:
|
|
List of update ops of the layer that depend on `inputs`.
|
|
"""
|
|
warnings.warn('`layer.get_updates_for` is deprecated and '
|
|
'will be removed in a future version. '
|
|
'Please use `layer.updates` method instead.')
|
|
return self.updates
|
|
|
|
@doc_controls.do_not_generate_docs
|
|
def get_losses_for(self, inputs):
|
|
"""Deprecated, do NOT use!
|
|
|
|
Retrieves losses relevant to a specific set of inputs.
|
|
|
|
Arguments:
|
|
inputs: Input tensor or list/tuple of input tensors.
|
|
|
|
Returns:
|
|
List of loss tensors of the layer that depend on `inputs`.
|
|
"""
|
|
warnings.warn('`layer.get_losses_for` is deprecated and '
|
|
'will be removed in a future version. '
|
|
'Please use `layer.losses` instead.')
|
|
return self.losses
|
|
|
|
@doc_controls.do_not_doc_inheritable
|
|
def get_input_mask_at(self, node_index):
|
|
"""Retrieves the input mask tensor(s) of a layer at a given node.
|
|
|
|
Arguments:
|
|
node_index: Integer, index of the node
|
|
from which to retrieve the attribute.
|
|
E.g. `node_index=0` will correspond to the
|
|
first time the layer was called.
|
|
|
|
Returns:
|
|
A mask tensor
|
|
(or list of tensors if the layer has multiple inputs).
|
|
"""
|
|
inputs = self.get_input_at(node_index)
|
|
if isinstance(inputs, list):
|
|
return [getattr(x, '_keras_mask', None) for x in inputs]
|
|
else:
|
|
return getattr(inputs, '_keras_mask', None)
|
|
|
|
@doc_controls.do_not_doc_inheritable
|
|
def get_output_mask_at(self, node_index):
|
|
"""Retrieves the output mask tensor(s) of a layer at a given node.
|
|
|
|
Arguments:
|
|
node_index: Integer, index of the node
|
|
from which to retrieve the attribute.
|
|
E.g. `node_index=0` will correspond to the
|
|
first time the layer was called.
|
|
|
|
Returns:
|
|
A mask tensor
|
|
(or list of tensors if the layer has multiple outputs).
|
|
"""
|
|
output = self.get_output_at(node_index)
|
|
if isinstance(output, list):
|
|
return [getattr(x, '_keras_mask', None) for x in output]
|
|
else:
|
|
return getattr(output, '_keras_mask', None)
|
|
|
|
@property
|
|
@doc_controls.do_not_doc_inheritable
|
|
def input_mask(self):
|
|
"""Retrieves the input mask tensor(s) of a layer.
|
|
|
|
Only applicable if the layer has exactly one inbound node,
|
|
i.e. if it is connected to one incoming layer.
|
|
|
|
Returns:
|
|
Input mask tensor (potentially None) or list of input
|
|
mask tensors.
|
|
|
|
Raises:
|
|
AttributeError: if the layer is connected to
|
|
more than one incoming layers.
|
|
"""
|
|
inputs = self.input
|
|
if isinstance(inputs, list):
|
|
return [getattr(x, '_keras_mask', None) for x in inputs]
|
|
else:
|
|
return getattr(inputs, '_keras_mask', None)
|
|
|
|
@property
|
|
@doc_controls.do_not_doc_inheritable
|
|
def output_mask(self):
|
|
"""Retrieves the output mask tensor(s) of a layer.
|
|
|
|
Only applicable if the layer has exactly one inbound node,
|
|
i.e. if it is connected to one incoming layer.
|
|
|
|
Returns:
|
|
Output mask tensor (potentially None) or list of output
|
|
mask tensors.
|
|
|
|
Raises:
|
|
AttributeError: if the layer is connected to
|
|
more than one incoming layers.
|
|
"""
|
|
output = self.output
|
|
if isinstance(output, list):
|
|
return [getattr(x, '_keras_mask', None) for x in output]
|
|
else:
|
|
return getattr(output, '_keras_mask', None)
|
|
|
|
@doc_controls.do_not_doc_inheritable
|
|
def get_input_shape_at(self, node_index):
|
|
"""Retrieves the input shape(s) of a layer at a given node.
|
|
|
|
Arguments:
|
|
node_index: Integer, index of the node
|
|
from which to retrieve the attribute.
|
|
E.g. `node_index=0` will correspond to the
|
|
first time the layer was called.
|
|
|
|
Returns:
|
|
A shape tuple
|
|
(or list of shape tuples if the layer has multiple inputs).
|
|
|
|
Raises:
|
|
RuntimeError: If called in Eager mode.
|
|
"""
|
|
return self._get_node_attribute_at_index(node_index, 'input_shapes',
|
|
'input shape')
|
|
|
|
@doc_controls.do_not_doc_inheritable
|
|
def get_output_shape_at(self, node_index):
|
|
"""Retrieves the output shape(s) of a layer at a given node.
|
|
|
|
Arguments:
|
|
node_index: Integer, index of the node
|
|
from which to retrieve the attribute.
|
|
E.g. `node_index=0` will correspond to the
|
|
first time the layer was called.
|
|
|
|
Returns:
|
|
A shape tuple
|
|
(or list of shape tuples if the layer has multiple outputs).
|
|
|
|
Raises:
|
|
RuntimeError: If called in Eager mode.
|
|
"""
|
|
return self._get_node_attribute_at_index(node_index, 'output_shapes',
|
|
'output shape')
|
|
|
|
@doc_controls.do_not_doc_inheritable
|
|
def get_input_at(self, node_index):
|
|
"""Retrieves the input tensor(s) of a layer at a given node.
|
|
|
|
Arguments:
|
|
node_index: Integer, index of the node
|
|
from which to retrieve the attribute.
|
|
E.g. `node_index=0` will correspond to the
|
|
first time the layer was called.
|
|
|
|
Returns:
|
|
A tensor (or list of tensors if the layer has multiple inputs).
|
|
|
|
Raises:
|
|
RuntimeError: If called in Eager mode.
|
|
"""
|
|
return self._get_node_attribute_at_index(node_index, 'input_tensors',
|
|
'input')
|
|
|
|
@doc_controls.do_not_doc_inheritable
|
|
def get_output_at(self, node_index):
|
|
"""Retrieves the output tensor(s) of a layer at a given node.
|
|
|
|
Arguments:
|
|
node_index: Integer, index of the node
|
|
from which to retrieve the attribute.
|
|
E.g. `node_index=0` will correspond to the
|
|
first time the layer was called.
|
|
|
|
Returns:
|
|
A tensor (or list of tensors if the layer has multiple outputs).
|
|
|
|
Raises:
|
|
RuntimeError: If called in Eager mode.
|
|
"""
|
|
return self._get_node_attribute_at_index(node_index, 'output_tensors',
|
|
'output')
|
|
|
|
@property
|
|
def input(self):
|
|
"""Retrieves the input tensor(s) of a layer.
|
|
|
|
Only applicable if the layer has exactly one input,
|
|
i.e. if it is connected to one incoming layer.
|
|
|
|
Returns:
|
|
Input tensor or list of input tensors.
|
|
|
|
Raises:
|
|
RuntimeError: If called in Eager mode.
|
|
AttributeError: If no inbound nodes are found.
