mirror of
https://github.com/zebrajr/tensorflow.git
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48cb1ae640
Adds a dependency on wrapt to TensorFlow's pip package. It's not a very large dependency, and this is a usability win when subclassing TensorFlow types (Model, Module, Layer, etc.).
wrapt fixes issues with CPython's direct access to dictionaries by inheriting the memory layout of the wrapped object, so we can now pass isinstance(obj, dict) checks while staying correct (i.e. {}.update(obj) won't look at the possibly-stale wrapper object's memory). There are several new oddities with the wrapt approach, but overall this is much better.
We're already doing dict wrapping, just not in a way that passes isinstance(obj, dict) checks. We need it to support restore-on-create for variables added to objects while executing eagerly.
I tried switching _ListWrapper to wrapt too. It works in most Python versions (with some tweaks to TF to accommodate the change), but apparently in Python 3.4 isinstance(obj, (list, tuple)) is not functioning properly. There are no correctness issues with actually subclassing list, and it means type(obj) shows up as a subclass of list, so this isn't too bad. Since ObjectProxy copies the memory layout of the wrapped object we can't do both of these at the same time.
PiperOrigin-RevId: 243118363
1934 lines
75 KiB
Python
1934 lines
75 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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"""A `Network` is way to compose layers: the topological form of a `Model`.
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import collections
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import copy
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import itertools
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import json
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import os
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from six.moves import zip # pylint: disable=redefined-builtin
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from tensorflow.python import pywrap_tensorflow
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from tensorflow.python.eager import context
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from tensorflow.python.framework import errors
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from tensorflow.python.framework import errors_impl
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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 tensor_shape
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from tensorflow.python.keras import backend
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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 training_utils
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from tensorflow.python.keras.mixed_precision.experimental import policy
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from tensorflow.python.keras.saving import hdf5_format
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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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from tensorflow.python.keras.utils.io_utils import ask_to_proceed_with_overwrite
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from tensorflow.python.platform import tf_logging as logging
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from tensorflow.python.training import checkpoint_management
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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 util as trackable_utils
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from tensorflow.python.util import nest
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from tensorflow.python.util import serialization
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from tensorflow.python.util import tf_inspect
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# pylint: disable=g-import-not-at-top
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try:
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import h5py
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except ImportError:
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h5py = None
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try:
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import yaml
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except ImportError:
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yaml = None
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# pylint: enable=g-import-not-at-top
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class Network(base_layer.Layer):
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"""A `Network` is a composition of layers.
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`Network` is the topological form of a "model". A `Model`
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is simply a `Network` with added training routines.
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Two types of `Networks` exist: Graph Networks and Subclass Networks. Graph
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networks are used in the Keras Functional and Sequential APIs. Subclassed
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networks are used when a user subclasses the `Model` class. In general,
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more Keras features are supported with Graph Networks than with Subclassed
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Networks, specifically:
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- Model cloning (`keras.models.clone`)
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- Serialization (`model.get_config()/from_config`, `model.to_json()/to_yaml()`
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- Whole-model saving (`model.save()`)
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A Graph Network can be instantiated by passing two arguments to `__init__`.
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The first argument is the `keras.Input` Tensors that represent the inputs
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to the Network. The second argument specifies the output Tensors that
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represent the outputs of this Network. Both arguments can be a nested
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structure of Tensors.
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Example:
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```
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inputs = {'x1': keras.Input(shape=(10,)), 'x2': keras.Input(shape=(1,))}
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t = keras.layers.Dense(1, activation='relu')(inputs['x1'])
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outputs = keras.layers.Add()([t, inputs['x2'])
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network = Network(inputs, outputs)
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```
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A Graph Network constructed using the Functional API can also include raw
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TensorFlow functions, with the exception of functions that create Variables
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or assign ops.
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Example:
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```
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inputs = keras.Input(shape=(10,))
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x = keras.layers.Dense(1)(inputs)
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outputs = tf.nn.relu(x)
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network = Network(inputs, outputs)
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```
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Subclassed Networks can be instantiated via `name` and (optional) `dynamic`
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keyword arguments. Subclassed Networks keep track of their Layers, and their
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`call` method can be overridden. Subclassed Networks are typically created
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indirectly, by subclassing the `Model` class.
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Example:
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```
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class MyModel(keras.Model):
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def __init__(self):
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super(MyModel, self).__init__(name='my_model', dynamic=False)
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self.layer1 = keras.layers.Dense(10, activation='relu')
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def call(self, inputs):
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return self.layer1(inputs)
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```
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"""
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# See tf.Module for the usage of this property.
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# The key of _layer_call_argspecs is a layer. tf.Module._flatten will fail to
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# flatten the key since it is trying to convert Trackable/Layer to a string.
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_TF_MODULE_IGNORED_PROPERTIES = frozenset(itertools.chain(
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('_layer_call_argspecs',),
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base_layer.Layer._TF_MODULE_IGNORED_PROPERTIES
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))
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def __init__(self, *args, **kwargs): # pylint: disable=super-init-not-called
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# Signature detection
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if (len(args) == 2 or
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len(args) == 1 and 'outputs' in kwargs or
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'inputs' in kwargs and 'outputs' in kwargs):
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# Graph network
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self._init_graph_network(*args, **kwargs)
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else:
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# Subclassed network
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self._init_subclassed_network(**kwargs)
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# Several Network methods have "no_automatic_dependency_tracking"
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# annotations. Since Network does automatic dependency tracking on attribute
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# assignment, including for common data structures such as lists, by default
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# we'd have quite a few empty dependencies which users don't care about (or
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# would need some way to ignore dependencies automatically, which is confusing
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# when applied to user code). Some attributes, such as _layers, would cause
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# structural issues (_layers being the place where Layers assigned to tracked
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# attributes are stored).
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#
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# Aside from these aesthetic and structural issues, useless dependencies on
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# empty lists shouldn't cause issues; adding or removing them will not break
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# checkpoints, but may cause "all Python objects matched" assertions to fail
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# (in which case less strict assertions may be substituted if necessary).
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@trackable.no_automatic_dependency_tracking
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def _base_init(self, name=None):
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# The following are implemented as property functions:
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# self.trainable_weights
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# self.non_trainable_weights
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# self.input_spec
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# self.losses
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# self.updates
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self._init_set_name(name, zero_based=True)
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self._activity_regularizer = None
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# This acts just like the `trainable` attribute of any layer instance.
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# It does not affect users of the underlying layers, only users of the
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# Network instance.
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self.trainable = True
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self._is_compiled = False
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self._expects_training_arg = False
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# This is True for Sequential networks and Functional networks.
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self._compute_output_and_mask_jointly = False
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self.supports_masking = False
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if not hasattr(self, 'optimizer'):
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# Don't reset optimizer if already set.
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self.optimizer = None
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# Private attributes to implement compatibility with Layer.
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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 = [] # Used in symbolic mode only.
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self._losses = []
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self._eager_losses = []
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self._callable_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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# A dictionary that maps metric names to metric result tensors.
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self._metrics_tensors = {}
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self._scope = None # Never used.
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self._reuse = None # Never used.
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if context.executing_eagerly():
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self._graph = None
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else:
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self._graph = ops.get_default_graph() # Used in symbolic mode only.
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# A Network does not create weights of its own, thus has no dtype.
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self._dtype = None
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# All layers in order of horizontal graph traversal.
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# Entries are unique. Includes input and output layers.
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self._maybe_create_attribute('_layers', [])
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# Used in symbolic mode only, only in conjunction with graph-networks
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self._outbound_nodes = []
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self._inbound_nodes = []
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self._trackable_saver = (
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trackable_utils.saver_with_op_caching(self))
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# Networks do not need to do any casting of inputs or variables, because
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# each of its layers will handle casting through the layer's own
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# implementation. Therefore networks use the 'infer' policy, which does no
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# casting.
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self._mixed_precision_policy = policy.Policy('infer')
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@trackable.no_automatic_dependency_tracking
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def _init_graph_network(self, inputs, outputs, name=None):
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self._call_convention = (base_layer_utils
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.CallConvention.EXPLICIT_INPUTS_ARGUMENT)
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# Normalize and set self.inputs, self.outputs.
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if isinstance(inputs, list) and len(nest.flatten(inputs)) == 1:
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inputs = inputs[0]
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if isinstance(outputs, list) and len(nest.flatten(outputs)) == 1:
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outputs = outputs[0]
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self._nested_outputs = outputs
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self._nested_inputs = inputs
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self.inputs = nest.flatten(inputs)
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self.outputs = nest.flatten(outputs)
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if any(not hasattr(tensor, '_keras_history') for tensor in self.outputs):
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base_layer_utils.create_keras_history(self._nested_outputs)
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self._base_init(name=name)
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self._validate_graph_inputs_and_outputs()
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self._compute_previous_mask = (
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'mask' in tf_inspect.getfullargspec(self.call).args or
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hasattr(self, 'compute_mask'))
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# A Network does not create weights of its own, thus it is already
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# built.
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self.built = True
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self._compute_output_and_mask_jointly = True
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self._is_graph_network = True
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self._dynamic = False
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# `_expects_training_arg` is True since the `training` argument is always
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# present in the signature of the `call` method of a graph network.
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self._expects_training_arg = True
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self._input_layers = []
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self._output_layers = []
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self._input_coordinates = []
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self._output_coordinates = []
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# This is for performance optimization when calling the Network on new
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# inputs. Every time the Network is called on a set on input tensors,
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# we compute the output tensors, output masks and output shapes in one pass,
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# then cache them here. When any of these outputs is queried later, we
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# retrieve it from there instead of recomputing it.
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self._output_mask_cache = {}
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self._output_tensor_cache = {}
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self._output_shape_cache = {}
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# Build self._output_layers:
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for x in self.outputs:
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layer, node_index, tensor_index = x._keras_history # pylint: disable=protected-access
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self._output_layers.append(layer)
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self._output_coordinates.append((layer, node_index, tensor_index))
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# Build self._input_layers:
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for x in self.inputs:
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layer, node_index, tensor_index = x._keras_history # pylint: disable=protected-access
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# It's supposed to be an input layer, so only one node
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# and one tensor output.
