# Copyright 2015 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Training-related part of the Keras engine.""" import copy import itertools import json import os import warnings import weakref import numpy as np import tensorflow.compat.v2 as tf from keras import backend from keras import callbacks as callbacks_module from keras import optimizers from keras.dtensor import layout_map as layout_map_lib from keras.engine import base_layer from keras.engine import base_layer_utils from keras.engine import compile_utils from keras.engine import data_adapter from keras.engine import input_layer as input_layer_module from keras.engine import training_utils from keras.mixed_precision import loss_scale_optimizer as lso from keras.optimizers import optimizer_v1 from keras.optimizers.optimizer_experimental import ( optimizer as optimizer_experimental, ) from keras.saving import hdf5_format from keras.saving import pickle_utils from keras.saving import save from keras.saving import saving_utils from keras.saving.experimental import saving_lib from keras.saving.saved_model import json_utils from keras.saving.saved_model import model_serialization from keras.utils import generic_utils from keras.utils import io_utils from keras.utils import layer_utils from keras.utils import tf_utils from keras.utils import traceback_utils from keras.utils import version_utils from keras.utils.mode_keys import ModeKeys # isort: off from tensorflow.python.eager import context from tensorflow.python.platform import tf_logging as logging from tensorflow.python.util.tf_export import keras_export from tensorflow.tools.docs import doc_controls try: import h5py except ImportError: h5py = None @keras_export("keras.Model", "keras.models.Model") class Model(base_layer.Layer, version_utils.ModelVersionSelector): """`Model` groups layers into an object with training and inference features. Args: inputs: The input(s) of the model: a `keras.Input` object or list of `keras.Input` objects. outputs: The output(s) of the model. See Functional API example below. name: String, the name of the model. There are two ways to instantiate a `Model`: 1 - With the "Functional API", where you start from `Input`, you chain layer calls to specify the model's forward pass, and finally you create your model from inputs and outputs: ```python import tensorflow as tf inputs = tf.keras.Input(shape=(3,)) x = tf.keras.layers.Dense(4, activation=tf.nn.relu)(inputs) outputs = tf.keras.layers.Dense(5, activation=tf.nn.softmax)(x) model = tf.keras.Model(inputs=inputs, outputs=outputs) ``` Note: Only dicts, lists, and tuples of input tensors are supported. Nested inputs are not supported (e.g. lists of list or dicts of dict). A new Functional API model can also be created by using the intermediate tensors. This enables you to quickly extract sub-components of the model. Example: ```python inputs = keras.Input(shape=(None, None, 3)) processed = keras.layers.RandomCrop(width=32, height=32)(inputs) conv = keras.layers.Conv2D(filters=2, kernel_size=3)(processed) pooling = keras.layers.GlobalAveragePooling2D()(conv) feature = keras.layers.Dense(10)(pooling) full_model = keras.Model(inputs, feature) backbone = keras.Model(processed, conv) activations = keras.Model(conv, feature) ``` Note that the `backbone` and `activations` models are not created with `keras.Input` objects, but with the tensors that are originated from `keras.Input` objects. Under the hood, the layers and weights will be shared across these models, so that user can train the `full_model`, and use `backbone` or `activations` to do feature extraction. The inputs and outputs of the model can be nested structures of tensors as well, and the created models are standard Functional API models that support all the existing APIs. 2 - By subclassing the `Model` class: in that case, you should define your layers in `__init__()` and you should implement the model's forward pass in `call()`. ```python import tensorflow as tf class MyModel(tf.keras.Model): def __init__(self): super().