9485250f44
It also allows us to simplify internal node-walking code. This may come with a subtle behavior change/simplification in the interaction between `training`, learning phase scopes and model construction. Before, it seems that constructing a network model in a learning phase scope would cause that model to permanently use that learning phase in some future code regardless of what values of `training` were passed in. The code underlying this behavior seems to have been pretty fragile and hard to guarantee. It was also quite likely unintentional or buggy in many cases. After this change, setting a learning phase scope will continue affecting call-time behavior when `training` isn't explicitly passed in, but building a model inside of a learning phase scope won't force it to always use that learning phase. (Passing `training=...` at construction time will continue to be the recommended method of freezing behavior at call time) PiperOrigin-RevId: 308436442 Change-Id: I8cb8922a6e3cd219a1771328dda3003287978b39
2447 lines
97 KiB
Python
2447 lines
97 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 functools
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import itertools
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import threading
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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 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.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
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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.mixed_precision.experimental import autocast_variable
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from tensorflow.python.keras.mixed_precision.experimental import loss_scale_optimizer
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from tensorflow.python.keras.mixed_precision.experimental 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_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.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 layer_utils as trackable_layer_utils
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from tensorflow.python.training.tracking import tracking
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from tensorflow.python.util import deprecation
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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 import tf_inspect
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from tensorflow.tools.docs import doc_controls
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# pylint: disable=g-classes-have-attributes
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class Layer(base_layer.Layer):
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"""Base layer class.
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This is the class from which all layers inherit.
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A layer is a class implementing common neural networks operations, such
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as convolution, batch norm, etc. These operations require managing weights,
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losses, updates, and inter-layer connectivity.
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Users will just instantiate a layer and then treat it as a callable.
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We recommend that descendants of `Layer` implement the following methods:
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* `__init__()`: Save configuration in member variables
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* `build()`: Called once from `__call__`, when we know the shapes of inputs
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and `dtype`. Should have the calls to `add_weight()`, and then
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call the super's `build()` (which sets `self.built = True`, which is
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nice in case the user wants to call `build()` manually before the
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first `__call__`).
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* `call()`: Called in `__call__` after making sure `build()` has been called
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once. Should actually perform the logic of applying the layer to the
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input tensors (which should be passed in as the first argument).
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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 (default of
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`None` means use `tf.keras.backend.floatx` in TensorFlow 2, or the type
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of the first input in TensorFlow 1).
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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 computations and weights. If mixed
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precision is used with a `tf.keras.mixed_precision.experimental.Policy`,
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this is instead just the dtype of the layer's weights, as the computations
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are done in a different dtype.
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updates: List of update ops of this layer.
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losses: List of losses added by this layer.
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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).
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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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Each layer has a dtype, which is typically the dtype of the layer's
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computations and variables. A layer's dtype can be queried via the
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`Layer.dtype` property. The dtype is specified with the `dtype` constructor
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argument. In TensorFlow 2, the dtype defaults to `tf.keras.backend.floatx()`
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if no dtype is passed. `floatx()` itself defaults to "float32". Additionally,
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layers will cast their inputs to the layer's dtype in TensorFlow 2. When mixed
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precision is used, layers may have different computation and variable dtypes.
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See `tf.keras.mixed_precision.experimental.Policy` for details on layer
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dtypes.
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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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@trackable.no_automatic_dependency_tracking
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def __init__(self, trainable=True, name=None, dtype=None, dynamic=False,
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**kwargs):
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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_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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}
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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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self._build_input_shape = 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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self.supports_masking = False
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self._supports_ragged_inputs = False
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self._init_set_name(name)
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self._activity_regularizer = 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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# 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, but other
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# fields, like the loss scale, are used by Models. For subclassed networks,
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# the compute and variable dtypes are used as like any 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 = []
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self._outbound_nodes = []
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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_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.
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# Default to True, which means auto tracking is turned on. Certain subclass
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# might want to turn it off, like Sequential model.
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self._auto_track_sub_layers = True
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@trackable.no_automatic_dependency_tracking
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@generic_utils.default
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def build(self, input_shape):
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"""Creates the variables of the layer (optional, for subclass implementers).
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This is a method that implementers of subclasses of `Layer` or `Model`
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can override if they need a state-creation step in-between
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layer instantiation and layer call.
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This is typically used to create the weights of `Layer` subclasses.
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Arguments:
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input_shape: Instance of `TensorShape`, or list of instances of
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`TensorShape` if the layer expects a list of inputs
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(one instance per input).
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"""
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if not hasattr(self.build, '_is_default'):
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self._build_input_shape = input_shape
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self.built = True
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@doc_controls.for_subclass_implementers
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def call(self, inputs, **kwargs): # pylint: disable=unused-argument
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"""This is where the layer's logic lives.
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Arguments:
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inputs: Input tensor, or list/tuple of input tensors.
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**kwargs: Additional keyword arguments.
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Returns:
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A tensor or list/tuple of tensors.
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"""
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return inputs
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@doc_controls.for_subclass_implementers
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def _add_trackable(self, trackable_object, trainable):
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"""Adds a Trackable object to this layer's state.
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Arguments:
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trackable_object: The tf.tracking.Trackable object to add.
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trainable: Boolean, whether the variable should be part of the layer's
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"trainable_variables" (e.g. variables, biases) or
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"non_trainable_variables" (e.g. BatchNorm mean and variance).
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Returns:
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The TrackableWeightHandler used to track this object.