|
|
"""
|
|
if not self._inbound_nodes:
|
|
raise AttributeError('Layer ' + self.name +
|
|
' is not connected, no input to return.')
|
|
return self._get_node_attribute_at_index(0, 'input_tensors', 'input')
|
|
|
|
@property
|
|
def output(self):
|
|
"""Retrieves the output tensor(s) of a layer.
|
|
|
|
Only applicable if the layer has exactly one output,
|
|
i.e. if it is connected to one incoming layer.
|
|
|
|
Returns:
|
|
Output tensor or list of output tensors.
|
|
|
|
Raises:
|
|
AttributeError: if the layer is connected to more than one incoming
|
|
layers.
|
|
RuntimeError: if called in Eager mode.
|
|
"""
|
|
if not self._inbound_nodes:
|
|
raise AttributeError('Layer ' + self.name + ' has no inbound nodes.')
|
|
return self._get_node_attribute_at_index(0, 'output_tensors', 'output')
|
|
|
|
@property
|
|
@doc_controls.do_not_doc_inheritable
|
|
def input_shape(self):
|
|
"""Retrieves the input shape(s) of a layer.
|
|
|
|
Only applicable if the layer has exactly one input,
|
|
i.e. if it is connected to one incoming layer, or if all inputs
|
|
have the same shape.
|
|
|
|
Returns:
|
|
Input shape, as an integer shape tuple
|
|
(or list of shape tuples, one tuple per input tensor).
|
|
|
|
Raises:
|
|
AttributeError: if the layer has no defined input_shape.
|
|
RuntimeError: if called in Eager mode.
|
|
"""
|
|
if not self._inbound_nodes:
|
|
raise AttributeError('The layer has never been called '
|
|
'and thus has no defined input shape.')
|
|
all_input_shapes = set(
|
|
[str(node.input_shapes) for node in self._inbound_nodes])
|
|
if len(all_input_shapes) == 1:
|
|
return self._inbound_nodes[0].input_shapes
|
|
else:
|
|
raise AttributeError('The layer "' + str(self.name) +
|
|
' has multiple inbound nodes, '
|
|
'with different input shapes. Hence '
|
|
'the notion of "input shape" is '
|
|
'ill-defined for the layer. '
|
|
'Use `get_input_shape_at(node_index)` '
|
|
'instead.')
|
|
|
|
def count_params(self):
|
|
"""Count the total number of scalars composing the weights.
|
|
|
|
Returns:
|
|
An integer count.
|
|
|
|
Raises:
|
|
ValueError: if the layer isn't yet built
|
|
(in which case its weights aren't yet defined).
|
|
"""
|
|
if not self.built:
|
|
if getattr(self, '_is_graph_network', False):
|
|
with tf_utils.maybe_init_scope(self):
|
|
self._maybe_build(self.inputs)
|
|
else:
|
|
raise ValueError('You tried to call `count_params` on ' + self.name +
|
|
', but the layer isn\'t built. '
|
|
'You can build it manually via: `' + self.name +
|
|
'.build(batch_input_shape)`.')
|
|
return layer_utils.count_params(self.weights)
|
|
|
|
@property
|
|
@doc_controls.do_not_doc_inheritable
|
|
def output_shape(self):
|
|
"""Retrieves the output shape(s) of a layer.
|
|
|
|
Only applicable if the layer has one output,
|
|
or if all outputs have the same shape.
|
|
|
|
Returns:
|
|
Output shape, as an integer shape tuple
|
|
(or list of shape tuples, one tuple per output tensor).
|
|
|
|
Raises:
|
|
AttributeError: if the layer has no defined output shape.
|
|
RuntimeError: if called in Eager mode.
|
|
"""
|
|
if not self._inbound_nodes:
|
|
raise AttributeError('The layer has never been called '
|
|
'and thus has no defined output shape.')
|
|
all_output_shapes = set(
|
|
[str(node.output_shapes) for node in self._inbound_nodes])
|
|
if len(all_output_shapes) == 1:
|
|
return self._inbound_nodes[0].output_shapes
|
|
else:
|
|
raise AttributeError('The layer "%s"'
|
|
' has multiple inbound nodes, '
|
|
'with different output shapes. Hence '
|
|
'the notion of "output shape" is '
|
|
'ill-defined for the layer. '
|
|
'Use `get_output_shape_at(node_index)` '
|
|
'instead.' % self.name)
|
|
|
|
@property
|
|
@doc_controls.do_not_doc_inheritable
|
|
def inbound_nodes(self):
|
|
"""Deprecated, do NOT use! Only for compatibility with external Keras."""
|
|
return self._inbound_nodes
|
|
|
|
@property
|
|
@doc_controls.do_not_doc_inheritable
|
|
def outbound_nodes(self):
|
|
"""Deprecated, do NOT use! Only for compatibility with external Keras."""
|
|
return self._outbound_nodes
|
|
|
|
##############################################################################
|
|
# Methods & attributes below are public aliases of other methods. #
|
|
##############################################################################
|
|
|
|
@doc_controls.do_not_doc_inheritable
|
|
def apply(self, inputs, *args, **kwargs):
|
|
"""Deprecated, do NOT use!
|
|
|
|
This is an alias of `self.__call__`.
|
|
|
|
Arguments:
|
|
inputs: Input tensor(s).
|
|
*args: additional positional arguments to be passed to `self.call`.
|
|
**kwargs: additional keyword arguments to be passed to `self.call`.
|
|
|
|
Returns:
|
|
Output tensor(s).
|
|
"""
|
|
warnings.warn('`layer.apply` is deprecated and '
|
|
'will be removed in a future version. '
|
|
'Please use `layer.__call__` method instead.')
|
|
return self.__call__(inputs, *args, **kwargs)
|
|
|
|
@doc_controls.do_not_doc_inheritable
|
|
def add_variable(self, *args, **kwargs):
|
|
"""Deprecated, do NOT use! Alias for `add_weight`."""
|
|
warnings.warn('`layer.add_variable` is deprecated and '
|
|
'will be removed in a future version. '
|
|
'Please use `layer.add_weight` method instead.')
|
|
return self.add_weight(*args, **kwargs)
|
|
|
|
@property
|
|
@doc_controls.do_not_generate_docs
|
|
def variables(self):
|
|
"""Returns the list of all layer variables/weights.
|
|
|
|
Alias of `self.weights`.
|
|
|
|
Note: This will not track the weights of nested `tf.Modules` that are not
|
|
themselves Keras layers.
|
|
|
|
Returns:
|
|
A list of variables.
|
|
"""
|
|
return self.weights
|
|
|
|
@property
|
|
@doc_controls.do_not_generate_docs
|
|
def trainable_variables(self):
|
|
return self.trainable_weights
|
|
|
|
@property
|
|
@doc_controls.do_not_generate_docs
|
|
def non_trainable_variables(self):
|
|
return self.non_trainable_weights
|
|
|
|
##############################################################################
|
|
# Methods & attributes below are all private and only used by the framework. #
|
|
##############################################################################
|
|
|
|
@property
|
|
def _inbound_nodes(self):
|
|
return self._inbound_nodes_value
|
|
|
|
@_inbound_nodes.setter
|
|
@trackable.no_automatic_dependency_tracking
|
|
def _inbound_nodes(self, value):
|
|
self._inbound_nodes_value = value
|
|
|
|
@property
|
|
def _outbound_nodes(self):
|
|
return self._outbound_nodes_value
|
|
|
|
@_outbound_nodes.setter
|
|
@trackable.no_automatic_dependency_tracking
|
|
def _outbound_nodes(self, value):
|
|
self._outbound_nodes_value = value
|
|
|
|
def _set_dtype_policy(self, dtype):
|
|
"""Sets self._dtype_policy."""