__init__() self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu) self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax) def call(self, inputs): x = self.dense1(inputs) return self.dense2(x) model = MyModel() ``` If you subclass `Model`, you can optionally have a `training` argument (boolean) in `call()`, which you can use to specify a different behavior in training and inference: ```python import tensorflow as tf class MyModel(tf.keras.Model): def __init__(self): super().__init__() self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu) self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax) self.dropout = tf.keras.layers.Dropout(0.5) def call(self, inputs, training=False): x = self.dense1(inputs) if training: x = self.dropout(x, training=training) return self.dense2(x) model = MyModel() ``` Once the model is created, you can config the model with losses and metrics with `model.compile()`, train the model with `model.fit()`, or use the model to do prediction with `model.predict()`. """ _TF_MODULE_IGNORED_PROPERTIES = frozenset( itertools.chain( ( "_train_counter", "_test_counter", "_predict_counter", "_steps_per_execution", ), base_layer.Layer._TF_MODULE_IGNORED_PROPERTIES, ) ) _SCALAR_UPRANKING_ON = False def __new__(cls, *args, **kwargs): # Signature detection if is_functional_model_init_params(args, kwargs) and cls == Model: # Functional model from keras.engine import functional return functional.Functional(skip_init=True, *args, **kwargs) else: return super(Model, cls).__new__(cls, *args, **kwargs) @tf.__internal__.tracking.no_automatic_dependency_tracking @traceback_utils.filter_traceback def __init__(self, *args, **kwargs): self._is_model_for_instrumentation = True base_layer.keras_api_gauge.get_cell("model").set(True) # Special case for Subclassed Functional Model, which we couldn't detect # when __new__ is called. We only realize it is a functional model when # it calls super.__init__ with input and output tensor. from keras.engine import functional if is_functional_model_init_params(args, kwargs) and not isinstance( self, functional.Functional ): # Filter the kwargs for multiple inheritance. supported_kwargs = [ "inputs", "outputs", "name", "trainable", "skip_init", ] model_kwargs = { k: kwargs[k] for k in kwargs if k in supported_kwargs } other_kwargs = { k: kwargs[k] for k in kwargs if k not in supported_kwargs } inject_functional_model_class(self.__class__) functional.Functional.__init__(self, *args, **model_kwargs) # In case there is any multiple inheritance here, we need to call # the __init__ for any class that appears after the Functional # class. clz_to_init = [] found_functional_class = False for clz in self.__class__.__bases__: if issubclass(clz, functional.Functional): found_functional_class = True continue if found_functional_class: clz_to_init.append(clz) if clz_to_init: for clz in clz_to_init: clz.__init__(self, *args, **other_kwargs) elif other_kwargs: # In case there are unused kwargs, we should raise an error to # user, in case they have a typo in the param name. raise TypeError( "The following keyword arguments passed to `Model` aren't " "supported: {}.".format(other_kwargs) ) return base_layer.keras_api_gauge.get_cell("Model subclass").set(True) # The following are implemented as property functions: # self.trainable_weights # self.non_trainable_weights # `inputs` / `outputs` will only appear in kwargs if either are # misspelled. generic_utils.validate_kwargs( kwargs, { "trainable", "dtype", "dynamic", "name", "autocast", "inputs", "outputs", }, ) super().__init__(**kwargs) # By default, Model is a subclass model, which is not in graph network. self._is_graph_network = False self.inputs = None self.outputs = None self.input_names = None self.output_names = None # stop_training is used by callback to stop training when error happens self.stop_training = False self.history = None # These objects are used in the default `Model.compile`. They are not # guaranteed to be set after `Model.compile` is called, as users can # override compile with custom logic. self.compiled_loss = None self.compiled_metrics = None # This is True for Sequential networks and Functional networks. self._compute_output_and_mask_jointly = False # Don't reset compilation if already done. This may occur if calling # `__init__` (or `_init_graph_network`) on an already-compiled model # such as a Sequential