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"""
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handler = base_layer_utils.TrackableWeightHandler(trackable_object)
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if trainable:
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self._trainable_weights.append(handler)
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else:
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self._non_trainable_weights.append(handler)
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return handler
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@doc_controls.for_subclass_implementers
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def add_weight(self,
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name=None,
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shape=None,
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dtype=None,
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initializer=None,
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regularizer=None,
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trainable=None,
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constraint=None,
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partitioner=None,
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use_resource=None,
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synchronization=tf_variables.VariableSynchronization.AUTO,
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aggregation=tf_variables.VariableAggregation.NONE,
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**kwargs):
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"""Adds a new variable to the layer.
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Arguments:
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name: Variable name.
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shape: Variable shape. Defaults to scalar if unspecified.
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dtype: The type of the variable. Defaults to `self.dtype` or `float32`.
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initializer: Initializer instance (callable).
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regularizer: Regularizer instance (callable).
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trainable: Boolean, whether the variable should be part of the layer's
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"trainable_variables" (e.g. variables, biases)
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or "non_trainable_variables" (e.g. BatchNorm mean and variance).
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Note that `trainable` cannot be `True` if `synchronization`
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is set to `ON_READ`.
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constraint: Constraint instance (callable).
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partitioner: Partitioner to be passed to the `Trackable` API.
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use_resource: Whether to use `ResourceVariable`.
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synchronization: Indicates when a distributed a variable will be
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aggregated. Accepted values are constants defined in the class
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`tf.VariableSynchronization`. By default the synchronization is set to
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`AUTO` and the current `DistributionStrategy` chooses
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when to synchronize. If `synchronization` is set to `ON_READ`,
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`trainable` must not be set to `True`.
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aggregation: Indicates how a distributed variable will be aggregated.
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Accepted values are constants defined in the class
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`tf.VariableAggregation`.
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**kwargs: Additional keyword arguments. Accepted values are `getter`,
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`collections`, `experimental_autocast` and `caching_device`.
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Returns:
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The created variable. Usually either a `Variable` or `ResourceVariable`
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instance. If `partitioner` is not `None`, a `PartitionedVariable`
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instance is returned.
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Raises:
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RuntimeError: If called with partitioned variable regularization and
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eager execution is enabled.
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ValueError: When giving unsupported dtype and no initializer or when
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trainable has been set to True with synchronization set as `ON_READ`.
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"""
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if shape is None:
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shape = ()
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# Validate optional keyword arguments.
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for kwarg in kwargs:
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if kwarg not in ['getter', 'collections', 'experimental_autocast',
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'caching_device']:
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raise TypeError('Unknown keyword argument:', kwarg)
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getter = kwargs.pop('getter', base_layer_utils.make_variable)
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collections_arg = kwargs.pop('collections', None)
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# 'experimental_autocast' can be set to False by the caller to indicate an
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# AutoCastVariable should never be created.
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autocast = kwargs.pop('experimental_autocast', True)
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# See the docstring for tf.Variable about the details for caching_device.
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caching_device = kwargs.pop('caching_device', None)
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if dtype is None:
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dtype = self.dtype or backend.floatx()
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dtype = dtypes.as_dtype(dtype)
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if self._dtype_policy.variable_dtype is None:
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# The policy is "_infer", so we infer the policy from the variable dtype.
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self._dtype_policy = policy.Policy(dtype.base_dtype.name)
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initializer = initializers.get(initializer)
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regularizer = regularizers.get(regularizer)
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constraint = constraints.get(constraint)
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if synchronization == tf_variables.VariableSynchronization.ON_READ:
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if trainable:
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raise ValueError(
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'Synchronization value can be set to '
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'VariableSynchronization.ON_READ only for non-trainable variables. '
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'You have specified trainable=True and '
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'synchronization=VariableSynchronization.ON_READ.')
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else:
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# Set trainable to be false when variable is to be synced on read.
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trainable = False
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elif trainable is None:
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trainable = True
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# Initialize variable when no initializer provided
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if initializer is None:
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# If dtype is DT_FLOAT, provide a uniform unit scaling initializer
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if dtype.is_floating:
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initializer = initializers.get('glorot_uniform')
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# If dtype is DT_INT/DT_UINT, provide a default value `zero`
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# If dtype is DT_BOOL, provide a default value `FALSE`
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elif dtype.is_integer or dtype.is_unsigned or dtype.is_bool:
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initializer = initializers.zeros()
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# NOTES:Do we need to support for handling DT_STRING and DT_COMPLEX here?
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else:
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raise ValueError('An initializer for variable %s of type %s is required'
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' for layer %s' % (name, dtype.base_dtype, self.name))
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if (autocast and self._dtype_policy.should_cast_variables and
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dtype.is_floating):
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# Wrap 'getter' with a version that returns an AutoCastVariable.
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old_getter = getter
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def getter(*args, **kwargs): # pylint: disable=function-redefined
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variable = old_getter(*args, **kwargs)
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return autocast_variable.create_autocast_variable(variable)
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# Also the caching_device does not work with the mixed precision API,
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# disable it if it is specified.
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# TODO(b/142020079): Reenable it once the bug is fixed.
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if caching_device is not None:
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tf_logging.warn('`caching_device` does not work with mixed precision '
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'API. Ignoring user specified `caching_device`.')
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caching_device = None
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variable = self._add_variable_with_custom_getter(
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name=name,
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shape=shape,
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# TODO(allenl): a `make_variable` equivalent should be added as a
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# `Trackable` method.