|
|
if isinstance(dtype, policy.Policy):
|
|
self._dtype_policy = dtype
|
|
elif isinstance(dtype, dict):
|
|
self._dtype_policy = policy.deserialize(dtype)
|
|
elif dtype:
|
|
self._dtype_policy = policy.Policy(dtypes.as_dtype(dtype).name)
|
|
else:
|
|
self._dtype_policy = policy.global_policy()
|
|
if (self._dtype_policy.name == 'mixed_float16' and
|
|
not loss_scale_optimizer.strategy_supports_loss_scaling()):
|
|
# Although only loss scaling doesn't support certain strategies, to avoid
|
|
# confusion, we disallow the 'mixed_float16' policy with unsupported
|
|
# strategies. This is because 'mixed_float16' requires loss scaling for
|
|
# numeric stability.
|
|
strategy = ds_context.get_strategy()
|
|
raise ValueError('Mixed precision is not supported with the '
|
|
'tf.distribute.Strategy: %s. Either stop using mixed '
|
|
'precision by removing the use of the "%s" policy or '
|
|
'use a different Strategy, e.g. a MirroredStrategy.' %
|
|
(strategy.__class__.__name__, self._dtype_policy.name))
|
|
|
|
# Performance optimization: cache the compute dtype as a Dtype object or
|
|
# None, so that str to Dtype conversion doesn't happen in Layer.__call__.
|
|
# TODO(b/157486353): Investigate returning DTypes in Policy.
|
|
if self._dtype_policy.compute_dtype:
|
|
self._compute_dtype_object = dtypes.as_dtype(
|
|
self._dtype_policy.compute_dtype)
|
|
else:
|
|
self._compute_dtype_object = None
|
|
|
|
@property
|
|
def dtype_policy(self):
|
|
"""The dtype policy associated with this layer.
|
|
|
|
This is an instance of a `tf.keras.mixed_precision.Policy`.
|
|
"""
|
|
return self._dtype_policy
|
|
|
|
@property
|
|
def compute_dtype(self):
|
|
"""The dtype of the layer's computations.
|
|
|
|
This is equivalent to `Layer.dtype_policy.compute_dtype`. Unless
|
|
mixed precision is used, this is the same as `Layer.dtype`, the dtype of
|
|
the weights.
|
|
|
|
Layers automatically cast their inputs to the compute dtype, which causes
|
|
computations and the output to be in the compute dtype as well. This is done
|
|
by the base Layer class in `Layer.__call__`, so you do not have to insert
|
|
these casts if implementing your own layer.
|
|
|
|
Layers often perform certain internal computations in higher precision when
|
|
`compute_dtype` is float16 or bfloat16 for numeric stability. The output
|
|
will still typically be float16 or bfloat16 in such cases.
|
|
|
|
Returns:
|
|
The layer's compute dtype.
|
|
"""
|
|
return self._dtype_policy.compute_dtype
|
|
|
|
@property
|
|
def _compute_dtype(self):
|
|
"""Deprecated alias of `compute_dtype`."""
|
|
return self._dtype_policy.compute_dtype
|
|
|
|
@property
|
|
def variable_dtype(self):
|
|
"""Alias of `Layer.dtype`, the dtype of the weights."""
|
|
return self.dtype
|
|
|
|
def _maybe_cast_inputs(self, inputs, input_list=None):
|
|
"""Maybe casts the inputs to the compute dtype.
|
|
|
|
If self._compute_dtype is floating-point, and self_autocast is True,
|
|
floating-point inputs are casted to self._compute_dtype.
|
|
|
|
Args:
|
|
inputs: Input tensor, or structure of input tensors.
|
|
input_list: Flat list of input tensors.
|
|
|
|
Returns:
|
|
`inputs`, but tensors may have been casted to self._compute_dtype
|
|
"""
|
|
if not input_list:
|
|
input_list = nest.flatten(inputs)
|
|
|
|
compute_dtype_object = self._compute_dtype_object
|
|
should_autocast = (
|
|
self._autocast and compute_dtype_object and
|
|
compute_dtype_object.is_floating)
|
|
|
|
if (should_autocast and
|
|
any(map(self._should_cast_single_input, input_list))):
|
|
# Only perform expensive `nest` operation when needed.
|
|
return nest.map_structure(self._cast_single_input, inputs)
|
|
else:
|
|
return inputs
|
|
|
|
def _should_cast_single_input(self, x):
|
|
if isinstance(x, _AUTOCAST_TYPES):
|
|
return (self._compute_dtype_object and
|
|
x.dtype != self._compute_dtype_object and x.dtype.is_floating)
|
|
return False
|
|
|
|
def _cast_single_input(self, x):
|
|
"""Cast a single Tensor or TensorSpec to the compute dtype."""
|
|
if self._should_cast_single_input(x):
|
|
return math_ops.cast(x, self._compute_dtype_object)
|
|
else:
|
|
return x
|
|
|
|
# _dtype used to be an attribute set in the constructor. We still expose it
|
|
# because some clients still use it.
|
|
# TODO(reedwm): Deprecate, then remove the _dtype property.
|
|
@property
|
|
def _dtype(self):
|
|
# This is equivalent to returning self.dtype . We do not return self.dtype
|
|
# as it would cause infinite recursion in a few subclasses, which override
|
|
# "dtype" to return self._dtype.
|
|
return self._dtype_policy.variable_dtype
|
|
|
|
@_dtype.setter
|
|
def _dtype(self, value):
|
|
value = dtypes.as_dtype(value).name
|
|
self._set_dtype_policy(policy.Policy(value))
|
|
|
|
def _name_scope(self):
|
|
if not tf2.enabled():
|
|
return self.name
|
|
name_scope = self.name
|
|
current_name_scope = ops.get_name_scope()
|
|
if current_name_scope:
|
|
name_scope = current_name_scope + '/' + name_scope
|
|
if name_scope:
|
|
# Note that the trailing `/` prevents autogenerated
|
|
# numerical suffixes to get appended. It will also fully reset
|
|
# nested name scope (i.e. the outer name scope has no effect).
|
|
name_scope += '/'
|
|
return name_scope
|
|
|
|
def _init_set_name(self, name, zero_based=True):
|
|
if not name:
|
|
self._name = backend.unique_object_name(
|
|
generic_utils.to_snake_case(self.__class__.__name__),
|
|
zero_based=zero_based)
|
|
else:
|
|
backend.observe_object_name(name)
|
|
self._name = name
|
|
|
|
def _get_existing_metric(self, name=None):
|
|
match = [m for m in self._metrics if m.name == name]
|
|
if not match:
|
|
return
|
|
if len(match) > 1:
|
|
raise ValueError(
|
|
'Please provide different names for the metrics you have added. '
|
|
'We found {} metrics with the name: "{}"'.format(len(match), name))
|
|
return match[0]
|
|
|
|
def _handle_weight_regularization(self, name, variable, regularizer):
|
|
"""Create lambdas which compute regularization losses."""