model. Sequential models may need to rebuild # themselves after compilation. self._maybe_create_attribute("_is_compiled", False) self._maybe_create_attribute("optimizer", None) # Model must be created under scope of DistStrat it will be trained # with. if tf.distribute.has_strategy(): self._distribution_strategy = tf.distribute.get_strategy() else: self._distribution_strategy = None self._cluster_coordinator = None # Defaults to value of `tf.config.experimental_functions_run_eagerly`. self._run_eagerly = None # Initialize cache attrs. self._reset_compile_cache() # Fault-tolerance handler. Set in `ModelCheckpoint`. self._training_state = None self._saved_model_inputs_spec = None self._saved_model_arg_spec = None self._checkpoint = tf.train.Checkpoint(root=weakref.ref(self)) self._steps_per_execution = None self._init_batch_counters() self._base_model_initialized = True # `jit_compile` starts off with None as default and gets overwritten by # the value specified in `Model.compile`, and this is effective for # `fit`, `evaluate`, and `predict`. self._jit_compile = None self._layout_map = layout_map_lib.get_current_layout_map() @tf.__internal__.tracking.no_automatic_dependency_tracking def _init_batch_counters(self): # Untracked Variables, used to keep track of mini-batches seen in `fit`, # `evaluate`, and `predict`. agg = tf.VariableAggregation.ONLY_FIRST_REPLICA self._train_counter = tf.Variable(0, dtype="int64", aggregation=agg) self._test_counter = tf.Variable(0, dtype="int64", aggregation=agg) self._predict_counter = tf.Variable(0, dtype="int64", aggregation=agg) def __setattr__(self, name, value): if not getattr(self, "_self_setattr_tracking", True): super().__setattr__(name, value) return if all( isinstance(v, (base_layer.Layer, tf.Variable)) or base_layer_utils.has_weights(v) for v in tf.nest.flatten(value) ): try: self._base_model_initialized except AttributeError: raise RuntimeError( "It looks like you are subclassing `Model` and you " "forgot to call `super().__init__()`." " Always start with this line." ) super().__setattr__(name, value) def __reduce__(self): if self.built: return ( pickle_utils.deserialize_model_from_bytecode, pickle_utils.serialize_model_as_bytecode(self), ) else: # SavedModel (and hence serialize_model_as_bytecode) only support # built models, but if the model is not built, # it may be possible to serialize as a plain Python object, # as long as the constituent parts (layers, optimizers, losses, # etc.) can be serialized as plain Python objects. Thus we call up # the superclass hierarchy to get an implementation of __reduce__ # that can pickle this Model as a plain Python object. return super().__reduce__() def __deepcopy__(self, memo): if self.built: new = pickle_utils.deserialize_model_from_bytecode( *pickle_utils.serialize_model_as_bytecode(self) ) memo[id(self)] = new else: # See comment in __reduce__ for explanation deserializer, serialized, *rest = super().__reduce__() new = deserializer(*serialized) memo[id(self)] = new if rest: state = copy.deepcopy(rest[0], memo=memo) new.__setstate__(state) return new def __copy__(self): return self.__deepcopy__({}) @generic_utils.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` instance, or list/dict of shapes, where shapes are tuples, integers, or `TensorShape` instances. Raises: ValueError: 1. In case of invalid user-provided data (not of type tuple, list, `TensorShape`, or dict). 2. If the model requires call arguments that are agnostic to the input shapes (positional or keyword arg 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: super().build(input_shape) return if input_shape is None: raise ValueError( "Input shape must be defined when calling `build()` on " "a `Model` subclass." ) valid_types = (tuple, list, tf.TensorShape, dict) 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 tf.executing_eagerly(): graph = tf.