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getter=getter,
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# Manage errors in Layer rather than Trackable.
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overwrite=True,
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initializer=initializer,
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dtype=dtype,
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constraint=constraint,
|
|
trainable=trainable,
|
|
partitioner=partitioner,
|
|
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 isinstance(variable, tf_variables.PartitionedVariable):
|
|
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('Layers with arguments in `__init__` must '
|
|
'override `get_config`.')
|
|
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 ops.get_default_graph().as_default():
|
|
graph = func_graph.FuncGraph('graph')
|
|
with graph.as_default():
|
|
input_shape = tf_utils.convert_shapes(input_shape, to_tuples=False)
|
|
inputs = nest.map_structure(
|
|
base_layer_utils.generate_placeholders_from_shape, 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
|
|
|
|
@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)
|
|
|
|
@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.')
|
|
|
|
# Grab the first positional or keyword argument.
|
|
if args:
|
|
inputs = args[0]
|
|
args = args[1:]
|
|
elif self._call_fn_args[0] in kwargs:
|
|
inputs = kwargs.pop(self._call_fn_args[0])
|
|
else:
|
|
raise ValueError(
|
|
'The first argument to `Layer.call` must always be passed.')
|
|
|
|
call_context = base_layer_utils.call_context()
|
|
input_list = nest.flatten(inputs)
|
|
|
|
# We will attempt to build a TF graph if & only if all inputs are symbolic.
|
|
# This is always the case in graph mode. It can also be the case in eager
|
|
# mode when all inputs can be traced back to `keras.Input()` (when building
|
|
# models using the functional API).
|
|
build_graph = tf_utils.are_all_symbolic_tensors(input_list)
|
|
|
|
# Accept NumPy and scalar inputs by converting to Tensors.
|
|
if any(isinstance(x, (np.ndarray, float, int)) for x in input_list):
|
|
def _convert_non_tensor(x):
|
|
# Don't call `ops.convert_to_tensor_v2` on all `inputs` because
|
|
# `SparseTensors` can't be converted to `Tensor`.
|
|
if isinstance(x, (np.ndarray, float, int)):
|
|
return ops.convert_to_tensor_v2(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 = self._collect_input_masks(inputs, args, kwargs)
|
|
if (self._expects_mask_arg and input_masks is not None and
|
|
not self._call_arg_was_passed('mask', args, kwargs)):
|
|
mask_arg_passed_by_framework = True
|
|
kwargs['mask'] = input_masks
|
|
|
|
# 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.
|
|
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 3a: `learning_phase()` has been set.
|
|
elif backend.global_learning_phase_is_set():
|
|
training_value = backend.learning_phase()
|
|
# Priority 3b: Pass the `learning_phase()` if in the Keras FuncGraph.
|
|
elif build_graph:
|
|
with backend.get_graph().as_default():
|
|
if base_layer_utils.is_in_keras_graph():
|
|
training_value = backend.learning_phase()
|
|
|
|
if self._expects_training_arg and training_value is not None:
|
|
# 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)
|
|
args, kwargs = self._set_call_arg_value(
|
|
'training', training_value, args, kwargs)
|
|
training_arg_passed_by_framework = True
|
|
|
|
# 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.
|
|
if build_graph and base_layer_utils.needs_keras_history(inputs):
|
|
base_layer_utils.create_keras_history(inputs)
|
|
|
|
with call_context.enter(self, inputs, build_graph, training_value):
|
|
# Check input assumptions set after layer building, e.g. input shape.
|
|
if build_graph:
|
|
# 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)
|
|
if (any(isinstance(x, ragged_tensor.RaggedTensor) for x in input_list)
|
|
and self._supports_ragged_inputs is False): # pylint: disable=g-bool-id-comparison
|
|
raise ValueError('Layer %s does not support RaggedTensors as input. '
|
|
'Inputs received: %s. You can try converting your '
|
|
'input to an uniform tensor.' % (self.name, inputs))
|
|
|
|
graph = backend.get_graph()
|
|
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)
|
|
|
|
# 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
|
|
|
|
if not self.dynamic:
|
|
try:
|
|
with base_layer_utils.autocast_context_manager(
|
|
self._compute_dtype):
|
|
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 + ').')
|
|
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')
|
|
outputs = self._set_connectivity_metadata((inputs,) + args, kwargs,
|
|
outputs)
|
|
self._handle_activity_regularization(inputs, outputs)
|
|
self._set_mask_metadata(inputs, outputs, input_masks)
|
|
if hasattr(self, '_set_inputs') and not self.inputs:
|
|
# Subclassed network: explicitly set metadata normally set by
|
|
# a call to self._set_inputs().
|
|
# TODO(b/120997007): This should be done in Eager as well, but
|
|
# causes garbage collection issues because of the placeholders
|
|
# created on the default Keras graph.
|
|
self._set_inputs(inputs, outputs)
|
|
else:
|
|
# Eager execution on data tensors.
|
|
with backend.name_scope(self._name_scope()):
|
|
self._maybe_build(inputs)
|
|
cast_inputs = self._maybe_cast_inputs(inputs)
|
|
with base_layer_utils.autocast_context_manager(
|
|
self._compute_dtype):
|
|
outputs = self.call(cast_inputs, *args, **kwargs)
|
|
self._handle_activity_regularization(inputs, outputs)
|
|
self._set_mask_metadata(inputs, outputs, input_masks)
|
|
|
|
return outputs
|
|
|
|
@property
|
|
def dtype(self):
|
|
return self._dtype_policy.variable_dtype
|
|
|
|
@property
|
|
def name(self):
|
|
return self._name
|
|
|
|
@property
|
|
@trackable_layer_utils.cache_recursive_attribute('dynamic')
|
|
def dynamic(self):