|
|
|
|
def _loss_for_variable(v):
|
|
"""Creates a regularization loss `Tensor` for variable `v`."""
|
|
with backend.name_scope(name + '/Regularizer'):
|
|
regularization = regularizer(v)
|
|
return regularization
|
|
|
|
if base_layer_utils.is_split_variable(variable):
|
|
for v in variable:
|
|
self.add_loss(functools.partial(_loss_for_variable, v))
|
|
else:
|
|
self.add_loss(functools.partial(_loss_for_variable, variable))
|
|
|
|
def _handle_activity_regularization(self, inputs, outputs):
|
|
# Apply activity regularization.
|
|
# Note that it should be applied every time the layer creates a new
|
|
# output, since it is output-specific.
|
|
if self._activity_regularizer:
|
|
output_list = nest.flatten(outputs)
|
|
with backend.name_scope('ActivityRegularizer'):
|
|
for output in output_list:
|
|
activity_loss = self._activity_regularizer(output)
|
|
batch_size = math_ops.cast(
|
|
array_ops.shape(output)[0], activity_loss.dtype)
|
|
# Make activity regularization strength batch-agnostic.
|
|
mean_activity_loss = activity_loss / batch_size
|
|
self.add_loss(mean_activity_loss)
|
|
|
|
def _set_mask_metadata(self, inputs, outputs, previous_mask, build_graph):
|
|
# Many `Layer`s don't need to call `compute_mask`.
|
|
# This method is optimized to do as little work as needed for the common
|
|
# case.
|
|
if not self._supports_masking:
|
|
return
|
|
|
|
flat_outputs = nest.flatten(outputs)
|
|
|
|
mask_already_computed = (
|
|
getattr(self, '_compute_output_and_mask_jointly', False) or
|
|
all(getattr(x, '_keras_mask', None) is not None for x in flat_outputs))
|
|
if mask_already_computed:
|
|
if build_graph:
|
|
self._set_mask_keras_history_checked(flat_outputs)
|
|
return
|
|
|
|
output_masks = self.compute_mask(inputs, previous_mask)
|
|
if output_masks is None:
|
|
return
|
|
|
|
flat_masks = nest.flatten(output_masks)
|
|
for tensor, mask in zip(flat_outputs, flat_masks):
|
|
try:
|
|
tensor._keras_mask = mask
|
|
except AttributeError:
|
|
# C Type such as np.ndarray.
|
|
pass
|
|
|
|
if build_graph:
|
|
self._set_mask_keras_history_checked(flat_outputs)
|
|
|
|
def _set_mask_keras_history_checked(self, flat_outputs):
|
|
for output in flat_outputs:
|
|
if getattr(output, '_keras_mask', None) is not None:
|
|
# Do not track masks for `TensorFlowOpLayer` construction.
|
|
output._keras_mask._keras_history_checked = True
|
|
|
|
def _get_input_masks(self, inputs, input_list, args, kwargs):
|
|
if not self._supports_masking and not self._expects_mask_arg:
|
|
# Input masks only need to be retrieved if they are needed for `call`
|
|
# or `compute_mask`.
|
|
input_masks = None
|
|
implicit_mask = False
|
|
elif self._call_arg_was_passed('mask', args, kwargs):
|
|
input_masks = self._get_call_arg_value('mask', args, kwargs)
|
|
implicit_mask = False
|
|
else:
|
|
input_masks = [getattr(t, '_keras_mask', None) for t in input_list]
|
|
if all(mask is None for mask in input_masks):
|
|
input_masks = None
|
|
implicit_mask = False
|
|
else:
|
|
# Only do expensive `nest` op when masking is actually being used.
|
|
input_masks = nest.pack_sequence_as(inputs, input_masks)
|
|
implicit_mask = True
|
|
return input_masks, implicit_mask
|
|
|
|
def _call_arg_was_passed(self, arg_name, args, kwargs, inputs_in_args=False):
|
|
# Performance optimization: do no work in most common case.
|
|
if not args and not kwargs:
|
|
return False
|
|
|
|
if arg_name in kwargs:
|
|
return True
|
|
call_fn_args = self._call_fn_args
|
|
if not inputs_in_args:
|
|
# Ignore `inputs` arg.
|
|
call_fn_args = call_fn_args[1:]
|
|
return arg_name in dict(zip(call_fn_args, args))
|
|
|
|
def _get_call_arg_value(self, arg_name, args, kwargs, inputs_in_args=False):
|
|
if arg_name in kwargs:
|
|
return kwargs[arg_name]
|
|
call_fn_args = self._call_fn_args
|
|
if not inputs_in_args:
|
|
# Ignore `inputs` arg.
|
|
call_fn_args = call_fn_args[1:]
|
|
args_dict = dict(zip(call_fn_args, args))
|
|
return args_dict[arg_name]
|
|
|
|
def _set_call_arg_value(
|
|
self, arg_name, new_value, args,
|
|
kwargs, inputs_in_args=False, pop_kwarg_if_none=False):
|
|
arg_pos = self._call_fn_arg_positions.get(arg_name, None)
|
|
if arg_pos is not None:
|
|
if not inputs_in_args:
|
|
# Ignore `inputs` arg.
|
|
arg_pos = arg_pos - 1
|
|
if len(args) > arg_pos:
|
|
args = list(args)
|
|
args[arg_pos] = new_value
|
|
return tuple(args), kwargs
|
|
if new_value is None and pop_kwarg_if_none:
|
|
kwargs.pop(arg_name, None)
|
|
else:
|
|
kwargs[arg_name] = new_value
|
|
return args, kwargs
|
|
|
|
def _set_connectivity_metadata(self, args, kwargs, outputs):
|
|
# If the layer returns tensors from its inputs unmodified,
|
|
# we copy them to avoid loss of KerasHistory metadata.
|
|
flat_outputs = nest.flatten(outputs)
|
|
flat_inputs = nest.flatten((args, kwargs))
|
|
input_ids_set = {id(i) for i in flat_inputs}
|
|
outputs_copy = []
|
|
for x in flat_outputs:
|
|
if id(x) in input_ids_set:
|
|
with backend.name_scope(self.name):
|
|
x = array_ops.identity(x)
|
|
outputs_copy.append(x)
|
|
outputs = nest.pack_sequence_as(outputs, outputs_copy)
|
|
|
|
# Create node, Node wires itself to inbound and outbound layers.
|
|
# The Node constructor actually updates this layer's self._inbound_nodes,
|
|
# sets _keras_history on the outputs, and adds itself to the
|
|
# `_outbound_nodes` of the layers that produced the inputs to this
|
|
# layer call.
|
|
node_module.Node(self, call_args=args, call_kwargs=kwargs, outputs=outputs)
|
|
return outputs
|
|
|
|
def _get_node_attribute_at_index(self, node_index, attr, attr_name):
|
|
"""Private utility to retrieves an attribute (e.g. inputs) from a node.
|
|
|
|
This is used to implement the methods:
|
|
- get_input_shape_at
|
|
- get_output_shape_at
|
|
- get_input_at
|
|
etc...
|
|
|
|
Arguments:
|
|
node_index: Integer index of the node from which
|
|
to retrieve the attribute.
|
|
attr: Exact node attribute name.