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assert node_index == 0
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assert tensor_index == 0
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self._input_layers.append(layer)
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self._input_coordinates.append((layer, node_index, tensor_index))
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# Keep track of the network's nodes and layers.
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nodes, nodes_by_depth, layers, layers_by_depth = _map_graph_network(
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self.inputs, self.outputs)
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self._network_nodes = nodes
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self._nodes_by_depth = nodes_by_depth
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self._layers = layers
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self._layers_by_depth = layers_by_depth
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self._layer_call_argspecs = {}
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for layer in self._layers:
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self._layer_call_argspecs[layer] = tf_inspect.getfullargspec(layer.call)
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self._track_layers(layers)
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# Create the node linking internal inputs to internal outputs.
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base_layer.Node(
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outbound_layer=self,
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inbound_layers=[],
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node_indices=[],
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tensor_indices=[],
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input_tensors=self._nested_inputs,
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output_tensors=self._nested_outputs)
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# Build self.input_names and self.output_names.
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self.input_names = []
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self.output_names = []
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self._feed_input_names = []
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self._feed_inputs = []
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self._feed_input_shapes = []
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for i, layer in enumerate(self._input_layers):
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self.input_names.append(layer.name)
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if layer.is_placeholder:
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self._feed_input_names.append(layer.name)
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self._feed_input_shapes.append(backend.int_shape(self.inputs[i]))
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self._feed_inputs.append(layer.input)
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for layer in self._output_layers:
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self.output_names.append(layer.name)
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@trackable.no_automatic_dependency_tracking
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def _init_subclassed_network(self, name=None, dynamic=False):
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self._base_init(name=name)
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self._is_graph_network = False
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self._dynamic = dynamic
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call_argspec = tf_inspect.getfullargspec(self.call)
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if 'training' in call_argspec.args:
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self._expects_training_arg = True
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else:
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self._expects_training_arg = False
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self._call_convention = self._determine_call_convention(call_argspec)
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self.outputs = []
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self.inputs = []
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self.built = False
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@property
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def dynamic(self):
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if self._is_graph_network:
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return any(layer.dynamic for layer in self.layers)
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return self._dynamic or any(layer.dynamic for layer in self.layers)
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def _determine_call_convention(self, call_argspec):
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"""Decides how `self.call()` is invoked. See `CallConvention`."""
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if call_argspec.varargs:
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may_take_single_argument = False
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else:
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try:
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# Note: tf_inspect doesn't raise a TypeError when regular inspect would,
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# so we need to keep in mind that "getcallargs" may have returned
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# something even though we under-specified positional arguments.
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all_args = tf_inspect.getcallargs(self.call, None)
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self_args = set()
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for arg_name, obj in all_args.items():
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if obj is self:
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self_args.add(arg_name)
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may_take_single_argument = True
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except TypeError:
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may_take_single_argument = False
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if may_take_single_argument:
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# A single positional argument (plus "self") is considered equivalent to
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# an "inputs" argument.
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all_positional_args = len(call_argspec.args)
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if call_argspec.defaults is not None:
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all_positional_args -= len(call_argspec.defaults)
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non_self_positional_args = all_positional_args
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for positional_arg_name in call_argspec.args[:all_positional_args]:
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if positional_arg_name in self_args:
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non_self_positional_args -= 1
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if non_self_positional_args == 1:
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if 'inputs' in call_argspec.args[all_positional_args:]:
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raise TypeError(
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"Model.call() takes a single positional argument (to which "
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"inputs are passed by convention) and a separate 'inputs' "
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"argument. Unable to determine which arguments are inputs.")
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return base_layer_utils.CallConvention.SINGLE_POSITIONAL_ARGUMENT
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if 'inputs' in call_argspec.args:
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return base_layer_utils.CallConvention.EXPLICIT_INPUTS_ARGUMENT
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else:
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return base_layer_utils.CallConvention.POSITIONAL_ARGUMENTS_ARE_INPUTS
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def _track_layers(self, layers):
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"""Add Trackable dependencies on a list of Layers."""
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weight_layer_index = 0
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for layer_index, layer in enumerate(layers):
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if layer.weights:
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# Keep a separate index for layers which have weights. This allows users
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# to insert Layers without weights anywhere in the network without
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# breaking checkpoints.
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self._track_trackable(
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layer, name='layer_with_weights-%d' % weight_layer_index,
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overwrite=True)
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weight_layer_index += 1
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# Even if it doesn't have weights, we should still track everything in
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# case it has/will have Trackable dependencies.
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self._track_trackable(
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layer, name='layer-%d' % layer_index, overwrite=True)
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def __setattr__(self, name, value):
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if not getattr(self, '_self_setattr_tracking', True):
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super(Network, self).__setattr__(name, value)
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return
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if all(
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isinstance(v, (base_layer.Layer,
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data_structures.TrackableDataStructure)) or
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trackable_layer_utils.has_weights(v) for v in nest.flatten(value)):
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try:
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self._is_graph_network
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except AttributeError:
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raise RuntimeError('It looks like you are subclassing `Model` and you '
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'forgot to call `super(YourClass, self).__init__()`.'
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' Always start with this line.')
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super(Network, self).__setattr__(name, value)
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# Keep track of metric instance created in subclassed model/layer.
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# We do this so that we can maintain the correct order of metrics by adding
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# the instance to the `metrics` list as soon as it is created.
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from tensorflow.python.keras import metrics as metrics_module # pylint: disable=g-import-not-at-top
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if isinstance(value, metrics_module.Metric):
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self._metrics.append(value)
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@property
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def stateful(self):
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return any((hasattr(layer, 'stateful') and layer.stateful)
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for layer in self.layers)
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def reset_states(self):
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for layer in self.layers:
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if hasattr(layer, 'reset_states') and getattr(layer, 'stateful', False):
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layer.reset_states()
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@property
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def state_updates(self):
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"""Returns the `updates` from all layers that are stateful.
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This is useful for separating training updates and
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state updates, e.g. when we need to update a layer's internal state
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during prediction.
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Returns:
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A list of update ops.
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"""
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state_updates = []
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|
for layer in self.layers:
|
|
if getattr(layer, 'stateful', False):
|
|
if hasattr(layer, 'updates'):
|
|
state_updates += layer.updates
|
|
return state_updates
|
|
|
|
def get_weights(self):
|
|
"""Retrieves the weights of the model.
|
|
|
|
Returns:
|
|
A flat list of Numpy arrays.
|
|
"""
|
|
weights = []
|
|
for layer in self.layers:
|
|
weights += layer.weights
|
|
weights += (self._trainable_weights + self._non_trainable_weights)
|
|
return backend.batch_get_value(weights)
|
|
|
|
def set_weights(self, weights):
|
|
"""Sets the weights of the model.
|
|
|
|
Arguments:
|
|
weights: A list of Numpy arrays with shapes and types matching
|
|
the output of `model.get_weights()`.
|
|
"""
|
|
tuples = []
|
|
for layer in self.layers:
|
|
num_param = len(layer.weights)
|
|
layer_weights = weights[:num_param]
|
|
for sw, w in zip(layer.weights, layer_weights):
|
|
tuples.append((sw, w))
|
|
weights = weights[num_param:]
|
|
for sw, w in zip(self._trainable_weights + self._non_trainable_weights,
|
|
weights):
|
|
tuples.append((sw, w))
|
|
backend.batch_set_value(tuples)
|
|
|
|
def compute_mask(self, inputs, mask):
|
|
if not self._is_graph_network:
|
|
return None
|
|
|
|
# TODO(omalleyt): b/123540974 This function is not really safe to call
|
|
# by itself because it will duplicate any updates and losses in graph
|
|
# mode by `call`ing the Layers again.
|
|
output_tensors = self._run_internal_graph(inputs, mask=mask)
|
|
return nest.map_structure(lambda t: t._keras_mask, output_tensors)
|
|
|
|
@property
|
|
def layers(self):
|
|
return trackable_layer_utils.filter_empty_layer_containers(
|
|
self._layers)
|
|
|
|
def get_layer(self, name=None, index=None):
|
|
"""Retrieves a layer based on either its name (unique) or index.
|
|
|
|
If `name` and `index` are both provided, `index` will take precedence.
|
|
Indices are based on order of horizontal graph traversal (bottom-up).
|
|
|
|
Arguments:
|
|
name: String, name of layer.
|
|
index: Integer, index of layer.
|
|
|
|
Returns:
|
|
A layer instance.
|
|
|
|
Raises:
|
|
ValueError: In case of invalid layer name or index.
|
|
"""
|
|
# TODO(fchollet): We could build a dictionary based on layer names
|
|
# since they are constant, but we have not done that yet.
|
|
if index is not None:
|
|
if len(self.layers) <= index:
|
|
raise ValueError('Was asked to retrieve layer at index ' + str(index) +
|
|
' but model only has ' + str(len(self.layers)) +
|
|
' layers.')
|
|
else:
|
|
return self.layers[index]
|
|
else:
|
|
if not name:
|
|
raise ValueError('Provide either a layer name or layer index.')
|
|
for layer in self.layers:
|
|
if layer.name == name:
|
|
return layer
|
|
raise ValueError('No such layer: ' + name)
|
|
|
|
def _get_unfiltered_updates(self, check_trainable=True):
|
|
if check_trainable and not self.trainable and not self.stateful:
|
|
return []
|
|
updates = []
|
|
for layer in self.layers:
|
|
updates += layer._get_unfiltered_updates(check_trainable=check_trainable)
|
|
updates += list(self._updates)
|
|
return updates
|
|
|
|
@property
|
|
def _unfiltered_losses(self):
|
|
losses = []
|
|
|
|
# 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 in that
|
|
# case.
|
|
if self._eager_losses:
|
|
losses.extend(self._eager_losses)
|
|
else:
|
|
losses.extend(self._losses)
|
|
for regularizer in self._callable_losses:
|
|
loss_tensor = regularizer()
|
|
if loss_tensor is not None:
|
|
losses.append(loss_tensor)
|
|
for layer in self.layers:
|
|
if isinstance(layer, Network):
|
|
losses += layer._unfiltered_losses
|
|
else:
|
|
losses += layer.losses
|
|
return losses
|
|
|
|
@trackable.no_automatic_dependency_tracking
|
|
def _clear_losses(self):
|
|
"""Used every step in eager to reset losses."""