|
|
# NOTE(taylorrobie): Currently self._dynamic is read-only. If that changes
|
|
# then this cache logic must be updated.
|
|
return self._dynamic
|
|
|
|
@property
|
|
@doc_controls.do_not_generate_docs
|
|
@trackable_layer_utils.cache_recursive_attribute('stateful')
|
|
def stateful(self):
|
|
return self._stateful
|
|
|
|
@stateful.setter
|
|
@trackable_layer_utils.invalidate_recursive_cache('stateful')
|
|
def stateful(self, value):
|
|
self._stateful = value
|
|
|
|
@property
|
|
def trainable(self):
|
|
return self._trainable
|
|
|
|
@trainable.setter
|
|
def trainable(self, value):
|
|
self._trainable = value
|
|
for layer in getattr(self, '_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):
|
|
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, base_layer.InputSpec):
|
|
raise TypeError('Layer input_spec must be an instance of InputSpec. '
|
|
'Got: {}'.format(v))
|
|
self._input_spec = value
|
|
|
|
@property
|
|
def trainable_weights(self):
|
|
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):
|
|
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.
|
|
|
|
Returns:
|
|
A list of variables.
|
|
"""
|
|
return self.trainable_weights + self.non_trainable_weights
|
|
|
|
@property
|
|
def updates(self):
|
|
collected_updates = []
|
|
all_layers = self._gather_unique_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):
|
|
try:
|
|
u = u()
|
|
except errors.InaccessibleTensorError:
|
|
base_layer_utils.check_graph_consistency(
|
|
method='add_update', force_raise=True)
|
|
raise # check_graph_consistency may not always raise.
|
|
base_layer_utils.check_graph_consistency(u, method='add_update')
|
|
collected_updates.append(u)
|
|
return collected_updates
|
|
|
|
@property
|
|
def losses(self):
|
|
"""Losses which are associated with this `Layer`.
|
|
|
|
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.
|
|
|
|
Returns:
|
|
A list of tensors.
|
|
"""
|
|
collected_losses = []
|
|
all_layers = self._gather_unique_layers()
|
|
for layer in all_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.
|
|
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
|
|
|
|
@doc_controls.for_subclass_implementers
|
|
def add_loss(self, losses, inputs=None):
|
|
"""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(inputs, self):
|
|
self.add_loss(tf.abs(tf.reduce_mean(inputs)), inputs=True)
|
|
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)
|
|
# Actvity 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,))
|
|
x = tf.keras.layers.Dense(10)(inputs)
|
|
outputs = tf.keras.layers.Dense(1)(x)
|
|
model = tf.keras.Model(inputs, outputs)
|
|
# Weight regularization.
|
|
model.add_loss(lambda: tf.reduce_mean(x.kernel))
|
|
```
|
|
|
|
The `get_losses_for` method allows to retrieve the losses relevant to a
|
|
specific set of inputs.
|
|
|
|
Arguments:
|
|
losses: Loss tensor, or list/tuple of tensors. Rather than tensors, losses
|
|
may also be zero-argument callables which create a loss tensor.
|
|
inputs: Ignored when executing eagerly. If anything other than None is
|
|
passed, it signals the losses are conditional on some of the layer's
|
|
inputs, and thus they should only be run where these inputs are
|
|
available. This is the case for activity regularization losses, for
|
|
instance. If `None` is passed, the losses are assumed
|
|
to be unconditional, and will apply across all dataflows of the layer
|
|
(e.g. weight regularization losses).
|
|
"""
|
|
def _tag_unconditional(loss):
|
|
"""Process the loss and tag it by setting loss._unconditional_loss."""
|
|
if callable(loss):
|
|
# We run the loss without autocasting, as regularizers are often
|
|
# numerically unstable in float16.
|
|
with base_layer_utils.autocast_context_manager(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(loss, dtype=backend.floatx())
|
|
loss._unconditional_loss = (inputs is None) # pylint: disable=protected-access
|
|
return loss
|
|
|
|
losses = nest.flatten(losses)
|
|
|
|
callable_losses = []
|
|
symbolic_losses = []
|
|
for loss in losses:
|
|
if callable(loss):
|
|
callable_losses.append(functools.partial(_tag_unconditional, loss))
|
|
continue
|
|
if loss is None:
|
|
continue
|
|
if not tensor_util.is_tensor(loss):
|
|
loss = ops.convert_to_tensor_v2(loss, dtype=backend.floatx())
|
|
# TF Functions should take the eager path.
|
|
if (tf_utils.is_symbolic_tensor(loss) and
|
|
not base_layer_utils.is_in_tf_function()):
|
|
symbolic_losses.append(_tag_unconditional(loss))
|
|
base_layer_utils.check_graph_consistency(loss, method='add_loss')
|
|
|
|
self._callable_losses.extend(callable_losses)
|
|
|
|
in_call_context = base_layer_utils.call_context().in_call
|
|
|
|
if in_call_context:
|
|
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)
|
|
|
|
@property
|
|
def metrics(self):
|
|
collected_metrics = []
|
|
all_layers = self._gather_unique_layers()
|
|
for layer in all_layers:
|
|
collected_metrics.extend(layer._metrics)
|
|
return collected_metrics
|
|
|
|
@doc_controls.for_subclass_implementers
|
|
def add_metric(self, value, aggregation=None, name=None):
|
|
"""Adds metric tensor to the layer.