|
|
attr_name: Human-readable attribute name, for error messages.
|
|
|
|
Returns:
|
|
The layer's attribute `attr` at the node of index `node_index`.
|
|
|
|
Raises:
|
|
RuntimeError: If the layer has no inbound nodes, or if called in Eager
|
|
mode.
|
|
ValueError: If the index provided does not match any node.
|
|
"""
|
|
if not self._inbound_nodes:
|
|
raise RuntimeError('The layer has never been called '
|
|
'and thus has no defined ' + attr_name + '.')
|
|
if not len(self._inbound_nodes) > node_index:
|
|
raise ValueError('Asked to get ' + attr_name + ' at node ' +
|
|
str(node_index) + ', but the layer has only ' +
|
|
str(len(self._inbound_nodes)) + ' inbound nodes.')
|
|
values = getattr(self._inbound_nodes[node_index], attr)
|
|
if isinstance(values, list) and len(values) == 1:
|
|
return values[0]
|
|
else:
|
|
return values
|
|
|
|
def _maybe_build(self, inputs):
|
|
# Check input assumptions set before layer building, e.g. input rank.
|
|
if not self.built:
|
|
input_spec.assert_input_compatibility(
|
|
self.input_spec, inputs, self.name)
|
|
input_list = nest.flatten(inputs)
|
|
if input_list and self._dtype_policy.compute_dtype is None:
|
|
try:
|
|
dtype = input_list[0].dtype.base_dtype.name
|
|
except AttributeError:
|
|
pass
|
|
else:
|
|
self._set_dtype_policy(policy.Policy(dtype))
|
|
input_shapes = None
|
|
# Converts Tensors / CompositeTensors to TensorShapes.
|
|
if all(hasattr(x, 'shape') for x in input_list):
|
|
input_shapes = tf_utils.get_shapes(inputs)
|
|
else:
|
|
# Converts input shape to TensorShapes.
|
|
try:
|
|
input_shapes = tf_utils.convert_shapes(inputs, to_tuples=False)
|
|
except ValueError:
|
|
pass
|
|
# Only call `build` if the user has manually overridden the build method.
|
|
if not hasattr(self.build, '_is_default'):
|
|
# Any setup work performed only once should happen in an `init_scope`
|
|
# to avoid creating symbolic Tensors that will later pollute any eager
|
|
# operations.
|
|
with tf_utils.maybe_init_scope(self):
|
|
self.build(input_shapes) # pylint:disable=not-callable
|
|
# We must set also ensure that the layer is marked as built, and the build
|
|
# shape is stored since user defined build functions may not be calling
|
|
# `super.build()`
|
|
Layer.build(self, input_shapes)
|
|
|
|
# Optionally load weight values specified at layer instantiation.
|
|
if self._initial_weights is not None:
|
|
if ops.executing_eagerly_outside_functions():
|
|
with ops.init_scope():
|
|
# Using `init_scope` since we want variable assignment in
|
|
# `set_weights` to be treated like variable initialization.
|
|
self.set_weights(self._initial_weights)
|
|
else:
|
|
self.set_weights(self._initial_weights)
|
|
self._initial_weights = None
|
|
|
|
def _symbolic_call(self, inputs):
|
|
input_shapes = nest.map_structure(lambda x: x.shape, inputs)
|
|
output_shapes = self.compute_output_shape(input_shapes)
|
|
# Convert to TensorShape so that nest.map_structure will not map into
|
|
# individual dim of the shape.
|
|
output_shapes = tf_utils.convert_shapes(output_shapes, to_tuples=False)
|
|
|
|
def _make_placeholder_like(shape):
|
|
ph = backend.placeholder(shape=shape, dtype=self.dtype)
|
|
ph._keras_mask = None
|
|
return ph
|
|
return nest.map_structure(_make_placeholder_like, output_shapes)
|
|
|
|
def _get_trainable_state(self):
|
|
"""Get the `trainable` state of each sublayer.
|
|
|
|
Returns:
|
|
A dict mapping all sublayers to their `trainable` value.
|
|
"""
|
|
trainable_state = weakref.WeakKeyDictionary()
|
|
for layer in self._flatten_layers():
|
|
trainable_state[layer] = layer.trainable
|
|
return trainable_state
|
|
|
|
def _set_trainable_state(self, trainable_state):
|
|
"""Set `trainable` state for each sublayer."""
|
|
for layer in self._flatten_layers():
|
|
if layer in trainable_state:
|
|
layer.trainable = trainable_state[layer]
|
|
|
|
@property
|
|
def _obj_reference_counts(self):
|
|
"""A dictionary counting the number of attributes referencing an object."""
|
|
self._maybe_create_attribute('_obj_reference_counts_dict',
|
|
object_identity.ObjectIdentityDictionary())
|
|
return self._obj_reference_counts_dict
|
|
|
|
@trackable.no_automatic_dependency_tracking
|
|
def _maybe_create_attribute(self, name, default_value):
|
|
"""Create the attribute with the default value if it hasn't been created.
|
|
|
|
This is useful for fields that is used for tracking purpose,
|
|
_trainable_weights, or _layers. Note that user could create a layer subclass
|
|
and assign an internal field before invoking the Layer.__init__(), the
|
|
__setattr__() need to create the tracking fields and __init__() need to not
|
|
override them.
|
|
|
|
Args:
|
|
name: String, the name of the attribute.
|
|
default_value: Object, the default value of the attribute.
|
|
"""
|
|
if not hasattr(self, name):
|
|
super(Layer, self).__setattr__(name, default_value)
|
|
|
|
def __delattr__(self, name):
|
|
# For any super.__delattr__() call, we will directly use the implementation
|
|
# in Trackable and skip the behavior in AutoTrackable. The Layer was
|
|
# originally use Trackable as base class, the change of using Module as base
|
|
# class forced us to have AutoTrackable in the class hierarchy. Skipping
|
|
# the __delattr__ and __setattr__ in AutoTrackable will keep the status quo.
|
|
existing_value = getattr(self, name, None)
|
|
|
|
# If this value is replacing an existing object assigned to an attribute, we
|
|
# should clean it out to avoid leaking memory. First we check if there are
|
|
# other attributes referencing it.
|
|
reference_counts = self._obj_reference_counts
|
|
if existing_value not in reference_counts:
|
|
super(tracking.AutoTrackable, self).__delattr__(name)
|
|
return
|
|
|
|
reference_count = reference_counts[existing_value]
|
|
if reference_count > 1:
|
|
# There are other remaining references. We can't remove this object from
|
|
# _layers etc.
|
|
reference_counts[existing_value] = reference_count - 1
|
|
super(tracking.AutoTrackable, self).__delattr__(name)
|
|
return
|
|
else:
|
|
# This is the last remaining reference.
|
|
del reference_counts[existing_value]
|
|
|
|
super(tracking.AutoTrackable, self).__delattr__(name)
|
|
|
|
if (isinstance(existing_value, Layer)
|
|
or base_layer_utils.has_weights(existing_value)):
|
|
super(tracking.AutoTrackable, self).__setattr__(
|
|
'_layers',
|
|
[l for l in self._layers if l is not existing_value])
|
|
if isinstance(existing_value, tf_variables.Variable):
|
|
super(tracking.AutoTrackable, self).__setattr__(
|
|
'_trainable_weights',
|
|
[w for w in self._trainable_weights if w is not existing_value])
|
|
super(tracking.AutoTrackable, self).__setattr__(
|
|
'_non_trainable_weights',
|
|
[w for w in self._non_trainable_weights if w is not existing_value])
|
|
|
|
def __setattr__(self, name, value):
|
|
if (name == '_self_setattr_tracking' or
|
|
not getattr(self, '_self_setattr_tracking', True) or
|
|
# Exclude @property.setters from tracking
|
|
hasattr(self.__class__, name)):
|
|
try:
|
|
super(tracking.AutoTrackable, self).__setattr__(name, value)
|
|
except AttributeError:
|
|
raise AttributeError(
|
|
('Can\'t set the attribute "{}", likely because it conflicts with '
|
|
'an existing read-only @property of the object. Please choose a '
|
|
'different name.').format(name))
|
|
return
|
|
|
|
# Keep track of trackable objects, for the needs of `Network.save_weights`.
|
|
value = data_structures.sticky_attribute_assignment(
|
|
trackable=self, value=value, name=name)
|
|
|
|
reference_counts = self._obj_reference_counts
|
|
reference_counts[value] = reference_counts.get(value, 0) + 1
|
|
|
|
# Clean out the old attribute, which clears _layers and _trainable_weights
|
|
# if necessary.
|
|
try:
|
|
self.__delattr__(name)
|
|
except AttributeError:
|
|
pass
|
|
|
|
# Keep track of metric instance created in subclassed layer.
|
|
for val in nest.flatten(value):
|
|
if isinstance(val, metrics_mod.Metric) and hasattr(self, '_metrics'):
|
|
self._metrics.append(val)
|
|
|
|
# TODO(scottzhu): Need to track Module object as well for weight tracking.
|
|
# Be careful about metric if it becomes a Module in future.
|
|
# Append value to self._layers if relevant
|
|
if (getattr(self, '_auto_track_sub_layers', True) and
|
|
(isinstance(value, Layer) or base_layer_utils.has_weights(value))):
|
|
self._maybe_create_attribute('_layers', [])
|
|
# We need to check object identity to avoid de-duplicating empty
|
|
# container types which compare equal.
|
|
if not any((layer is value for layer in self._layers)):
|
|
self._layers.append(value)
|
|
if hasattr(value, '_use_resource_variables'):
|
|
# Legacy layers (V1 tf.layers) must always use
|
|
# resource variables.
|
|
value._use_resource_variables = True
|
|
|
|
# Append value to list of trainable / non-trainable weights if relevant
|
|
# TODO(b/125122625): This won't pick up on any variables added to a
|
|
# list/dict after creation.
|
|
for val in nest.flatten(value, expand_composites=True):
|
|
# TODO(b/126450014): Remove `_UnreadVariable` check here when assign ops
|
|
# no longer return True for isinstance Variable checks.
|
|
if not isinstance(val, tf_variables.Variable):
|
|
continue
|
|
if isinstance(val, resource_variable_ops._UnreadVariable): # pylint: disable=protected-access
|
|
continue
|
|
|
|
# Users may add extra weights/variables
|
|
# simply by assigning them to attributes (invalid for graph networks)
|
|
self._maybe_create_attribute('_trainable_weights', [])
|
|
self._maybe_create_attribute('_non_trainable_weights', [])
|
|
if val.trainable:
|
|
if any(val is w for w in self._trainable_weights):
|
|
continue
|
|
self._trainable_weights.append(val)
|
|
else:
|
|
if any(val is w for w in self._non_trainable_weights):
|
|
continue
|
|
self._non_trainable_weights.append(val)
|
|
|
|
backend.track_variable(val)
|
|
|
|
# Skip the auto trackable from tf.Module to keep status quo. See the comment
|
|
# at __delattr__.
|
|
super(tracking.AutoTrackable, self).__setattr__(name, value)
|
|
|
|
def _gather_children_attribute(self, attribute):
|
|
assert attribute in {
|
|
'weights', 'trainable_weights', 'non_trainable_weights'
|
|
}
|
|
if hasattr(self, '_layers'):
|
|
nested_layers = layer_utils.filter_empty_layer_containers(
|
|
self._layers)
|
|
return list(
|
|
itertools.chain.from_iterable(
|
|
getattr(layer, attribute) for layer in nested_layers))
|
|
return []
|
|
|
|
def _flatten_layers(self, recursive=True, include_self=True):
|
|
if include_self:
|
|
yield self
|
|
|
|
# Only instantiate set and deque if needed.
|
|
layers_or_containers = getattr(self, '_layers', None)
|
|
if layers_or_containers:
|
|
seen_object_ids = set()
|
|
deque = collections.deque(layers_or_containers)
|
|
while deque:
|
|
layer_or_container = deque.popleft()
|
|
|
|
layer_or_container_id = id(layer_or_container)
|
|
if layer_or_container_id in seen_object_ids:
|
|
continue
|
|
seen_object_ids.add(layer_or_container_id)
|
|
|
|
if (isinstance(layer_or_container, Layer) and
|
|
not isinstance(layer_or_container, metrics_mod.Metric)):
|
|
yield layer_or_container
|
|
# Introspect recursively through sublayers.
|
|
if recursive:
|
|
sublayers = getattr(layer_or_container, '_layers', None)
|
|
if sublayers:
|
|
deque.extendleft(reversed(sublayers))
|
|
elif isinstance(layer_or_container,
|
|
data_structures.TrackableDataStructure):
|
|
# Data structures are introspected even with `recursive=False`.
|
|
tracked_values = layer_or_container._values
|
|
if tracked_values:
|
|
deque.extendleft(reversed(tracked_values))
|
|
|
|
# This is a hack so that the is_layer (within
|
|
# training/trackable/layer_utils.py) check doesn't get the weights attr.
|
|
# TODO(b/110718070): Remove when fixed.
|
|
def _is_layer(self):
|
|
return True
|
|
|
|
def _init_call_fn_args(self):
|
|
# Clear cached call function arguments.
|
|
self.__class__._call_full_argspec.fget.cache.pop(self, None)
|
|
self.__class__._call_fn_args.fget.cache.pop(self, None)
|
|
self.__class__._call_accepts_kwargs.fget.cache.pop(self, None)
|
|
|
|
call_fn_args = self._call_fn_args
|
|
self._expects_training_arg = ('training' in call_fn_args or
|
|
self._call_accepts_kwargs)
|
|
# The default training arg will be any (non-None) default specified in the
|
|
# method signature, or None if no value is specified.
|
|
self._default_training_arg = self._call_fn_arg_defaults.get(
|
|
'training')
|
|
self._expects_mask_arg = ('mask' in call_fn_args or
|
|
self._call_accepts_kwargs)
|
|
|
|
@property
|
|
@layer_utils.cached_per_instance
|
|
def _call_full_argspec(self):
|
|
# Argspec inspection is expensive and the call spec is used often, so it
|
|
# makes sense to cache the result.
|
|
return tf_inspect.getfullargspec(self.call)
|
|
|
|
@property
|
|
@layer_utils.cached_per_instance
|
|
def _call_fn_args(self):