|
|
self._eager_losses = []
|
|
for layer in self.layers:
|
|
layer._clear_losses()
|
|
|
|
@property
|
|
def updates(self):
|
|
"""Retrieves the network's updates.
|
|
|
|
Will only include updates that are either
|
|
unconditional, or conditional on inputs to this model
|
|
(e.g. will not include updates that were created by layers of this model
|
|
outside of the model).
|
|
|
|
When the network has no registered inputs, all updates are returned.
|
|
|
|
Effectively, `network.updates` behaves like `layer.updates`.
|
|
|
|
Concrete example:
|
|
|
|
```python
|
|
bn = keras.layers.BatchNormalization()
|
|
x1 = keras.layers.Input(shape=(10,))
|
|
_ = bn(x1) # This creates 2 updates.
|
|
|
|
x2 = keras.layers.Input(shape=(10,))
|
|
y2 = bn(x2) # This creates 2 more updates.
|
|
|
|
# The BN layer has now 4 updates.
|
|
self.assertEqual(len(bn.updates), 4)
|
|
|
|
# Let's create a model from x2 to y2.
|
|
model = keras.models.Model(x2, y2)
|
|
|
|
# The model does not list all updates from its underlying layers,
|
|
# but only the updates that are relevant to it. Updates created by layers
|
|
# outside of the model are discarded.
|
|
self.assertEqual(len(model.updates), 2)
|
|
|
|
# If you keep calling the model, you append to its updates, just like
|
|
# what happens for a layer.
|
|
x3 = keras.layers.Input(shape=(10,))
|
|
y3 = model(x3)
|
|
self.assertEqual(len(model.updates), 4)
|
|
|
|
# But if you call the inner BN layer independently, you don't affect
|
|
# the model's updates.
|
|
x4 = keras.layers.Input(shape=(10,))
|
|
_ = bn(x4)
|
|
self.assertEqual(len(model.updates), 4)
|
|
```
|
|
|
|
Returns:
|
|
A list of update ops.
|
|
"""
|
|
|
|
updates = self._get_unfiltered_updates(check_trainable=True)
|
|
|
|
# `updates` might contain irrelevant updates, so it needs to be filtered
|
|
# with respect to inputs the model has been called on.
|
|
relevant_inputs = []
|
|
for i in range(0, len(self._inbound_nodes)):
|
|
inputs = self.get_input_at(i)
|
|
if isinstance(inputs, list):
|
|
relevant_inputs += inputs
|
|
else:
|
|
relevant_inputs.append(inputs)
|
|
if not relevant_inputs:
|
|
return list(set(updates))
|
|
|
|
reachable = tf_utils.get_reachable_from_inputs(relevant_inputs, updates)
|
|
relevant_conditional_updates = [x for x in updates if x in reachable]
|
|
unconditional_updates = [
|
|
x for x in updates if x._unconditional_update] # pylint: disable=protected-access
|
|
# A layer could be used multiple times in a nested structure,
|
|
# so the updates list must be de-duped.
|
|
return list(set(relevant_conditional_updates + unconditional_updates))
|
|
|
|
@property
|
|
def losses(self):
|
|
"""Retrieves the network's losses.
|
|
|
|
Will only include losses that are either
|
|
unconditional, or conditional on inputs to this model
|
|
(e.g. will not include losses that depend on tensors
|
|
that aren't inputs to this model).
|
|
|
|
When the network has no registered inputs, all losses are returned.
|
|
|
|
Returns:
|
|
A list of loss tensors.
|
|
"""
|
|
losses = self._unfiltered_losses
|
|
|
|
if context.executing_eagerly():
|
|
return losses
|
|
|
|
# TODO(kaftan/fchollet): Clean this up / make it obsolete.
|
|
# This is a super ugly, confusing check necessary to
|
|
# handle the case where we are executing in a function graph in eager mode
|
|
# but the model was constructed symbolically in a separate graph scope.
|
|
# We need to capture the losses created in the current graph function,
|
|
# and filter out the incorrect loss tensors created when symbolically
|
|
# building the graph.
|
|
# We have to use this check because the code after it that checks
|
|
# for reachable inputs only captures the part of the model that was
|
|
# built symbolically, and captures the wrong tensors from a different
|
|
# func graph (causing a crash later on when trying to execute the
|
|
# graph function)
|
|
if ops.executing_eagerly_outside_functions():
|
|
return [
|
|
loss for loss in losses
|
|
if getattr(loss, 'graph', None) == ops.get_default_graph()
|
|
]
|
|
|
|
relevant_inputs = []
|
|
for i in range(0, len(self._inbound_nodes)):
|
|
inputs = self.get_input_at(i)
|
|
if isinstance(inputs, list):
|
|
relevant_inputs += inputs
|
|
else:
|
|
relevant_inputs.append(inputs)
|
|
if not relevant_inputs:
|
|
return losses
|
|
|
|
reachable = tf_utils.get_reachable_from_inputs(relevant_inputs, losses)
|
|
relevant_conditional_losses = [x for x in losses if x in reachable]
|
|
unconditional_losses = [
|
|
x for x in losses if x._unconditional_loss] # pylint: disable=protected-access
|
|
return list(set(
|
|
relevant_conditional_losses + unconditional_losses + self._losses))
|
|
|
|
@property
|
|
def trainable_weights(self):
|
|
return trackable_layer_utils.gather_trainable_weights(
|
|
trainable=self.trainable,
|
|
sub_layers=self._layers,
|
|
extra_variables=self._trainable_weights)
|
|
|
|
@property
|
|
def non_trainable_weights(self):
|
|
return trackable_layer_utils.gather_non_trainable_weights(
|
|
trainable=self.trainable,
|
|
sub_layers=self._layers,
|
|
extra_variables=self._non_trainable_weights + self._trainable_weights)
|
|
|
|
@property
|
|
def _all_metrics_tensors(self):
|
|
"""Returns the network's symbolic metric tensors."""
|
|
# TODO(psv): Remove this property.
|
|
metrics_tensors = {}
|
|
for layer in self.layers:
|
|
if isinstance(layer, Network):
|
|
metrics_tensors.update(layer._all_metrics_tensors)
|
|
else:
|
|
metrics_tensors.update(layer._metrics_tensors)
|
|
metrics_tensors.update(self._metrics_tensors)
|
|
return metrics_tensors
|
|
|
|
@property
|
|
def input_spec(self):
|
|
"""Gets the network's input specs.
|
|
|
|
Returns:
|
|
A list of `InputSpec` instances (one per input to the model)
|
|
or a single instance if the model has only one input.
|
|
"""
|
|
# If subclassed model, can't assume anything.
|
|
if not self._is_graph_network:
|
|
return None
|
|
|
|
specs = []
|
|
for layer in self._input_layers:
|
|
if layer.input_spec is None:
|
|
specs.append(None)
|
|
else:
|
|
if not isinstance(layer.input_spec, list):
|
|
raise TypeError('Layer ' + layer.name +
|
|
' has an input_spec attribute that '
|
|
'is not a list. We expect a list. '
|
|
'Found input_spec = ' + str(layer.input_spec))
|
|
specs += layer.input_spec
|
|
if len(specs) == 1:
|
|
return specs[0]
|
|
return specs
|
|
|
|
@base_layer.default
|
|
def build(self, input_shape):
|
|
"""Builds the model based on input shapes received.
|
|
|
|
This is to be used for subclassed models, which do not know at instantiation
|
|
time what their inputs look like.
|
|
|
|
This method only exists for users who want to call `model.build()` in a
|
|
standalone way (as a substitute for calling the model on real data to
|
|
build it). It will never be called by the framework (and thus it will
|
|
never throw unexpected errors in an unrelated workflow).
|
|
|
|
Args:
|
|
input_shape: Single tuple, TensorShape, or list of shapes, where shapes
|
|
are tuples, integers, or TensorShapes.
|
|
|
|
Raises:
|
|
ValueError:
|
|
1. In case of invalid user-provided data (not of type tuple,
|
|
list, or TensorShape).
|
|
2. If the model requires call arguments that are agnostic
|
|
to the input shapes (positional or kwarg in call signature).
|
|
3. If not all layers were properly built.
|
|
4. If float type inputs are not supported within the layers.
|
|
|
|
In each of these cases, the user should build their model by calling it
|
|
on real tensor data.
|
|
"""
|
|
if self._is_graph_network:
|
|
self.built = True
|
|
return
|
|
|
|
# If subclass network
|
|
if input_shape is None:
|
|
raise ValueError('Input shape must be defined when calling build on a '
|
|
'model subclass network.')