|
|
|
|
Args:
|
|
value: Metric tensor.
|
|
aggregation: Sample-wise metric reduction function. If `aggregation=None`,
|
|
it indicates that the metric tensor provided has been aggregated
|
|
already. eg, `bin_acc = BinaryAccuracy(name='acc')` followed by
|
|
`model.add_metric(bin_acc(y_true, y_pred))`. If aggregation='mean', the
|
|
given metric tensor will be sample-wise reduced using `mean` function.
|
|
eg, `model.add_metric(tf.reduce_sum(outputs), name='output_mean',
|
|
aggregation='mean')`.
|
|
name: String metric name.
|
|
|
|
Raises:
|
|
ValueError: If `aggregation` is anything other than None or `mean`.
|
|
"""
|
|
if aggregation is not None and aggregation != 'mean':
|
|
raise ValueError(
|
|
'We currently support only `mean` sample-wise metric aggregation. '
|
|
'You provided aggregation=`%s`' % aggregation)
|
|
|
|
from_metric_obj = hasattr(value, '_metric_obj')
|
|
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), aggregation='mean')`
|
|
# 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\', aggregation=\'mean\')`')
|
|
elif from_metric_obj:
|
|
name = value._metric_obj.name
|
|
|
|
if in_call_context:
|
|
# TF Function path should take the eager path.
|
|
self._symbolic_add_metric(value, aggregation, name)
|
|
else:
|
|
if not is_symbolic:
|
|
raise ValueError('Expected a symbolic Tensor for the metric value, '
|
|
'received: ' + str(value))
|
|
|
|
# Possible a metric was added in a Layer's `build`.
|
|
if not getattr(self, '_is_graph_network', False):
|
|
with backend.get_graph().as_default():
|
|
self._symbolic_add_metric(value, aggregation, name)
|
|
return
|
|
|
|
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.
|
|
self._graph_network_add_metric(value, aggregation, name)
|
|
|
|
@deprecation.deprecated_args(None, '`inputs` is now automatically inferred',
|
|
'inputs')
|
|
@doc_controls.for_subclass_implementers
|
|
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.
|
|
|
|
The `get_updates_for` method allows to retrieve the updates relevant to a
|
|
specific set of inputs.
|
|
|
|
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.
|
|
"""
|
|
call_context = base_layer_utils.call_context()
|
|
|
|
if (ds_context.has_strategy() and
|
|
ds_context.in_cross_replica_context() and
|
|
# When saving the model, the distribution strategy context should be
|
|
# ignored, following the default path for adding updates.
|
|
not call_context.saving):
|
|
# Updates don't need to be run in a cross-replica context.
|
|
return
|
|
|
|
updates = generic_utils.to_list(updates)
|
|
|
|
if call_context.in_call:
|
|
relevant_inputs = call_context.inputs
|
|
else:
|
|
inbound_nodes = getattr(self, '_inbound_nodes', [])
|
|
relevant_inputs = [node.input_tensors for node in inbound_nodes]
|
|
|
|
def process_update(x):
|
|
"""Standardize update ops.
|
|
|
|
Arguments:
|
|
x: Tensor, op, or callable.
|
|
|
|
Returns:
|
|
An update op.
|
|
"""
|
|
if callable(x):
|
|
update = lambda: process_update(x())
|
|
return update()
|
|
elif isinstance(x, ops.Operation):
|
|
update = x
|
|
elif hasattr(x, 'op'):
|
|
update = x.op
|
|
else:
|
|
update = ops.convert_to_tensor_v2(x)
|
|
|
|
reachable = tf_utils.get_reachable_from_inputs(relevant_inputs, [update])
|
|
update._unconditional_update = update not in reachable
|
|
return update
|
|
|
|
updates = [process_update(x) for x in updates]
|
|
self._updates.extend(updates)
|
|
|
|
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)
|
|
|
|
def get_updates_for(self, inputs):
|
|
"""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`.
|
|
"""
|
|
if inputs is None:
|
|
# Requesting unconditional updates.
|
|
return [u for u in self.updates if u._unconditional_update]
|
|
|
|
# Requesting input-conditional updates.
|
|
updates = [u for u in self.updates if not u._unconditional_update]
|
|
inputs = nest.flatten(inputs)
|
|
reachable = tf_utils.get_reachable_from_inputs(inputs, updates)
|
|
return [u for u in updates if u in reachable]
|
|
|
|
def get_losses_for(self, inputs):
|
|
"""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`.
|
|
"""
|
|
if inputs is None:
|
|
# Requesting unconditional losses.
|
|
return [l for l in self.losses if l._unconditional_loss]
|
|
|
|
# Requesting input-conditional losses.
|
|
losses = [l for l in self.losses if not l._unconditional_loss]
|
|
inputs = nest.flatten(inputs)
|
|
reachable = tf_utils.get_reachable_from_inputs(inputs, losses)
|
|
return [l for l in losses if l in reachable]
|
|
|
|
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)
|
|
|
|
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
|
|
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
|
|
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)
|
|
|
|
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')
|
|
|
|
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')
|
|
|
|
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')
|
|
|
|
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
|
|
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
|
|
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. #
|
|
##############################################################################
|
|
|
|
@deprecation.deprecated(
|
|
date=None, instructions='Please use `layer.__call__` method instead.')