|
|
all_args = self._call_full_argspec.args
|
|
# Scrub `self` that appears if a decorator was applied.
|
|
if all_args and all_args[0] == 'self':
|
|
return all_args[1:]
|
|
return all_args
|
|
|
|
@property
|
|
@layer_utils.cached_per_instance
|
|
def _call_fn_arg_defaults(self):
|
|
call_fn_args = self._call_fn_args
|
|
call_fn_defaults = self._call_full_argspec.defaults or []
|
|
defaults = dict()
|
|
|
|
# The call arg defaults are an n-tuple of the last n elements of the args
|
|
# list. (n = # of elements that have a default argument)
|
|
for i in range(-1 * len(call_fn_defaults), 0):
|
|
defaults[call_fn_args[i]] = call_fn_defaults[i]
|
|
return defaults
|
|
|
|
@property
|
|
@layer_utils.cached_per_instance
|
|
def _call_fn_arg_positions(self):
|
|
call_fn_arg_positions = dict()
|
|
for pos, arg in enumerate(self._call_fn_args):
|
|
call_fn_arg_positions[arg] = pos
|
|
return call_fn_arg_positions
|
|
|
|
@property
|
|
@layer_utils.cached_per_instance
|
|
def _call_accepts_kwargs(self):
|
|
return self._call_full_argspec.varkw is not None
|
|
|
|
@property
|
|
def _eager_losses(self):
|
|
# A list of loss values containing activity regularizers and losses
|
|
# manually added through `add_loss` during eager execution. It is cleared
|
|
# after every batch.
|
|
# Because we plan on eventually allowing a same model instance to be trained
|
|
# in eager mode or graph mode alternatively, we need to keep track of
|
|
# eager losses and symbolic losses via separate attributes.
|
|
if not hasattr(self._thread_local, '_eager_losses'):
|
|
self._thread_local._eager_losses = []
|
|
return self._thread_local._eager_losses
|
|
|
|
@_eager_losses.setter
|
|
def _eager_losses(self, losses):
|
|
self._thread_local._eager_losses = losses
|
|
|
|
def _dedup_weights(self, weights):
|
|
"""Dedupe weights while maintaining order as much as possible."""
|
|
output, seen_ids = [], set()
|
|
for w in weights:
|
|
if id(w) not in seen_ids:
|
|
output.append(w)
|
|
# Track the Variable's identity to avoid __eq__ issues.
|
|
seen_ids.add(id(w))
|
|
|
|
return output
|
|
|
|
def _split_out_first_arg(self, args, kwargs):
|
|
# Grab the argument corresponding to the first argument in the
|
|
# layer's `call` method spec. This will either be the first positional
|
|
# argument, or it will be provided as a keyword argument.
|
|
if args:
|
|
inputs = args[0]
|
|
args = args[1:]
|
|
elif self._call_fn_args[0] in kwargs:
|
|
kwargs = copy.copy(kwargs)
|
|
inputs = kwargs.pop(self._call_fn_args[0])
|
|
else:
|
|
raise ValueError(
|
|
'The first argument to `Layer.call` must always be passed.')
|
|
return inputs, args, kwargs
|
|
|
|
# SavedModel properties. Please see keras/saving/saved_model for details.
|
|
|
|
@trackable.no_automatic_dependency_tracking
|
|
def _set_save_spec(self, inputs):
|
|
if self._saved_model_inputs_spec is not None:
|
|
return # Already set.
|
|
|
|
self._saved_model_inputs_spec = nest.map_structure(tf_utils.get_tensor_spec,
|
|
inputs)
|
|
|
|
def _get_save_spec(self, dynamic_batch=True):
|
|
if self._saved_model_inputs_spec is None:
|
|
return None
|
|
|
|
return nest.map_structure(
|
|
lambda t: tf_utils.get_tensor_spec(t, dynamic_batch=dynamic_batch),
|
|
self._saved_model_inputs_spec)
|
|
|
|
@property
|
|
def _trackable_saved_model_saver(self):
|
|
return layer_serialization.LayerSavedModelSaver(self)
|
|
|
|
@property
|
|
def _object_identifier(self):
|
|
return self._trackable_saved_model_saver.object_identifier
|
|
|
|
@property
|
|
def _tracking_metadata(self):
|
|
return self._trackable_saved_model_saver.tracking_metadata
|
|
|
|
def _list_extra_dependencies_for_serialization(self, serialization_cache):
|
|
return (self._trackable_saved_model_saver
|
|
.list_extra_dependencies_for_serialization(serialization_cache))
|
|
|
|
def _list_functions_for_serialization(self, serialization_cache):
|
|
return (self._trackable_saved_model_saver
|
|
.list_functions_for_serialization(serialization_cache))
|
|
|
|
def __getstate__(self):
|
|
# Override to support `copy.deepcopy` and pickling.
|
|
# Thread-local objects cannot be copied in Python 3, so pop these.
|
|
# Thread-local objects are used to cache losses in MirroredStrategy, and
|
|
# so shouldn't be copied.
|
|
state = self.__dict__.copy()
|
|
state.pop('_thread_local', None)
|
|
state.pop('_metrics_lock', None)
|
|
return state
|
|
|
|
def __setstate__(self, state):
|
|
state['_thread_local'] = threading.local()
|
|
state['_metrics_lock'] = threading.Lock()
|
|
# Bypass Trackable logic as `__dict__` already contains this info.
|
|
object.__setattr__(self, '__dict__', state)
|
|
|
|
|
|
class TensorFlowOpLayer(Layer):
|
|
"""Wraps a TensorFlow Operation in a Layer.
|
|
|
|
This class is used internally by the Functional API. When a user
|
|
uses a raw TensorFlow Operation on symbolic tensors originating
|
|
from an `Input` Layer, the resultant operation will be wrapped
|
|
with this Layer object in order to make the operation compatible
|
|
with the Keras API.
|
|
|
|
This Layer will create a new, identical operation (except for inputs
|
|
and outputs) every time it is called. If `run_eagerly` is `True`,
|
|
the op creation and calculation will happen inside an Eager function.
|
|
|
|
Instances of this Layer are created when `autolambda` is called, which
|
|
is whenever a Layer's `__call__` encounters symbolic inputs that do
|
|
not have Keras metadata, or when a Network's `__init__` encounters
|
|
outputs that do not have Keras metadata.
|
|
|
|
Attributes:
|
|
node_def: String, the serialized NodeDef of the Op this layer will wrap.
|
|
name: String, the name of the Layer.
|
|
constants: Dict of NumPy arrays, the values of any Tensors needed for this
|
|
Operation that do not originate from a Keras `Input` Layer. Since all
|
|
placeholders must come from Keras `Input` Layers, these Tensors must be
|
|
treated as constant in the Functional API.
|
|
trainable: Bool, whether this Layer is trainable. Currently Variables are
|
|
not supported, and so this parameter has no effect.
|
|
dtype: The default dtype of this Layer. Inherited from `Layer` and has no
|
|
effect on this class, however is used in `get_config`.