|
|
valid_types = (tuple, list, tensor_shape.TensorShape)
|
|
if not isinstance(input_shape, valid_types):
|
|
raise ValueError('Specified input shape is not one of the valid types. '
|
|
'Please specify a batch input shape of type tuple or '
|
|
'list of input shapes. User provided '
|
|
'input type: {}'.format(type(input_shape)))
|
|
|
|
if input_shape and not self.inputs:
|
|
# We create placeholders for the `None`s in the shape and build the model
|
|
# in a Graph. Since tf.Variable is compatible with both eager execution
|
|
# and graph building, the variables created after building the model in
|
|
# a Graph are still valid when executing eagerly.
|
|
if context.executing_eagerly():
|
|
graph = func_graph.FuncGraph('build_graph')
|
|
else:
|
|
graph = backend.get_graph()
|
|
with graph.as_default():
|
|
if isinstance(input_shape, list):
|
|
x = [base_layer_utils.generate_placeholders_from_shape(shape)
|
|
for shape in input_shape]
|
|
else:
|
|
x = base_layer_utils.generate_placeholders_from_shape(input_shape)
|
|
|
|
kwargs = {}
|
|
call_signature = tf_inspect.getfullargspec(self.call)
|
|
call_args = call_signature.args
|
|
# Exclude `self`, `inputs`, and any argument with a default value.
|
|
if len(call_args) > 2:
|
|
if call_signature.defaults:
|
|
call_args = call_args[2:-len(call_signature.defaults)]
|
|
else:
|
|
call_args = call_args[2:]
|
|
for arg in call_args:
|
|
if arg == 'training':
|
|
# Case where `training` is a positional arg with no default.
|
|
kwargs['training'] = False
|
|
else:
|
|
# Has invalid call signature with unknown positional arguments.
|
|
raise ValueError(
|
|
'Currently, you cannot build your model if it has '
|
|
'positional or keyword arguments that are not '
|
|
'inputs to the model, but are required for its '
|
|
'`call` method. Instead, in order to instantiate '
|
|
'and build your model, `call` your model on real '
|
|
'tensor data with all expected call arguments.')
|
|
elif len(call_args) < 2:
|
|
# Signature without `inputs`.
|
|
raise ValueError('You can only call `build` on a model if its `call` '
|
|
'method accepts an `inputs` argument.')
|
|
try:
|
|
self.call(x, **kwargs)
|
|
except (errors.InvalidArgumentError, TypeError):
|
|
raise ValueError('You cannot build your model by calling `build` '
|
|
'if your layers do not support float type inputs. '
|
|
'Instead, in order to instantiate and build your '
|
|
'model, `call` your model on real tensor data (of '
|
|
'the correct dtype).')
|
|
if self._layers:
|
|
self._track_layers(self._layers)
|
|
self.built = True
|
|
|
|
def call(self, inputs, training=None, mask=None):
|
|
"""Calls the model on new inputs.
|
|
|
|
In this case `call` just reapplies
|
|
all ops in the graph to the new inputs
|
|
(e.g. build a new computational graph from the provided inputs).
|
|
|
|
Arguments:
|
|
inputs: A tensor or list of tensors.
|
|
training: Boolean or boolean scalar tensor, indicating whether to run
|
|
the `Network` in training mode or inference mode.
|
|
mask: A mask or list of masks. A mask can be
|
|
either a tensor or None (no mask).
|
|
|
|
Returns:
|
|
A tensor if there is a single output, or
|
|
a list of tensors if there are more than one outputs.
|
|
"""
|
|
if not self._is_graph_network:
|
|
raise NotImplementedError('When subclassing the `Model` class, you should'
|
|
' implement a `call` method.')
|
|
|
|
return self._run_internal_graph(inputs, training=training, mask=mask)
|
|
|
|
def compute_output_shape(self, input_shape):
|
|
if not self._is_graph_network:
|
|
return super(Network, self).compute_output_shape(input_shape)
|
|
|
|
# Convert any shapes in tuple format to TensorShapes.
|
|
input_shape = tf_utils.convert_shapes(input_shape, to_tuples=False)
|
|
|
|
if len(nest.flatten(input_shape)) != len(nest.flatten(self._input_layers)):
|
|
raise ValueError('Invalid input_shape argument ' + str(input_shape) +
|
|
': model has ' + str(len(self._input_layers)) +
|
|
' tensor inputs.')
|
|
|
|
cache_key = generic_utils.object_list_uid(input_shape)
|
|
if cache_key in self._output_shape_cache:
|
|
# Cache hit. Return shapes as TensorShapes.
|
|
return self._output_shape_cache[cache_key]
|
|
|
|
layers_to_output_shapes = {}
|
|
for layer, shape in zip(self._input_layers, nest.flatten(input_shape)):
|
|
# It's an input layer: then `compute_output_shape` is identity,
|
|
# and there is only one node and one tensor..
|
|
shape_key = layer.name + '_0_0'
|
|
layers_to_output_shapes[shape_key] = shape
|
|
|
|
depth_keys = list(self._nodes_by_depth.keys())
|
|
depth_keys.sort(reverse=True)
|
|
# Iterate over nodes, by depth level.
|
|
if len(depth_keys) > 1:
|
|
for depth in depth_keys:
|
|
nodes = self._nodes_by_depth[depth]
|
|
for node in nodes:
|
|
# This is always a single layer, never a list.
|
|
layer = node.outbound_layer
|
|
if layer in self._input_layers:
|
|
# We've already covered the input layers
|
|
# a few lines above.
|
|
continue
|
|
# Potentially redundant list,
|
|
# same size as node.input_tensors.
|
|
layer_input_shapes = []
|
|
for inbound_layer, node_id, tensor_id, _ in node.iterate_inbound():
|
|
input_layer_key = inbound_layer.name + '_%s_%s' % (node_id,
|
|
tensor_id)
|
|
layer_input_shapes.append(layers_to_output_shapes[input_layer_key])
|
|
layer_input_shapes = nest.pack_sequence_as(node.inbound_layers,
|
|
layer_input_shapes)
|
|
# Layers expect shapes to be tuples for `compute_output_shape`.
|
|
layer_input_shapes = tf_utils.convert_shapes(
|
|
layer_input_shapes, to_tuples=True)
|
|
layer_output_shapes = layer.compute_output_shape(layer_input_shapes)
|
|
# Convert back to TensorShapes.
|
|
layer_output_shapes = tf_utils.convert_shapes(
|
|
layer_output_shapes, to_tuples=False)
|
|
|
|
node_index = layer._inbound_nodes.index(node) # pylint: disable=protected-access
|
|
for j, shape in enumerate(nest.flatten(layer_output_shapes)):
|
|
shape_key = layer.name + '_%s_%s' % (node_index, j)
|
|
layers_to_output_shapes[shape_key] = shape
|
|
|
|
# Read final output shapes from layers_to_output_shapes.
|
|
output_shapes = []
|
|
for i in range(len(self._output_layers)):
|
|
layer, node_index, tensor_index = self._output_coordinates[i]
|
|
shape_key = layer.name + '_%s_%s' % (node_index, tensor_index)
|
|
output_shapes.append(layers_to_output_shapes[shape_key])
|
|
output_shapes = nest.pack_sequence_as(self._nested_outputs, output_shapes)
|
|
# Store in cache.
|
|
self._output_shape_cache[cache_key] = output_shapes
|
|
|
|
# Return shapes as TensorShapes.
|
|
return output_shapes
|
|
|
|
def _run_internal_graph(self, inputs, training=None, mask=None):
|
|
"""Computes output tensors for new inputs.
|
|
|
|
# Note:
|
|
- Expects `inputs` to be a list (potentially with 1 element).
|
|
- Can be run on non-Keras tensors.
|
|
|
|
Arguments:
|
|
inputs: Tensor or nested structure of Tensors.
|
|
training: Boolean learning phase.
|
|
mask: (Optional) Tensor or nested structure of Tensors.
|
|
|
|
Returns:
|
|
Two lists: output_tensors, output_masks
|
|
"""
|
|
# Note: masking support is relevant mainly for Keras.
|
|
# It cannot be factored out without having the fully reimplement the network
|
|
# calling logic on the Keras side. We choose to incorporate it in
|
|
# Network because 1) it may be useful to fully support in tf.layers in
|
|
# the future and 2) Keras is a major user of Network. If you don't
|
|
# use masking, it does not interfere with regular behavior at all and you
|
|
# can ignore it.
|
|
inputs = nest.flatten(inputs)
|
|
if mask is None:
|
|
masks = [None for _ in range(len(inputs))]
|
|
else:
|
|
masks = nest.flatten(mask)
|
|
|
|
for input_t, mask in zip(inputs, masks):
|
|
input_t._keras_mask = mask
|
|
|
|
# Dictionary mapping reference tensors to computed tensors.
|
|
tensor_dict = {}
|
|
|
|
for x, y, mask in zip(self.inputs, inputs, masks):
|
|
tensor_dict[str(id(x))] = y
|
|
|
|
depth_keys = list(self._nodes_by_depth.keys())
|
|
depth_keys.sort(reverse=True)
|
|
# Ignore the InputLayers when computing the graph.
|
|
depth_keys = depth_keys[1:]
|
|
|
|
for depth in depth_keys:
|
|
nodes = self._nodes_by_depth[depth]
|
|
for node in nodes:
|
|
# This is always a single layer, never a list.
|
|
layer = node.outbound_layer
|
|
|
|
if all(
|
|
str(id(tensor)) in tensor_dict
|
|
for tensor in nest.flatten(node.input_tensors)):
|
|
|
|
# Call layer (reapplying ops to new inputs).
|
|
computed_tensors = nest.map_structure(
|
|
lambda t: tensor_dict[str(id(t))], node.input_tensors)
|
|
|
|
# Ensure `training` and `mask` arg propagation if applicable.
|
|
kwargs = node.arguments or {}
|
|
argspec = self._layer_call_argspecs[layer].args
|
|
if 'training' in argspec:
|
|
kwargs.setdefault('training', training)
|
|
if 'mask' in argspec:
|
|
computed_masks = nest.map_structure(lambda t: t._keras_mask,
|
|
computed_tensors)
|
|
kwargs.setdefault('mask', computed_masks)
|
|
|
|
# Compute outputs.
|
|
output_tensors = layer(computed_tensors, **kwargs)
|
|
|
|
# Update tensor_dict.
|
|
for x, y in zip(
|
|
nest.flatten(node.output_tensors), nest.flatten(output_tensors)):
|
|
tensor_dict[str(id(x))] = y
|
|
|
|
output_tensors = []
|
|
output_shapes = []
|
|
for x in self.outputs:
|
|
assert str(id(x)) in tensor_dict, 'Could not compute output ' + str(x)
|
|
tensor = tensor_dict[str(id(x))]
|
|
output_shapes.append(x.shape)
|
|
output_tensors.append(tensor)
|
|
|
|
if output_shapes is not None:
|
|
input_shapes = [x.shape for x in inputs]
|
|
cache_key = generic_utils.object_list_uid(input_shapes)
|
|
self._output_shape_cache[cache_key] = nest.pack_sequence_as(
|
|
self._nested_outputs, output_shapes)
|
|
|
|
output_tensors = nest.pack_sequence_as(self._nested_outputs, output_tensors)
|
|
return output_tensors
|
|
|
|
def get_config(self):
|
|
if not self._is_graph_network:
|
|
raise NotImplementedError
|
|
|
|
config = {
|
|
'name': self.name,
|
|
}
|
|
node_conversion_map = {}
|
|
for layer in self.layers:
|
|
if issubclass(layer.__class__, Network):
|
|
# Networks start with a pre-existing node
|
|
# linking their input to output.
|
|
kept_nodes = 1
|
|
else:
|
|
kept_nodes = 0
|
|
for original_node_index, node in enumerate(layer._inbound_nodes):
|
|
node_key = _make_node_key(layer.name, original_node_index)
|
|
if node_key in self._network_nodes:
|
|
node_conversion_map[node_key] = kept_nodes
|
|
kept_nodes += 1
|
|
layer_configs = []
|
|
for layer in self.layers: # From the earliest layers on.