|
|
@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).
|
|
"""
|
|
return self.__call__(inputs, *args, **kwargs)
|
|
|
|
@deprecation.deprecated(
|
|
date=None, instructions='Please use `layer.add_weight` method instead.')
|
|
@doc_controls.do_not_doc_inheritable
|
|
def add_variable(self, *args, **kwargs):
|
|
"""Deprecated, do NOT use! Alias for `add_weight`."""
|
|
return self.add_weight(*args, **kwargs)
|
|
|
|
@property
|
|
def variables(self):
|
|
"""Returns the list of all layer variables/weights.
|
|
|
|
Alias of `self.weights`.
|
|
|
|
Returns:
|
|
A list of variables.
|
|
"""
|
|
return self.weights
|
|
|
|
@property
|
|
def trainable_variables(self):
|
|
return self.trainable_weights
|
|
|
|
@property
|
|
def non_trainable_variables(self):
|
|
return self.non_trainable_weights
|
|
|
|
##############################################################################
|
|
# Methods & attributes below are all private and only used by the framework. #
|
|
##############################################################################
|
|
|
|
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))
|
|
|
|
# This has no impact on the layer behavior, and is only used for printing
|
|
# warnings.
|
|
self._dtype_defaulted_to_floatx = (not dtype and
|
|
policy.policy_defaults_to_floatx())
|
|
|
|
# TODO(reedwm): Expose this property?
|
|
@property
|
|
def _compute_dtype(self):
|
|
"""The layer's compute dtype.
|
|
|
|
Unless mixed-precision is used, this is the same as `Layer.dtype`.
|
|
|
|
If self._autocast is True, layer's will cast floating-point inputs to this.
|
|
|
|
Returns:
|
|
The layer's compute dtype.
|
|
"""
|
|
return self._dtype_policy.compute_dtype
|
|
|
|
def _maybe_cast_inputs(self, inputs):
|
|
"""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.
|
|
|
|
Returns:
|
|
`inputs`, but tensors may have been casted to self._compute_dtype
|
|
"""
|
|
compute_dtype = self._compute_dtype
|
|
if (self._autocast and compute_dtype and
|
|
dtypes.as_dtype(compute_dtype).is_floating):
|
|
def f(x):
|
|
"""Cast a single Tensor or TensorSpec to the compute dtype."""
|
|
cast_types = (ops.Tensor, sparse_tensor.SparseTensor,
|
|
ragged_tensor.RaggedTensor)
|
|
if (isinstance(x, cast_types) and x.dtype.is_floating and
|
|
x.dtype.base_dtype.name != compute_dtype):
|
|
if self._dtype_defaulted_to_floatx:
|
|
self._warn_about_input_casting(x.dtype.base_dtype)
|
|
return math_ops.cast(x, compute_dtype)
|
|
elif isinstance(x, tensor_spec.TensorSpec) and x.dtype.is_floating:
|
|
# Inputs may be TensorSpecs when this function is called from
|
|
# model._set_inputs.
|
|
return tensor_spec.TensorSpec(x.shape, compute_dtype, x.name)
|
|
else:
|
|
return x
|
|
return nest.map_structure(f, inputs)
|
|
else:
|
|
return inputs
|
|
|
|
def _warn_about_input_casting(self, input_dtype):
|
|
# self._already_warned_about_input_casting is only retrieved or set in this
|
|
# function.
|
|
already_warned = getattr(self, '_already_warned_about_input_casting', False)
|
|
if not already_warned:
|
|
tf_logging.warn(
|
|
"Layer {self.name} is casting an input tensor from dtype "
|
|
"{input_dtype} to the layer's dtype of {layer_dtype}, which is new "
|
|
"behavior in TensorFlow 2. The layer has dtype {layer_dtype} "
|
|
'because its dtype defaults to floatx.\n\n'
|
|
""
|
|
"If you intended to run this layer in {layer_dtype}, you can safely "
|
|
"ignore this warning. If in doubt, this warning is likely only an "
|
|
"issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n\n"
|
|
""
|
|
"To change all layers to have dtype {input_dtype} by default, call "
|
|
"`tf.keras.backend.set_floatx('{input_dtype}')`. To change just this "
|
|
"layer, pass dtype='{input_dtype}' to the layer constructor. If you "
|
|
"are the author of this layer, you can disable autocasting by "
|
|
"passing autocast=False to the base Layer constructor.\n".format(
|
|
self=self,
|
|
input_dtype=input_dtype.name,
|
|
layer_dtype=self._compute_dtype))
|
|
self._already_warned_about_input_casting = True
|
|
|
|
# _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._dtype_policy = policy.Policy(value)
|
|
|
|
def _name_scope(self):
|
|
return self.name
|
|
|
|
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:
|
|
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 _symbolic_add_metric(self, value, aggregation=None, name=None):
|
|
base_layer_utils.check_graph_consistency(value, method='add_metric')
|
|
match = self._get_existing_metric(name)
|
|
if aggregation is None:
|
|
# Iterate over the metrics and check if the given metric exists already.
|
|
# This can happen when a metric instance is created in subclassed model
|
|
# layer `__init__` and we have tracked that instance already in
|
|
# model.__setattr__.
|
|
if match:
|
|
result_tensor = value
|
|
metric_obj = match
|
|
elif hasattr(value, '_metric_obj'):