__internal__.FuncGraph("build_graph") else: graph = backend.get_graph() with graph.as_default(): if isinstance(input_shape, list) and all( d is None or isinstance(d, int) for d in input_shape ): input_shape = tuple(input_shape) if isinstance(input_shape, list): x = [ base_layer_utils.generate_placeholders_from_shape(shape) for shape in input_shape ] elif isinstance(input_shape, dict): x = { k: base_layer_utils.generate_placeholders_from_shape( shape ) for k, shape in input_shape.items() } else: x = base_layer_utils.generate_placeholders_from_shape( input_shape ) kwargs = {} call_signature = self._call_spec.full_argspec 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. The argument " "for `call()` can be a single list/tuple that " "contains multiple inputs." ) 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 (tf.errors.InvalidArgumentError, TypeError) as e: 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).\n\nThe actual error from " f"`call` is: {e}." ) super().build(input_shape) @traceback_utils.filter_traceback def __call__(self, *args, **kwargs): if self._layout_map is not None and not self.built: # Note that this method is only overridden for DTensor and layout # injection purpose. # Capture the inputs and create graph input as replacement for model # to initialize its weights first. copied_args = copy.copy(args) copied_kwargs = copy.copy(kwargs) ( inputs, copied_args, copied_kwargs, ) = self._call_spec.split_out_first_arg(copied_args, copied_kwargs) def _convert_to_graph_inputs(x): if isinstance(x, (tf.Tensor, np.ndarray, float, int)): x = tf.convert_to_tensor(x) return input_layer_module.Input(x.shape) # TODO(scottzhu): maybe better handle mask and training flag. inputs = tf.nest.map_structure(_convert_to_graph_inputs, inputs) copied_args = tf.nest.map_structure( _convert_to_graph_inputs, copied_args ) copied_kwargs = tf.nest.map_structure( _convert_to_graph_inputs, copied_kwargs ) with layout_map_lib.layout_map_scope(self._layout_map): # We ignore the result here. super().__call__(inputs, *copied_args, **copied_kwargs) layout_map_lib._map_subclass_model_variable(self, self._layout_map) return super().__call__(*args, **kwargs) @doc_controls.doc_in_current_and_subclasses def call(self, inputs, training=None, mask=None): """Calls the model on new inputs and returns the outputs as tensors. 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). Note: This method should not be called directly. It is only meant to be overridden when subclassing `tf.keras.Model`. To call a model on an input, always use the `__call__()` method, i.e. `model(inputs)`, which relies on the underlying `call()` method. Args: inputs: Input tensor, or dict/list/tuple of input 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 boolean tensor or None (no mask). For more details, check the guide [here](https://www.tensorflow.org/guide/keras/masking_and_padding). Returns: A tensor if there is a single output, or a list of tensors if there are more than one outputs. """ raise NotImplementedError( "Unimplemented `tf.keras.Model.call()`: if you " "intend to create a `Model` with the Functional " "API, please provide `inputs` and `outputs` " "arguments. Otherwise, subclass `Model` with an " "overridden `call()` method." ) @traceback_utils.filter_traceback def compile( self, optimizer="rmsprop", loss=None, metrics=None, loss_weights=None, weighted_metrics=None, run_eagerly=None, steps_per_execution=None, jit_compile=None, **kwargs, ): """Configures the model for training. Example: ```python model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-3), loss=tf.keras.losses.BinaryCrossentropy(), metrics=[tf.keras.metrics.BinaryAccuracy(), tf.keras.metrics.FalseNegatives()]) ``` Args: optimizer: String (name of optimizer) or optimizer instance. See `tf.keras.optimizers`. loss: Loss function. May be a string (name of loss function), or a `tf.keras.losses.Loss` instance. See `tf.keras.losses`. A loss function is any callable with the signature `loss = fn(y_true, y_pred)`, where `y_true` are the ground truth values, and `y_pred` are the model's predictions. `y_true` should have shape `(batch_size, d0, .. dN)` (except in the case of sparse loss functions such as sparse categorical crossentropy which expects integer arrays of shape `(batch_size, d0, .. dN-1)`). `y_pred` should have shape `(batch_size, d0, .. dN)`. The loss function should return a float tensor. If a custom `Loss` instance is used and reduction is set to `None`, return value has shape `(batch_size, d0, .. dN-1)` i.e. per-sample or per-timestep loss