|
|
layer_class_name = layer.__class__.__name__
|
|
layer_config = layer.get_config()
|
|
filtered_inbound_nodes = []
|
|
for original_node_index, node in enumerate(layer._inbound_nodes):
|
|
node_key = _make_node_key(layer.name, original_node_index)
|
|
if node_key in self._network_nodes:
|
|
# The node is relevant to the model:
|
|
# add to filtered_inbound_nodes.
|
|
if node.arguments:
|
|
try:
|
|
json.dumps(node.arguments)
|
|
kwargs = node.arguments
|
|
except TypeError:
|
|
logging.warning(
|
|
'Layer ' + layer.name +
|
|
' was passed non-serializable keyword arguments: ' +
|
|
str(node.arguments) + '. They will not be included '
|
|
'in the serialized model (and thus will be missing '
|
|
'at deserialization time).')
|
|
kwargs = {}
|
|
else:
|
|
kwargs = {}
|
|
if node.inbound_layers:
|
|
node_data = []
|
|
for inbound_layer, node_id, tensor_id, _ in node.iterate_inbound():
|
|
node_key = _make_node_key(inbound_layer.name, node_id)
|
|
new_node_index = node_conversion_map.get(node_key, 0)
|
|
node_data.append(
|
|
tf_utils.ListWrapper(
|
|
[inbound_layer.name, new_node_index, tensor_id, kwargs]))
|
|
node_data = nest.pack_sequence_as(node.input_tensors, node_data)
|
|
if not nest.is_sequence(node_data):
|
|
node_data = [node_data]
|
|
# Convert ListWrapper to list for backwards compatible configs.
|
|
node_data = tf_utils.convert_inner_node_data(node_data)
|
|
filtered_inbound_nodes.append(node_data)
|
|
layer_configs.append({
|
|
'name': layer.name,
|
|
'class_name': layer_class_name,
|
|
'config': layer_config,
|
|
'inbound_nodes': filtered_inbound_nodes,
|
|
})
|
|
config['layers'] = layer_configs
|
|
|
|
# Gather info about inputs and outputs.
|
|
model_inputs = []
|
|
for i in range(len(self._input_layers)):
|
|
layer, node_index, tensor_index = self._input_coordinates[i]
|
|
node_key = _make_node_key(layer.name, node_index)
|
|
if node_key not in self._network_nodes:
|
|
continue
|
|
new_node_index = node_conversion_map[node_key]
|
|
model_inputs.append(
|
|
tf_utils.ListWrapper([layer.name, new_node_index, tensor_index]))
|
|
model_inputs = nest.pack_sequence_as(self._nested_inputs, model_inputs)
|
|
# Preserve external Keras compat for Models with single input.
|
|
if not nest.is_sequence(model_inputs):
|
|
model_inputs = [model_inputs]
|
|
model_inputs = tf_utils.convert_inner_node_data(model_inputs)
|
|
config['input_layers'] = model_inputs
|
|
|
|
model_outputs = []
|
|
for i in range(len(self._output_layers)):
|
|
layer, node_index, tensor_index = self._output_coordinates[i]
|
|
node_key = _make_node_key(layer.name, node_index)
|
|
if node_key not in self._network_nodes:
|
|
continue
|
|
new_node_index = node_conversion_map[node_key]
|
|
model_outputs.append(
|
|
tf_utils.ListWrapper([layer.name, new_node_index, tensor_index]))
|
|
model_outputs = nest.pack_sequence_as(self._nested_outputs, model_outputs)
|
|
# Preserve external Keras compat for Models with single output.
|
|
if not nest.is_sequence(model_outputs):
|
|
model_outputs = [model_outputs]
|
|
model_outputs = tf_utils.convert_inner_node_data(model_outputs)
|
|
config['output_layers'] = model_outputs
|
|
return copy.deepcopy(config)
|
|
|
|
@classmethod
|
|
def from_config(cls, config, custom_objects=None):
|
|
"""Instantiates a Model from its config (output of `get_config()`).
|
|
|
|
Arguments:
|
|
config: Model config dictionary.
|
|
custom_objects: Optional dictionary mapping names
|
|
(strings) to custom classes or functions to be
|
|
considered during deserialization.
|
|
|
|
Returns:
|
|
A model instance.
|
|
|
|
Raises:
|
|
ValueError: In case of improperly formatted config dict.
|
|
"""
|
|
# Layer instances created during
|
|
# the graph reconstruction process
|
|
created_layers = {}
|
|
|
|
# Dictionary mapping layer instances to
|
|
# node data that specifies a layer call.
|
|
# It acts as a queue that maintains any unprocessed
|
|
# layer call until it becomes possible to process it
|
|
# (i.e. until the input tensors to the call all exist).
|
|
unprocessed_nodes = {}
|
|
|
|
def add_unprocessed_node(layer, node_data):
|
|
if layer not in unprocessed_nodes:
|
|
unprocessed_nodes[layer] = [node_data]
|
|
else:
|
|
unprocessed_nodes[layer].append(node_data)
|
|
|
|
def process_node(layer, node_data):
|
|
"""Deserialize a node.
|
|
|
|
Arguments:
|
|
layer: layer instance.
|
|
node_data: Nested structure of `ListWrapper`.
|
|
|
|
Raises:
|
|
ValueError: In case of improperly formatted `node_data`.
|
|
"""
|
|
input_tensors = []
|
|
for input_data in nest.flatten(node_data):
|
|
input_data = input_data.as_list()
|
|
inbound_layer_name = input_data[0]
|
|
inbound_node_index = input_data[1]
|
|
inbound_tensor_index = input_data[2]
|
|
if len(input_data) == 3:
|
|
kwargs = {}
|
|
elif len(input_data) == 4:
|
|
kwargs = input_data[3]
|
|
else:
|
|
raise ValueError('Improperly formatted model config.')
|
|
|
|
inbound_layer = created_layers[inbound_layer_name]
|
|
if len(inbound_layer._inbound_nodes) <= inbound_node_index:
|
|
add_unprocessed_node(layer, node_data)
|
|
return
|
|
inbound_node = inbound_layer._inbound_nodes[inbound_node_index]
|
|
input_tensors.append(
|
|
nest.flatten(inbound_node.output_tensors)[inbound_tensor_index])
|
|
input_tensors = nest.pack_sequence_as(node_data, input_tensors)
|
|
# Call layer on its inputs, thus creating the node
|
|
# and building the layer if needed.
|
|
if input_tensors is not None:
|
|
# Preserve compatibility with older configs.
|
|
flat_input_tensors = nest.flatten(input_tensors)
|
|
if len(flat_input_tensors) == 1:
|
|
layer(flat_input_tensors[0], **kwargs)
|
|
else:
|
|
layer(input_tensors, **kwargs)
|
|
|
|
def process_layer(layer_data):
|
|
"""Deserializes a layer, then call it on appropriate inputs.
|
|
|
|
Arguments:
|
|
layer_data: layer config dict.
|
|
|
|
Raises:
|
|
ValueError: In case of improperly formatted `layer_data` dict.
|
|
"""
|
|
layer_name = layer_data['name']
|
|
|
|
# Instantiate layer.
|
|
from tensorflow.python.keras.layers import deserialize as deserialize_layer # pylint: disable=g-import-not-at-top
|
|
|
|
layer = deserialize_layer(layer_data, custom_objects=custom_objects)
|
|
created_layers[layer_name] = layer
|
|
|
|
# Gather layer inputs and convert to `ListWrapper` objects.
|
|
inbound_nodes_data = layer_data['inbound_nodes']
|
|
inbound_nodes_data = tf_utils.convert_inner_node_data(
|
|
inbound_nodes_data, wrap=True)
|
|
for node_data in inbound_nodes_data:
|
|
# We don't process nodes (i.e. make layer calls)
|
|
# on the fly because the inbound node may not yet exist,
|
|
# in case of layer shared at different topological depths
|
|
# (e.g. a model such as A(B(A(B(x)))))
|
|
add_unprocessed_node(layer, node_data)
|
|
|
|
# First, we create all layers and enqueue nodes to be processed
|
|
for layer_data in config['layers']:
|
|
process_layer(layer_data)