|
|
# We track the instance using the metadata on the result tensor.
|
|
result_tensor = value
|
|
metric_obj = result_tensor._metric_obj
|
|
self._metrics.append(metric_obj)
|
|
else:
|
|
raise ValueError(
|
|
'We do not support adding an aggregated metric result tensor that '
|
|
'is not the output of a `tf.keras.metrics.Metric` metric instance. '
|
|
'Without having access to the metric instance we cannot reset the '
|
|
'state of a metric after every epoch during training. You can '
|
|
'create a `tf.keras.metrics.Metric` instance and pass the result '
|
|
'here or pass an un-aggregated result with `aggregation` parameter '
|
|
'set as `mean`. For example: `self.add_metric(tf.reduce_sum(inputs)'
|
|
', name=\'mean_activation\', aggregation=\'mean\')`')
|
|
else:
|
|
# If a non-aggregated tensor is given as input (ie. `aggregation` is
|
|
# explicitly set to `mean`), we wrap the tensor in `Mean` metric.
|
|
if match:
|
|
result_tensor = match(value)
|
|
metric_obj = match
|
|
else:
|
|
metric_obj, result_tensor = base_layer_utils.create_mean_metric(
|
|
value, name)
|
|
self._metrics.append(metric_obj)
|
|
|
|
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 isinstance(variable, tf_variables.PartitionedVariable):
|
|
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
|
|
base_layer_utils.check_graph_consistency(
|
|
mean_activity_loss, method='activity_regularizer')
|
|
self.add_loss(mean_activity_loss, inputs=inputs)
|
|
|
|
def _set_mask_metadata(self, inputs, outputs, previous_mask):
|
|
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))
|
|
|
|
# Only compute the mask if the Layer explicitly supports masking or has
|
|
# overridden `compute_mask`.
|
|
should_compute_mask = (
|
|
hasattr(self, 'compute_mask') and
|
|
(self.supports_masking or
|
|
not getattr(self.compute_mask, '_is_default', False)))
|
|
|
|
if mask_already_computed:
|
|
flat_masks = [getattr(x, '_keras_mask', None) for x in flat_outputs]
|
|
elif not should_compute_mask:
|
|
flat_masks = [None for _ in flat_outputs]
|
|
else:
|
|
output_masks = self.compute_mask(inputs, previous_mask)
|
|
# `compute_mask` can return a single `None` even when a Layer
|
|
# has multiple outputs.
|
|
if output_masks is None:
|
|
flat_masks = [None for _ in flat_outputs]
|
|
else:
|
|
flat_masks = nest.flatten(output_masks)
|
|
|
|
for output, mask in zip(flat_outputs, flat_masks):
|
|
try:
|
|
output._keras_mask = mask
|
|
except AttributeError:
|
|
# C Type such as np.ndarray.
|
|
pass
|
|
|
|
if tf_utils.are_all_symbolic_tensors(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 _collect_input_masks(self, inputs, args, kwargs):
|
|
"""Checks if `mask` argument was passed, else gathers mask from inputs."""
|
|
if self._call_arg_was_passed('mask', args, kwargs):
|
|
return self._get_call_arg_value('mask', args, kwargs)
|
|
|
|
if not self._should_compute_mask:
|
|
return None
|
|
|
|
input_masks = nest.map_structure(lambda t: getattr(t, '_keras_mask', None),
|
|
inputs)
|
|
if generic_utils.is_all_none(input_masks):
|
|
return None
|
|
return input_masks
|
|
|
|
def _call_arg_was_passed(self, arg_name, args, kwargs, inputs_in_args=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:]
|
|
if arg_name in dict(zip(call_fn_args, args)):
|
|
return True
|
|
return False
|
|
|
|
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 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 _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._dtype_policy = policy.Policy(dtype)
|
|
input_shapes = None
|
|
if all(hasattr(x, 'shape') for x in input_list):
|
|
input_shapes = nest.map_structure(lambda x: x.shape, inputs)
|
|
# 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)
|
|
# We must set self.built since user defined build functions are not
|
|
# constrained to set self.built.
|
|
self.built = True
|
|
|
|
# Optionally load weight values specified at layer instantiation.
|
|
if self._initial_weights is not None:
|
|
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)
|
|
|
|
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.
|
|
"""
|
|
layers = trackable_layer_utils.filter_empty_layer_containers(self._layers)
|
|
# Keep track of each top-level layers' `trainable` as well as the
|
|
# state of all of its sublayers.
|
|
trainable_state = {self: self.trainable}
|
|
for layer in layers:
|
|
trainable_state.update(layer._get_trainable_state())
|
|
return trainable_state
|
|
|
|
def _set_trainable_state(self, trainable_state):
|
|
"""Set `trainable` state for each sublayer."""