|
|
"""
|
|
|
|
@trackable.no_automatic_dependency_tracking
|
|
def __init__(self,
|
|
node_def,
|
|
name,
|
|
constants=None,
|
|
trainable=True,
|
|
dtype=None):
|
|
# Pass autocast=False, as if inputs are cast, input types might not match
|
|
# Operation type.
|
|
super(TensorFlowOpLayer, self).__init__(
|
|
name=_TF_OP_LAYER_NAME_PREFIX + name, trainable=trainable, dtype=dtype,
|
|
autocast=False)
|
|
if isinstance(node_def, dict):
|
|
self.node_def = json_format.ParseDict(node_def, node_def_pb2.NodeDef())
|
|
else:
|
|
if not isinstance(node_def, bytes):
|
|
node_def = node_def.encode('utf-8')
|
|
self.node_def = node_def_pb2.NodeDef.FromString(node_def)
|
|
# JSON serialization stringifies keys which are integer input indices.
|
|
self.constants = ({
|
|
int(index): constant for index, constant in constants.items()
|
|
} if constants is not None else {})
|
|
# Layer uses original op unless it is called on new inputs.
|
|
# This means `built` is not set in `__call__`.
|
|
self.built = True
|
|
|
|
# Do not individually trace TensorflowOpLayers in the SavedModel.
|
|
self._must_restore_from_config = True
|
|
|
|
def call(self, inputs):
|
|
if context.executing_eagerly():
|
|
return self._defun_call(inputs)
|
|
return self._make_op(inputs)
|
|
|
|
def _make_node_def(self, graph):
|
|
node_def = node_def_pb2.NodeDef()
|
|
node_def.CopyFrom(self.node_def)
|
|
# Used in TPUReplicateContext to indicate whether this node has been cloned
|
|
# and to not add TPU attributes.
|
|
node_def.attr['_cloned'].b = True
|
|
node_def.name = graph.unique_name(node_def.name)
|
|
return node_def
|
|
|
|
def _make_op(self, inputs):
|
|
inputs = nest.flatten(inputs)
|
|
graph = inputs[0].graph
|
|
node_def = self._make_node_def(graph)
|
|
with graph.as_default():
|
|
for index, constant in self.constants.items():
|
|
# Recreate constant in graph to add distribution context.
|
|
value = tensor_util.constant_value(constant)
|
|
if value is not None:
|
|
constant = constant_op.constant(value, name=node_def.input[index])
|
|
inputs.insert(index, constant)
|
|
c_op = ops._create_c_op(graph, node_def, inputs, control_inputs=[])
|
|
op = graph._create_op_from_tf_operation(c_op)
|
|
op._control_flow_post_processing()
|
|
|
|
# Record the gradient because custom-made ops don't go through the
|
|
# code-gen'd eager call path
|
|
op_type = compat.as_str(op.op_def.name)
|
|
attr_names = [compat.as_str(attr.name) for attr in op.op_def.attr]
|
|
attrs = []
|
|
for attr_name in attr_names:
|
|
attrs.append(attr_name)
|
|
attrs.append(op.get_attr(attr_name))
|
|
attrs = tuple(attrs)
|
|
execute.record_gradient(op_type, op.inputs, attrs, op.outputs)
|
|
|
|
if len(op.outputs) == 1:
|
|
return op.outputs[0]
|
|
return op.outputs
|
|
|
|
@def_function.function
|
|
def _defun_call(self, inputs):
|
|
"""Wraps the op creation method in an Eager function for `run_eagerly`."""
|
|
return self._make_op(inputs)
|
|
|
|
def get_config(self):
|
|
config = super(TensorFlowOpLayer, self).get_config()
|
|
config.update({
|
|
# `__init__` prefixes the name. Revert to the constructor argument.
|
|
'name': config['name'][len(_TF_OP_LAYER_NAME_PREFIX):],
|
|
'node_def': json_format.MessageToDict(self.node_def),
|
|
'constants': {
|
|
i: backend.get_value(c) for i, c in self.constants.items()
|
|
}
|
|
})
|
|
return config
|
|
|
|
|
|
class AddLoss(Layer):
|
|
"""Adds its inputs as a loss.
|
|
|
|
Attributes:
|
|
unconditional: Whether or not the loss should be conditioned on the inputs.
|
|
"""
|
|
|
|
def __init__(self, unconditional, **kwargs):
|
|
# Pass autocast=False, as there is no reason to cast loss to a different
|
|
# dtype.
|
|
kwargs['autocast'] = False
|
|
super(AddLoss, self).__init__(**kwargs)
|
|
self.unconditional = unconditional
|
|
|
|
def call(self, inputs):
|
|
self.add_loss(inputs, inputs=(not self.unconditional))
|
|
return inputs
|
|
|
|
def get_config(self):
|
|
config = super(AddLoss, self).get_config()
|
|
config.update({'unconditional': self.unconditional})
|
|
return config
|
|
|
|
|
|
class AddMetric(Layer):
|
|
"""Adds its inputs as a metric.
|
|
|
|
Attributes:
|
|
aggregation: 'mean' or None. How the inputs should be aggregated.
|
|
metric_name: The name to use for this metric.
|
|
"""
|
|
|
|
def __init__(self, aggregation=None, metric_name=None, **kwargs):
|
|
super(AddMetric, self).__init__(**kwargs)
|
|
self.aggregation = aggregation
|
|
self.metric_name = metric_name
|
|
|
|
def call(self, inputs):
|
|
self.add_metric(inputs, aggregation=self.aggregation, name=self.metric_name)
|
|
return inputs
|
|
|
|
def get_config(self):
|
|
config = super(AddMetric, self).get_config()
|
|
config.update({
|
|
'aggregation': self.aggregation,
|
|
'metric_name': self.metric_name
|
|
})
|
|
return config
|
|
|
|
|
|
def _in_functional_construction_mode(layer, inputs, args, kwargs, input_list): # pylint: disable=unused-argument
|
|
"""Check the arguments to see if we are constructing a functional model."""
|
|
if keras_tensor.keras_tensors_enabled():
|
|
# We are constructing a functional model if any of the inputs
|
|
# are KerasTensors
|
|
return any(
|
|
isinstance(tensor, keras_tensor.KerasTensor)
|
|
for tensor in nest.flatten([inputs, args, kwargs]))
|
|
else:
|
|
if context.executing_eagerly():
|
|
all_inputs_symbolic = all(
|
|
tf_utils.is_symbolic_tensor(t) for t in input_list)
|
|
if (base_layer_utils.is_subclassed(layer) and
|
|
any(tf_utils.is_symbolic_tensor(t) for t in nest.flatten(
|
|
[inputs, args, kwargs])) and not all_inputs_symbolic):
|
|
raise ValueError('It appears you are trying to construct a '
|
|
'functional model, but not all of the inputs in '
|
|
'the first positional argument of your layer call '
|
|
'are symbolic tensors. '
|
|
'(Input objects, or the output of another layer) '
|
|
'Functional models cannot correctly track custom '
|
|
'layers unless all values in the first call argument '
|
|
'are symbolic.')
|
|
return all_inputs_symbolic
|
|
else:
|
|
return (base_layer_utils.is_in_keras_graph() or
|
|
all(hasattr(t, '_keras_history') for t in input_list))
|
|
|
|
|
|
def _convert_numpy_or_python_types(x):
|
|
if isinstance(x, (np_arrays.ndarray, np.ndarray, float, int)):
|
|
return ops.convert_to_tensor_v2_with_dispatch(x)
|
|
return x
|
|
|
|
|
|
# Avoid breaking users who directly import this symbol from this file.
|
|
# TODO(fchollet): remove this.
|
|
InputSpec = input_spec.InputSpec # pylint:disable=invalid-name
|