|
|
# Then we process nodes in order of layer depth.
|
|
# Nodes that cannot yet be processed (if the inbound node
|
|
# does not yet exist) are re-enqueued, and the process
|
|
# is repeated until all nodes are processed.
|
|
while unprocessed_nodes:
|
|
for layer_data in config['layers']:
|
|
layer = created_layers[layer_data['name']]
|
|
if layer in unprocessed_nodes:
|
|
for node_data in unprocessed_nodes.pop(layer):
|
|
process_node(layer, node_data)
|
|
|
|
name = config.get('name')
|
|
input_tensors = []
|
|
output_tensors = []
|
|
|
|
input_layers = tf_utils.convert_inner_node_data(
|
|
config['input_layers'], wrap=True)
|
|
for layer_data in nest.flatten(input_layers):
|
|
layer_name, node_index, tensor_index = layer_data.as_list()
|
|
assert layer_name in created_layers
|
|
layer = created_layers[layer_name]
|
|
layer_output_tensors = layer._inbound_nodes[node_index].output_tensors
|
|
input_tensors.append(nest.flatten(layer_output_tensors)[tensor_index])
|
|
|
|
output_layers = tf_utils.convert_inner_node_data(
|
|
config['output_layers'], wrap=True)
|
|
for layer_data in nest.flatten(output_layers):
|
|
layer_name, node_index, tensor_index = layer_data.as_list()
|
|
assert layer_name in created_layers
|
|
layer = created_layers[layer_name]
|
|
layer_output_tensors = layer._inbound_nodes[node_index].output_tensors
|
|
output_tensors.append(nest.flatten(layer_output_tensors)[tensor_index])
|
|
|
|
input_tensors = nest.pack_sequence_as(input_layers, input_tensors)
|
|
output_tensors = nest.pack_sequence_as(output_layers, output_tensors)
|
|
model = cls(inputs=input_tensors, outputs=output_tensors, name=name)
|
|
|
|
# Layers not connected to outputs, such as those added in `add_loss`.
|
|
ancillary_layers = [
|
|
layer for layer in created_layers.values() if layer not in model.layers
|
|
]
|
|
if ancillary_layers:
|
|
model._insert_layers(ancillary_layers)
|
|
return model
|
|
|
|
def save(self, filepath, overwrite=True, include_optimizer=True):
|
|
"""Saves the model to a single HDF5 file.
|
|
|
|
The savefile includes:
|
|
- The model architecture, allowing to re-instantiate the model.
|
|
- The model weights.
|
|
- The state of the optimizer, allowing to resume training
|
|
exactly where you left off.
|
|
|
|
This allows you to save the entirety of the state of a model
|
|
in a single file.
|
|
|
|
Saved models can be reinstantiated via `keras.models.load_model`.
|
|
The model returned by `load_model`
|
|
is a compiled model ready to be used (unless the saved model
|
|
was never compiled in the first place).
|
|
|
|
Arguments:
|
|
filepath: String, path to the file to save the weights to.
|
|
overwrite: Whether to silently overwrite any existing file at the
|
|
target location, or provide the user with a manual prompt.
|
|
include_optimizer: If True, save optimizer's state together.
|
|
|
|
Example:
|
|
|
|
```python
|
|
from keras.models import load_model
|
|
|
|
model.save('my_model.h5') # creates a HDF5 file 'my_model.h5'
|
|
del model # deletes the existing model
|
|
|
|
# returns a compiled model
|
|
# identical to the previous one
|
|
model = load_model('my_model.h5')
|
|
```
|
|
"""
|
|
if not self._is_graph_network:
|
|
raise NotImplementedError(
|
|
'The `save` method requires the model to be a Functional model or a '
|
|
'Sequential model. It does not work for subclassed models, '
|
|
'because such models are defined via the body of a Python method, '
|
|
'which isn\'t safely serializable. Consider '
|
|
'using `save_weights`, in order to save the weights of the model.')
|
|
|
|
from tensorflow.python.keras.models import save_model # pylint: disable=g-import-not-at-top
|
|
save_model(self, filepath, overwrite, include_optimizer)
|
|
|
|
def save_weights(self, filepath, overwrite=True, save_format=None):
|
|
"""Saves all layer weights.
|
|
|
|
Either saves in HDF5 or in TensorFlow format based on the `save_format`
|
|
argument.
|
|
|
|
When saving in HDF5 format, the weight file has:
|
|
- `layer_names` (attribute), a list of strings
|
|
(ordered names of model layers).
|
|
- For every layer, a `group` named `layer.name`
|
|
- For every such layer group, a group attribute `weight_names`,
|
|
a list of strings
|
|
(ordered names of weights tensor of the layer).
|
|
- For every weight in the layer, a dataset
|
|
storing the weight value, named after the weight tensor.
|
|
|
|
When saving in TensorFlow format, all objects referenced by the network are
|
|
saved in the same format as `tf.train.Checkpoint`, including any `Layer`
|
|
instances or `Optimizer` instances assigned to object attributes. For
|
|
networks constructed from inputs and outputs using `tf.keras.Model(inputs,
|
|
outputs)`, `Layer` instances used by the network are tracked/saved
|
|
automatically. For user-defined classes which inherit from `tf.keras.Model`,
|
|
`Layer` instances must be assigned to object attributes, typically in the
|
|
constructor. See the documentation of `tf.train.Checkpoint` and
|
|
`tf.keras.Model` for details.
|
|
|
|
Arguments:
|
|
filepath: String, path to the file to save the weights to. When saving
|
|
in TensorFlow format, this is the prefix used for checkpoint files
|
|
(multiple files are generated). Note that the '.h5' suffix causes
|
|
weights to be saved in HDF5 format.
|
|
overwrite: Whether to silently overwrite any existing file at the
|
|
target location, or provide the user with a manual prompt.
|
|
save_format: Either 'tf' or 'h5'. A `filepath` ending in '.h5' or
|
|
'.keras' will default to HDF5 if `save_format` is `None`. Otherwise
|
|
`None` defaults to 'tf'.
|
|
|
|
Raises:
|
|
ImportError: If h5py is not available when attempting to save in HDF5
|
|
format.
|
|
ValueError: For invalid/unknown format arguments.
|
|
"""
|
|
filepath_is_h5 = _is_hdf5_filepath(filepath)
|
|
if save_format is None:
|
|
if filepath_is_h5:
|
|
save_format = 'h5'
|
|
else:
|
|
save_format = 'tf'
|
|
else:
|
|
user_format = save_format.lower().strip()
|
|
if user_format in ('tensorflow', 'tf'):
|
|
save_format = 'tf'
|
|
elif user_format in ('hdf5', 'h5', 'keras'):
|
|
save_format = 'h5'
|
|
else:
|
|
raise ValueError(
|
|
'Unknown format "%s". Was expecting one of {"tf", "h5"}.' % (
|
|
save_format,))
|
|
if save_format == 'tf' and filepath_is_h5:
|
|
raise ValueError(
|
|
('save_weights got save_format="tf"/"tensorflow", but the '
|
|
'filepath ("%s") looks like an HDF5 file. Omit the ".h5"/".keras" '
|
|
'when saving in TensorFlow format.')
|
|
% filepath)
|
|
|
|
if save_format == 'h5' and h5py is None:
|
|
raise ImportError(
|
|
'`save_weights` requires h5py when saving in hdf5.')
|
|
if save_format == 'tf':
|
|
check_filepath = filepath + '.index'
|
|
else:
|
|
check_filepath = filepath
|
|
# If file exists and should not be overwritten:
|
|
if not overwrite and os.path.isfile(check_filepath):
|
|
proceed = ask_to_proceed_with_overwrite(check_filepath)
|
|
if not proceed:
|
|
return
|
|
if save_format == 'h5':
|
|
with h5py.File(filepath, 'w') as f:
|
|
hdf5_format.save_weights_to_hdf5_group(f, self.layers)
|
|
else:
|
|
if context.executing_eagerly():
|
|
session = None
|
|
else:
|
|
session = backend.get_session()
|
|
optimizer = getattr(self, 'optimizer', None)
|
|
if (optimizer
|
|
and not isinstance(optimizer, trackable.Trackable)):
|
|
logging.warning(
|
|
('This model was compiled with a Keras optimizer (%s) but is being '
|
|
'saved in TensorFlow format with `save_weights`. The model\'s '
|
|
'weights will be saved, but unlike with TensorFlow optimizers in '
|
|
'the TensorFlow format the optimizer\'s state will not be '
|
|
'saved.\n\nConsider using a TensorFlow optimizer from `tf.train`.')
|
|
% (optimizer,))
|
|
self._trackable_saver.save(filepath, session=session)
|
|
# Record this checkpoint so it's visible from tf.train.latest_checkpoint.
|
|
checkpoint_management.update_checkpoint_state_internal(
|
|
save_dir=os.path.dirname(filepath),
|
|
model_checkpoint_path=filepath,
|
|
save_relative_paths=True,
|
|
all_model_checkpoint_paths=[filepath])
|
|
|
|
def load_weights(self, filepath, by_name=False):
|
|
"""Loads all layer weights, either from a TensorFlow or an HDF5 weight file.
|
|
|
|
If `by_name` is False weights are loaded based on the network's
|
|
topology. This means the architecture should be the same as when the weights
|
|
were saved. Note that layers that don't have weights are not taken into
|
|
account in the topological ordering, so adding or removing layers is fine as
|
|
long as they don't have weights.
|
|
|
|
If `by_name` is True, weights are loaded into layers only if they share the
|
|
same name. This is useful for fine-tuning or transfer-learning models where
|
|
some of the layers have changed.
|
|
|
|
Only topological loading (`by_name=False`) is supported when loading weights
|
|
from the TensorFlow format. Note that topological loading differs slightly
|
|
between TensorFlow and HDF5 formats for user-defined classes inheriting from
|
|
`tf.keras.Model`: HDF5 loads based on a flattened list of weights, while the
|
|
TensorFlow format loads based on the object-local names of attributes to
|
|
which layers are assigned in the `Model`'s constructor.
|
|
|
|
Arguments:
|
|
filepath: String, path to the weights file to load. For weight files in
|
|
TensorFlow format, this is the file prefix (the same as was passed
|
|
to `save_weights`).
|
|
by_name: Boolean, whether to load weights by name or by topological
|
|
order. Only topological loading is supported for weight files in
|
|
TensorFlow format.