|
|
layers = trackable_layer_utils.filter_empty_layer_containers(self._layers)
|
|
if self in trainable_state:
|
|
self.trainable = trainable_state[self]
|
|
for layer in layers:
|
|
layer._set_trainable_state(trainable_state)
|
|
|
|
@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 trackable_layer_utils.has_weights(existing_value)):
|
|
super(tracking.AutoTrackable, self).__setattr__(
|
|
'_layers',
|
|
[l for l in self._layers if l is not existing_value])
|
|
self._attribute_sentinel.invalidate_all()
|
|
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])
|
|
|
|
# Any time we change `_layers` (either by deleting the attribute or by
|
|
# reassigning it which will call __delattr__ from __setattr__) the topology
|
|
# of the subgraph of Layers may change. In that case we will need to
|
|
# recompute any attribute which depends on that subgraph.
|
|
if name == '_layers':
|
|
self._attribute_sentinel.invalidate_all()
|
|
|
|
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
|
|
|
|
# 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 trackable_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, '_attribute_sentinel'):
|
|
value._attribute_sentinel.add_parent(self._attribute_sentinel)
|
|
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):
|
|
# 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 = trackable_layer_utils.filter_empty_layer_containers(
|
|
self._layers)
|
|
return list(
|
|
itertools.chain.from_iterable(
|
|
getattr(layer, attribute) for layer in nested_layers))
|
|
return []
|
|
|
|
def _gather_unique_layers(self):
|
|
"""Returns the current layer and all its children depth first deduped.
|
|
|
|
We are deduping after getting the layers to maintain the order.
|
|
"""
|
|
all_layers = self._gather_layers()
|
|
unique_layers, seen_layers = [], object_identity.ObjectIdentitySet()
|
|
for layer in all_layers:
|
|
if layer not in seen_layers:
|
|
unique_layers.append(layer)
|
|
# Track the Variable's identity to avoid __eq__ issues.
|
|
seen_layers.add(layer)
|
|
return unique_layers
|
|
|
|
def _gather_layers(self):
|
|
"""Returns the current layer and all its children depth first."""
|
|
all_layers = [self]
|
|
if hasattr(self, '_layers'):
|
|
child_layers = trackable_layer_utils.filter_empty_layer_containers(
|
|
self._layers)
|
|
for child_layer in child_layers:
|
|
all_layers.extend(child_layer._gather_layers())
|
|
return all_layers
|
|
|
|
@property
|
|
@tracking.cached_per_instance
|
|
def _attribute_sentinel(self):
|
|
return trackable_layer_utils.AttributeSentinel()
|
|
|
|
# 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)
|
|
self._expects_mask_arg = ('mask' in call_fn_args or
|
|
self._call_accepts_kwargs)
|
|
|
|
@property
|
|
@tracking.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
|
|
@tracking.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
|
|
@tracking.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
|
|
@tracking.cached_per_instance
|
|
def _call_accepts_kwargs(self):
|
|
return self._call_full_argspec.varkw is not None
|
|
|
|
@property
|
|
@tracking.cached_per_instance
|
|
def _should_compute_mask(self):
|
|
return ('mask' in self._call_fn_args or
|
|
getattr(self, 'compute_mask', None) is not None)
|
|
|
|
def _dedup_weights(self, weights):
|
|
"""Dedupe weights while maintaining order as much as possible."""
|
|
output, seen_weights = [], object_identity.ObjectIdentitySet()
|
|
for w in weights:
|
|
if w not in seen_weights:
|
|
output.append(w)
|
|
# Track the Variable's identity to avoid __eq__ issues.
|
|
seen_weights.add(w)
|
|
return output
|
|
|
|
# SavedModel properties. Please see keras/saving/saved_model for details.
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@property
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def _trackable_saved_model_saver(self):
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return layer_serialization.LayerSavedModelSaver(self)
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@property
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def _object_identifier(self):
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return self._trackable_saved_model_saver.object_identifier
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@property
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def _tracking_metadata(self):
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return self._trackable_saved_model_saver.tracking_metadata
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def _list_extra_dependencies_for_serialization(self, serialization_cache):
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return (self._trackable_saved_model_saver
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.list_extra_dependencies_for_serialization(serialization_cache))
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def _list_functions_for_serialization(self, serialization_cache):
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return (self._trackable_saved_model_saver
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.list_functions_for_serialization(serialization_cache))
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def __getstate__(self):
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# Override to support `copy.deepcopy` and pickling.
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# Thread-local objects cannot be copied in Python 3, so pop these.
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# Thread-local objects are used to cache losses in MirroredStrategy, and
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# so shouldn't be copied.
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state = self.__dict__.copy()
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state.pop('_thread_local', None)
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return state
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def __setstate__(self, state):
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state['_thread_local'] = threading.local()
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# Bypass Trackable logic as `__dict__` already contains this info.
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object.__setattr__(self, '__dict__', state)
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class KerasHistory(
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collections.namedtuple('KerasHistory',
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['layer', 'node_index', 'tensor_index'])):
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"""Tracks the Layer call that created a Tensor, for Keras Graph Networks.
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During construction of Keras Graph Networks, this metadata is added to
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each Tensor produced as the output of a Layer, starting with an
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`InputLayer`. This allows Keras to track how each Tensor was produced, and
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|
this information is later retraced by the `keras.engine.Network` class to
|
|
reconstruct the Keras Graph Network.
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|
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|
Attributes:
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layer: The Layer that produced the Tensor.
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node_index: The specific call to the Layer that produced this Tensor. Layers
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|
can be called multiple times in order to share weights. A new node is
|
|
created every time a Tensor is called.
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tensor_index: The output index for this Tensor. Always zero if the Layer
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|
that produced this Tensor only has one output. Nested structures of
|
|
Tensors are deterministically assigned an index via `nest.flatten`.
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"""
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# Added to maintain memory and performance characteristics of `namedtuple`
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# while subclassing.
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__slots__ = ()
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|
|
|
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# Avoid breaking users who directly import this symbol from this file.
|
|
# TODO(fchollet): remove this.
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|
InputSpec = input_spec.InputSpec # pylint:disable=invalid-name
|