|
|
|
|
Returns:
|
|
When loading a weight file in TensorFlow format, returns the same status
|
|
object as `tf.train.Checkpoint.restore`. When graph building, restore
|
|
ops are run automatically as soon as the network is built (on first call
|
|
for user-defined classes inheriting from `Model`, immediately if it is
|
|
already built).
|
|
|
|
When loading weights in HDF5 format, returns `None`.
|
|
|
|
Raises:
|
|
ImportError: If h5py is not available and the weight file is in HDF5
|
|
format.
|
|
"""
|
|
if _is_hdf5_filepath(filepath):
|
|
save_format = 'h5'
|
|
else:
|
|
try:
|
|
pywrap_tensorflow.NewCheckpointReader(filepath)
|
|
save_format = 'tf'
|
|
except errors_impl.DataLossError:
|
|
# The checkpoint is not readable in TensorFlow format. Try HDF5.
|
|
save_format = 'h5'
|
|
if save_format == 'tf':
|
|
status = self._trackable_saver.restore(filepath)
|
|
if by_name:
|
|
raise NotImplementedError(
|
|
'Weights may only be loaded based on topology into Models when '
|
|
'loading TensorFlow-formatted weights (got by_name=True to '
|
|
'load_weights).')
|
|
if not context.executing_eagerly():
|
|
session = backend.get_session()
|
|
# Restore existing variables (if any) immediately, and set up a
|
|
# streaming restore for any variables created in the future.
|
|
trackable_utils.streaming_restore(status=status, session=session)
|
|
status.assert_nontrivial_match()
|
|
return status
|
|
if h5py is None:
|
|
raise ImportError(
|
|
'`load_weights` requires h5py when loading weights from HDF5.')
|
|
if self._is_graph_network and not self.built:
|
|
raise NotImplementedError(
|
|
'Unable to load weights saved in HDF5 format into a subclassed '
|
|
'Model which has not created its variables yet. Call the Model '
|
|
'first, then load the weights.')
|
|
with h5py.File(filepath, 'r') as f:
|
|
if 'layer_names' not in f.attrs and 'model_weights' in f:
|
|
f = f['model_weights']
|
|
if by_name:
|
|
hdf5_format.load_weights_from_hdf5_group_by_name(f, self.layers)
|
|
else:
|
|
hdf5_format.load_weights_from_hdf5_group(f, self.layers)
|
|
|
|
def _updated_config(self):
|
|
"""Util shared between different serialization methods.
|
|
|
|
Returns:
|
|
Model config with Keras version information added.
|
|
"""
|
|
from tensorflow.python.keras import __version__ as keras_version # pylint: disable=g-import-not-at-top
|
|
|
|
config = self.get_config()
|
|
model_config = {
|
|
'class_name': self.__class__.__name__,
|
|
'config': config,
|
|
'keras_version': keras_version,
|
|
'backend': backend.backend()
|
|
}
|
|
return model_config
|
|
|
|
def to_json(self, **kwargs):
|
|
"""Returns a JSON string containing the network configuration.
|
|
|
|
To load a network from a JSON save file, use
|
|
`keras.models.model_from_json(json_string, custom_objects={})`.
|
|
|
|
Arguments:
|
|
**kwargs: Additional keyword arguments
|
|
to be passed to `json.dumps()`.
|
|
|
|
Returns:
|
|
A JSON string.
|
|
"""
|
|
model_config = self._updated_config()
|
|
return json.dumps(
|
|
model_config, default=serialization.get_json_type, **kwargs)
|
|
|
|
def to_yaml(self, **kwargs):
|
|
"""Returns a yaml string containing the network configuration.
|
|
|
|
To load a network from a yaml save file, use
|
|
`keras.models.model_from_yaml(yaml_string, custom_objects={})`.
|
|
|
|
`custom_objects` should be a dictionary mapping
|
|
the names of custom losses / layers / etc to the corresponding
|
|
functions / classes.
|
|
|
|
Arguments:
|
|
**kwargs: Additional keyword arguments
|
|
to be passed to `yaml.dump()`.
|
|
|
|
Returns:
|
|
A YAML string.
|
|
|
|
Raises:
|
|
ImportError: if yaml module is not found.
|
|
"""
|
|
if yaml is None:
|
|
raise ImportError(
|
|
'Requires yaml module installed (`pip install pyyaml`).')
|
|
return yaml.dump(self._updated_config(), **kwargs)
|
|
|
|
def summary(self, line_length=None, positions=None, print_fn=None):
|
|
"""Prints a string summary of the network.
|
|
|
|
Arguments:
|
|
line_length: Total length of printed lines
|
|
(e.g. set this to adapt the display to different
|
|
terminal window sizes).
|
|
positions: Relative or absolute positions of log elements
|
|
in each line. If not provided,
|
|
defaults to `[.33, .55, .67, 1.]`.
|
|
print_fn: Print function to use. Defaults to `print`.
|
|
It will be called on each line of the summary.
|
|
You can set it to a custom function
|
|
in order to capture the string summary.
|
|
|
|
Raises:
|
|
ValueError: if `summary()` is called before the model is built.
|
|
"""
|
|
if not self.built:
|
|
raise ValueError('This model has not yet been built. '
|
|
'Build the model first by calling `build()` or calling '
|
|
'`fit()` with some data, or specify '
|
|
'an `input_shape` argument in the first layer(s) for '
|
|
'automatic build.')
|
|
layer_utils.print_summary(self,
|
|
line_length=line_length,
|
|
positions=positions,
|
|
print_fn=print_fn)
|
|
|
|
def _validate_graph_inputs_and_outputs(self):
|
|
"""Validates the inputs and outputs of a Graph Network."""
|
|
# Check for redundancy in inputs.
|
|
if len(set(self.inputs)) != len(self.inputs):
|
|
raise ValueError('The list of inputs passed to the model '
|
|
'is redundant. '
|
|
'All inputs should only appear once.'
|
|
' Found: ' + str(self.inputs))
|
|
|
|
for x in self.inputs:
|
|
# Check that x has appropriate `_keras_history` metadata.
|
|
if not hasattr(x, '_keras_history'):
|
|
cls_name = self.__class__.__name__
|
|
raise ValueError('Input tensors to a ' + cls_name + ' ' +
|
|
'must come from `tf.keras.Input`. '
|
|
'Received: ' + str(x) +
|
|
' (missing previous layer metadata).')
|
|
# Check that x is an input tensor.
|
|
# pylint: disable=protected-access
|
|
layer, _, _ = x._keras_history
|
|
if len(layer._inbound_nodes) > 1 or (
|
|
layer._inbound_nodes and layer._inbound_nodes[0].inbound_layers):
|
|
cls_name = self.__class__.__name__
|
|
logging.warning(cls_name + ' inputs must come from '
|
|
'`tf.keras.Input` (thus holding past layer metadata), '
|
|
'they cannot be the output of '
|
|
'a previous non-Input layer. '
|
|
'Here, a tensor specified as '
|
|
'input to "' + self.name + '" was not an Input tensor, '
|
|
'it was generated by layer ' + layer.name + '.\n'
|
|
'Note that input tensors are '
|
|
'instantiated via `tensor = tf.keras.Input(shape)`.\n'
|
|
'The tensor that caused the issue was: ' + str(x.name))
|
|
|
|
# Check compatibility of batch sizes of Input Layers.
|
|
input_batch_sizes = [
|
|
training_utils.get_static_batch_size(x._keras_history[0])
|
|
for x in self.inputs
|
|
]
|
|
consistent_batch_size = None
|
|
for batch_size in input_batch_sizes:
|
|
if batch_size is not None:
|
|
if (consistent_batch_size is not None and
|
|
batch_size != consistent_batch_size):
|
|
raise ValueError('The specified batch sizes of the Input Layers'
|
|
' are incompatible. Found batch sizes: {}'.format(
|
|
input_batch_sizes))
|
|
consistent_batch_size = batch_size
|
|
|
|
for x in self.outputs:
|
|
if not hasattr(x, '_keras_history'):
|
|
cls_name = self.__class__.__name__
|
|
raise ValueError('Output tensors to a ' + cls_name + ' must be '
|
|
'the output of a TensorFlow `Layer` '
|
|
'(thus holding past layer metadata). Found: ' + str(x))
|
|
|
|
def _insert_layers(self, layers, relevant_nodes=None):
|
|
"""Inserts Layers into the Network after Network creation.
|
|
|
|
This is only valid for Keras Graph Networks. Layers added via this function
|
|
will be included in the `call` computation and `get_config` of this Network.
|
|
They will not be added to the Network's outputs.
|
|
|
|
|
|
Arguments:
|
|
layers: Arbitrary nested structure of Layers. Layers must be reachable
|
|
from one or more of the `keras.Input` Tensors that correspond to this
|
|
Network's inputs.
|
|
relevant_nodes: Nodes from the Layers that should be considered part of
|
|
this Network. If `None`, all Nodes will be considered part of this
|
|
Network.
|
|
|
|
Raises:
|
|
ValueError: If the layers depend on `Input`s not found in this Model.
|
|
"""
|
|
layers = nest.flatten(layers)
|
|
node_to_depth = {}
|
|
for depth, nodes in self._nodes_by_depth.items():
|
|
node_to_depth.update({node: depth for node in nodes})
|
|
# The nodes of these Layers that are relevant to this Network. If not
|
|
# provided, assume all Nodes are relevant
|
|
if not relevant_nodes:
|
|
relevant_nodes = nest.flatten([layer._inbound_nodes for layer in layers])
|
|
network_nodes = set(relevant_nodes + list(node_to_depth.keys()))
|
|
|
|
def _get_min_depth(node):
|
|
"""Gets the minimum depth at which node can be computed."""
|
|
min_depth = 0
|
|
for layer, node_id, _, _ in node.iterate_inbound():
|
|
inbound_node = layer._inbound_nodes[node_id]
|
|
if inbound_node in node_to_depth:
|
|
min_depth = min(min_depth, node_to_depth[inbound_node])
|
|
elif inbound_node not in network_nodes:
|
|
continue
|
|
else:
|
|
# Previous relevant nodes haven't been processed yet.
|
|
return None
|
|
# New node is one shallower than its shallowest input.
|
|
return min_depth - 1
|
|
|
|
# Insert nodes into `_nodes_by_depth` and other node attrs.
|
|
unprocessed_nodes = copy.copy(relevant_nodes)
|
|
i = 0
|
|
while unprocessed_nodes:
|
|
i += 1
|
|
# Do a sanity check. This can occur if `Input`s from outside this Model
|
|
# are being relied on.
|
|
if i > 10000:
|
|
raise ValueError('Layers could not be added due to missing '
|
|
'dependencies.')
|
|
|
|
node = unprocessed_nodes.pop(0)
|
|
depth = _get_min_depth(node)
|
|
if depth is None:
|
|
unprocessed_nodes.append(node)
|
|
else:
|
|
node_key = _make_node_key(
|
|
node.outbound_layer.name,
|
|
node.outbound_layer._inbound_nodes.index(node))
|
|
node_to_depth[node] = depth
|
|
self._network_nodes.add(node_key)
|
|
self._nodes_by_depth[depth].append(node)
|
|
|
|
# Insert layers into `_layer_by_depth` and other layer attrs.
|
|
for layer in layers:
|
|
depth = min([
|
|
node_to_depth[node]
|
|
for node in layer.inbound_nodes
|
|
if node in network_nodes
|
|
])
|
|
self._layers_by_depth[depth].append(layer)
|
|
self._layers.append(layer)
|
|
self._layer_call_argspecs[layer] = tf_inspect.getfullargspec(layer.call)
|
|
|
|
|
|
def _is_hdf5_filepath(filepath):
|
|
return (filepath.endswith('.h5') or filepath.endswith('.keras') or
|
|
filepath.endswith('.hdf5'))
|
|
|
|
|
|
def _make_node_key(layer_name, node_index):
|
|
return layer_name + '_ib-' + str(node_index)
|
|
|
|
|
|
def _map_graph_network(inputs, outputs):
|
|
"""Validates a network's topology and gather its layers and nodes.
|
|
|
|
Arguments:
|
|
inputs: List of input tensors.
|
|
outputs: List of outputs tensors.
|
|
|
|
Returns:
|
|
A tuple `(nodes, nodes_by_depth, layers, layers_by_depth)`.
|
|
- nodes: list of Node instances.
|
|
- nodes_by_depth: dict mapping ints (depth) to lists of node instances.
|
|
- layers: list of Layer instances.
|
|
- layers_by_depth: dict mapping ints (depth) to lists of layer instances.
|
|
|
|
Raises:
|
|
ValueError: In case the network is not valid (e.g. disconnected graph).
|
|
"""
|
|
# Network_nodes: set of nodes included in the graph of layers
|
|
# (not all nodes included in the layers are relevant to the current graph).
|
|
network_nodes = set() # ids of all nodes relevant to the Network
|
|
nodes_depths = {} # dict {node: depth value}
|
|
layers_depths = {} # dict {layer: depth value}
|
|
layer_indices = {} # dict {layer: index in traversal}
|
|
nodes_in_decreasing_depth = []
|
|
|
|
def build_map(tensor,
|
|
finished_nodes,
|
|
nodes_in_progress,
|
|
layer,
|
|
node_index,
|
|
tensor_index):
|
|
"""Builds a map of the graph of layers.
|
|
|
|
This recursively updates the map `layer_indices`,
|
|
the list `nodes_in_decreasing_depth` and the set `network_nodes`.
|
|
|
|
Arguments:
|
|
tensor: Some tensor in a graph.
|
|
finished_nodes: Set of nodes whose subgraphs have been traversed
|
|
completely. Useful to prevent duplicated work.
|
|
nodes_in_progress: Set of nodes that are currently active on the
|
|
recursion stack. Useful to detect cycles.
|
|
layer: Layer from which `tensor` comes from. If not provided,
|
|
will be obtained from `tensor._keras_history`.
|
|
node_index: Node index from which `tensor` comes from.
|
|
tensor_index: Tensor_index from which `tensor` comes from.
|
|
|
|
Raises:
|
|
ValueError: if a cycle is detected.
|
|
"""
|
|
node = layer._inbound_nodes[node_index] # pylint: disable=protected-access
|
|
|
|
# Prevent cycles.
|
|
if node in nodes_in_progress:
|
|
raise ValueError('The tensor ' + str(tensor) + ' at layer "' +
|
|
layer.name + '" is part of a cycle.')
|
|
|
|
# Don't repeat work for shared subgraphs
|
|
if node in finished_nodes:
|
|
return
|
|
|
|
node_key = _make_node_key(layer.name, node_index)
|
|
# Update network_nodes.
|
|
network_nodes.add(node_key)
|
|
|
|
# Store the traversal order for layer sorting.
|
|
if layer not in layer_indices:
|
|
layer_indices[layer] = len(layer_indices)
|
|
|
|
nodes_in_progress.add(node)
|
|
|
|
# Propagate to all previous tensors connected to this node.
|
|
for layer, node_index, tensor_index, tensor in node.iterate_inbound():
|
|
build_map(tensor, finished_nodes, nodes_in_progress, layer, node_index,
|
|
tensor_index)
|
|
|
|
finished_nodes.add(node)
|
|
nodes_in_progress.remove(node)
|
|
nodes_in_decreasing_depth.append(node)
|
|
|
|
finished_nodes = set()
|
|
nodes_in_progress = set()
|
|
for x in outputs:
|
|
layer, node_index, tensor_index = x._keras_history # pylint: disable=protected-access
|
|
build_map(x, finished_nodes, nodes_in_progress,
|
|
layer=layer,
|
|
node_index=node_index,
|
|
tensor_index=tensor_index)
|
|
|
|
for node in reversed(nodes_in_decreasing_depth):
|
|
# If the depth is not set, the node has no outbound nodes (depth 0).
|
|
depth = nodes_depths.setdefault(node, 0)
|
|
|
|
# Update the depth of the corresponding layer
|
|
previous_depth = layers_depths.get(node.outbound_layer, 0)
|
|
# If we've seen this layer before at a higher depth,
|
|
# we should use that depth instead of the node depth.
|
|
# This is necessary for shared layers that have inputs at different
|
|
# depth levels in the graph.
|
|
depth = max(depth, previous_depth)
|
|
layers_depths[node.outbound_layer] = depth
|
|
nodes_depths[node] = depth
|
|
|
|
# Update the depth of inbound nodes.
|
|
# The "depth" of a node is the max of the depths
|
|
# of all layers it is connected to.
|
|
for inbound_layer, node_index, _, _ in node.iterate_inbound():
|
|
inbound_node = inbound_layer._inbound_nodes[node_index] # pylint: disable=protected-access
|
|
previous_depth = nodes_depths.get(inbound_node, 0)
|
|
nodes_depths[inbound_node] = max(depth + 1, previous_depth)
|
|
|
|
# Handle inputs that are not connected to outputs.
|
|
for input_t in inputs:
|
|
input_layer = input_t._keras_history[0]
|
|
if input_layer not in layers_depths:
|
|
layers_depths[input_layer] = 0
|
|
layer_indices[input_layer] = -1
|
|
nodes_depths[input_layer._inbound_nodes[0]] = 0
|
|
|
|
# Build a dict {depth: list of nodes with this depth}
|
|
nodes_by_depth = collections.defaultdict(list)
|
|
for node, depth in nodes_depths.items():
|
|
nodes_by_depth[depth].append(node)
|
|
|
|
# Build a dict {depth: list of layers with this depth}
|
|
layers_by_depth = collections.defaultdict(list)
|
|
for layer, depth in layers_depths.items():
|
|
layers_by_depth[depth].append(layer)
|
|
|
|
# Get sorted list of layer depths.
|
|
depth_keys = list(layers_by_depth.keys())
|
|
depth_keys.sort(reverse=True)
|
|
|
|
# Set self.layers and self._layers_by_depth.
|
|
layers = []
|
|
for depth in depth_keys:
|
|
layers_for_depth = layers_by_depth[depth]
|
|
# Network.layers needs to have a deterministic order:
|
|
# here we order them by traversal order.
|
|
layers_for_depth.sort(key=lambda x: layer_indices[x])
|
|
layers.extend(layers_for_depth)
|
|
|
|
# Get sorted list of node depths.
|
|
depth_keys = list(nodes_by_depth.keys())
|
|
depth_keys.sort(reverse=True)
|
|
|
|
# Check that all tensors required are computable.
|
|
# computable_tensors: all tensors in the graph
|
|
# that can be computed from the inputs provided.
|
|
computable_tensors = []
|
|
for x in inputs:
|
|
computable_tensors.append(x)
|
|
|
|
layers_with_complete_input = [] # To provide a better error msg.
|
|
for depth in depth_keys:
|
|
for node in nodes_by_depth[depth]:
|
|
layer = node.outbound_layer
|
|
if layer:
|
|
for x in nest.flatten(node.input_tensors):
|
|
if x not in computable_tensors:
|
|
raise ValueError('Graph disconnected: '
|
|
'cannot obtain value for tensor ' + str(x) +
|
|
' at layer "' + layer.name + '". '
|
|
'The following previous layers '
|
|
'were accessed without issue: ' +
|
|
str(layers_with_complete_input))
|
|
for x in nest.flatten(node.output_tensors):
|
|
computable_tensors.append(x)
|
|
layers_with_complete_input.append(layer.name)
|
|
|
|
# Ensure name unicity, which will be crucial for serialization
|
|
# (since serialized nodes refer to layers by their name).
|
|
all_names = [layer.name for layer in layers]
|
|
for name in all_names:
|
|
if all_names.count(name) != 1:
|
|
raise ValueError('The name "' + name + '" is used ' +
|
|
str(all_names.count(name)) + ' times in the model. '
|
|
'All layer names should be unique.')
|
|
return network_nodes, nodes_by_depth, layers, layers_by_depth
|