values; otherwise, it is a scalar. If the model has multiple outputs, you can use a different loss on each output by passing a dictionary or a list of losses. The loss value that will be minimized by the model will then be the sum of all individual losses, unless `loss_weights` is specified. metrics: List of metrics to be evaluated by the model during training and testing. Each of this can be a string (name of a built-in function), function or a `tf.keras.metrics.Metric` instance. See `tf.keras.metrics`. Typically you will use `metrics=['accuracy']`. A function is any callable with the signature `result = fn(y_true, y_pred)`. To specify different metrics for different outputs of a multi-output model, you could also pass a dictionary, such as `metrics={'output_a':'accuracy', 'output_b':['accuracy', 'mse']}`. You can also pass a list to specify a metric or a list of metrics for each output, such as `metrics=[['accuracy'], ['accuracy', 'mse']]` or `metrics=['accuracy', ['accuracy', 'mse']]`. When you pass the strings 'accuracy' or 'acc', we convert this to one of `tf.keras.metrics.BinaryAccuracy`, `tf.keras.metrics.CategoricalAccuracy`, `tf.keras.metrics.SparseCategoricalAccuracy` based on the loss function used and the model output shape. We do a similar conversion for the strings 'crossentropy' and 'ce' as well. The metrics passed here are evaluated without sample weighting; if you would like sample weighting to apply, you can specify your metrics via the `weighted_metrics` argument instead. loss_weights: Optional list or dictionary specifying scalar coefficients (Python floats) to weight the loss contributions of different model outputs. The loss value that will be minimized by the model will then be the *weighted sum* of all individual losses, weighted by the `loss_weights` coefficients. If a list, it is expected to have a 1:1 mapping to the model's outputs. If a dict, it is expected to map output names (strings) to scalar coefficients. weighted_metrics: List of metrics to be evaluated and weighted by `sample_weight` or `class_weight` during training and testing. run_eagerly: Bool. Defaults to `False`. If `True`, this `Model`'s logic will not be wrapped in a `tf.function`. Recommended to leave this as `None` unless your `Model` cannot be run inside a `tf.function`. `run_eagerly=True` is not supported when using `tf.distribute.experimental.ParameterServerStrategy`. steps_per_execution: Int. Defaults to 1. The number of batches to run during each `tf.function` call. Running multiple batches inside a single `tf.function` call can greatly improve performance on TPUs or small models with a large Python overhead. At most, one full epoch will be run each execution. If a number larger than the size of the epoch is passed, the execution will be truncated to the size of the epoch. Note that if `steps_per_execution` is set to `N`, `Callback.on_batch_begin` and `Callback.on_batch_end` methods will only be called every `N` batches (i.e. before/after each `tf.function` execution). jit_compile: If `True`, compile the model training step with XLA. [XLA](https://www.tensorflow.org/xla) is an optimizing compiler for machine learning. `jit_compile` is not enabled for by default. This option cannot be enabled with `run_eagerly=True`. Note that `jit_compile=True` may not necessarily work for all models. For more information on supported operations please refer to the [XLA documentation](https://www.tensorflow.org/xla). Also refer to [known XLA issues](https://www.tensorflow.org/xla/known_issues) for more details. **kwargs: Arguments supported for backwards compatibility only. """ base_layer.keras_api_gauge.get_cell("compile").set(True) with self.distribute_strategy.scope(): if "experimental_steps_per_execution" in kwargs: logging.warning( "The argument `steps_per_execution` is no longer " "experimental. Pass `steps_per_execution` instead of " "`experimental_steps_per_execution`." ) if not steps_per_execution: steps_per_execution = kwargs.pop( "experimental_steps_per_execution" ) # When compiling from an already-serialized model, we do not want to # reapply some processing steps (e.g. metric renaming for # multi-output models, which have prefixes added for each # corresponding output name). from_serialized = kwargs.pop("from_serialized", False) self._validate_compile(optimizer, metrics, **kwargs) self._run_eagerly = run_eagerly self.optimizer = self._get_optimizer(optimizer) if isinstance(loss, compile_utils.LossesContainer): self.compiled_loss = loss else: self.compiled_loss = compile_utils.LossesContainer( loss, loss_weights, output_names=self.output_names ) self.compiled_metrics = compile_utils.MetricsContainer( metrics, weighted_metrics, output_names=self.output_names, from_serialized=from_serialized, ) self._configure_steps_per_execution(steps_per_execution or 1) # Initializes attrs that are reset each time `compile` is called. self._reset_compile_cache() self._is_compiled = True self.loss = loss or {} if (self._run_eagerly or self.dynamic) and jit_compile: raise ValueError( "You cannot enable `run_eagerly` and `jit_compile` " "at the same time." ) else: self._jit_compile = jit_compile def _get_optimizer(self, optimizer): """Wraps `optimizer` in `LossScaleOptimizer` if necessary.""" def _get_single_optimizer(opt): opt = optimizers.get(opt) if self.dtype_policy.name == "mixed_float16" and not isinstance( opt, lso.BaseLossScaleOptimizer ): # Loss scaling is necessary with mixed_float16 for models to # converge to the same accuracy as with float32. opt = lso.BaseLossScaleOptimizer(opt) return opt return tf.nest.map_structure(_get_single_optimizer, optimizer) @tf.__internal__.tracking.no_automatic_dependency_tracking def _reset_compile_cache(self): self.train_function = None self.test_function = None self.predict_function = None # Used to cache the `tf.function`'ed `train_function` to be logged in # TensorBoard, since the original `train_function` is not necessarily # a `tf.function` (e.g., with ParameterServerStrategy, the # `train_function` is a scheduling of the actual training function to a # remote worker). self.train_tf_function = None # Used to cache `trainable` attr of `Layer`s for `fit`. self._compiled_trainable_state = self._get_trainable_state() @tf.__internal__.tracking.no_automatic_dependency_tracking def _configure_steps_per_execution(self, steps_per_execution): self._steps_per_execution = tf.Variable( steps_per_execution, dtype="int64", aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA, ) @property def _should_compute_mask(self): return False @property def metrics(self): """Returns the model's metrics added using `compile()`, `add_metric()` APIs. Note: Metrics passed to `compile()` are available only after a `keras.Model` has been trained/evaluated on actual data. Examples: >>> inputs = tf.keras.layers.Input(shape=(3,)) >>> outputs = tf.keras.layers.Dense(2)(inputs) >>> model = tf.keras.models.Model(inputs=inputs, outputs=outputs) >>> model.compile(optimizer="Adam", loss="mse", metrics=["mae"]) >>> [m.name for m in model.metrics] [] >>> x = np.random.random((2, 3)) >>> y = np.random.randint(0, 2, (2, 2)) >>> model.fit(x, y) >>> [m.name for m in model.metrics] ['loss', 'mae'] >>> inputs = tf.keras.layers.Input(shape=(3,)) >>> d = tf.keras.layers.Dense(2, name='out') >>> output_1 = d(inputs) >>> output_2 = d(inputs) >>> model = tf.keras.models.Model( ... inputs=inputs, outputs=[output_1, output_2]) >>> model.add_metric( ... tf.reduce_sum(output_2), name='mean', aggregation='mean') >>> model.compile(optimizer="Adam", loss="mse", metrics=["mae", "acc"]) >>> model.fit(x, (y, y)) >>> [m.name for m in model.metrics] ['loss', 'out_loss', 'out_1_loss', 'out_mae', 'out_acc', 'out_1_mae', 'out_1_acc', 'mean'] """ metrics = [] if self._is_compiled: # TODO(omalleyt): Track `LossesContainer` and `MetricsContainer` # objects so that attr names are not load-bearing. if self.compiled_loss is not None: metrics += self.compiled_loss.metrics if self.compiled_metrics is not None: metrics += self.compiled_metrics.metrics for l in self._flatten_layers(): metrics.extend(l._metrics) return metrics @property def metrics_names(self): """Returns the model's display labels for all outputs. Note: `metrics_names` are available only after a `keras.Model` has been trained/evaluated on actual data. Examples: >>> inputs = tf.keras.layers.Input(shape=(3,)) >>> outputs = tf.keras.layers.Dense(2)(inputs) >>> model = tf.keras.models.Model(inputs=inputs, outputs=outputs) >>> model.compile(optimizer="Adam", loss="mse", metrics=["mae"]) >>> model.metrics_names [] >>> x = np.random.random((2, 3)) >>> y = np.random.randint(0, 2, (2, 2)) >>> model.fit(x, y) >>> model.metrics_names ['loss', 'mae'] >>> inputs = tf.keras.layers.Input(shape=(3,)) >>> d = tf.keras.layers.Dense(2, name='out') >>> output_1 = d(inputs) >>> output_2 = d(inputs) >>> model = tf.keras.models.Model( ... inputs=inputs, outputs=[output_1, output_2]) >>> model.compile(optimizer="Adam", loss="mse", metrics=["mae", "acc"]) >>> model.fit(x, (y, y)) >>> model.metrics_names ['loss', 'out_loss', 'out_1_loss', 'out_mae', 'out_acc', 'out_1_mae', 'out_1_acc'] """ # This property includes all output names including `loss` and # per-output losses for backward compatibility. return [m.name for m in self.metrics] @property def distribute_strategy(self): """The `tf.distribute.Strategy` this model was created under.""" return self._distribution_strategy or tf.distribute.get_strategy() @property def run_eagerly(self): """Settable attribute indicating whether the model should run eagerly. Running eagerly means that your model will be run step by step, like Python code. Your model might run slower, but it should become easier for you to debug it by stepping into individual layer calls. By default, we will attempt to compile your model to a static graph to deliver the best execution performance. Returns: Boolean, whether the model should run eagerly. """ if self.dynamic and self._run_eagerly == False: # TODO(fchollet): consider using py_func to enable this. raise ValueError( "Your model contains layers that can only be " "successfully run in eager execution (layers " "constructed with `dynamic=True`). " "You cannot set `run_eagerly=False`." ) if self._cluster_coordinator and self._run_eagerly: raise ValueError( "When using `Model` with `ParameterServerStrategy`, " "`run_eagerly` is not supported." ) # Run eagerly logic, by priority: # (1) Dynamic models must be run eagerly. # (2) Explicitly setting run_eagerly causes a Model to be run eagerly. # (3) Not explicitly setting run_eagerly defaults to TF's global # setting. return ( self.dynamic or self._run_eagerly or (tf.config.functions_run_eagerly() and self._run_eagerly is None) ) @run_eagerly.setter def run_eagerly(self, value): self._run_eagerly = value def _validate_target_and_loss(self, y, loss): """Raises error if target or loss is not found. This method verifies that the target and loss are properly populated when applicable, or raises errors. Args: y: the target for training. loss: the total loss tensor including loss added via `compile` and `add_loss`. """ # `self.loss` references the loss added via `compile` call. If users # have provided such, the target must be provided; otherwise it's a user # error. Note that `self.loss` does not include losses added via # `add_loss`, and it is a valid use when such loss from `add_loss` # exists and target does not. if self.loss and y is None: raise ValueError( "Target data is missing. Your model was compiled with " f"loss={self.loss}, " "and therefore expects target data to be provided in `fit()`." ) # For training, there must be compiled loss or regularization loss to # exist in order to apply the gradients. If one is not found, it means # no loss was supplied via `compile` or `add_loss`. elif loss is None: raise ValueError( "No loss found. You may have forgotten to provide a `loss` " "argument in the `compile()` method." ) def train_step(self, data): """The logic for one training step. This method can be overridden to support custom training logic. For concrete examples of how to override this method see [Customizing what happens in fit]( https://www.tensorflow.org/guide/keras/customizing_what_happens_in_fit). This method is called by `Model.make_train_function`. This method should contain the mathematical logic for one step of training. This typically includes the forward pass, loss calculation, backpropagation, and metric updates. Configuration details for *how* this logic is run (e.g. `tf.function` and `tf.distribute.Strategy` settings), should be left to `Model.make_train_function`, which can also be overridden. Args: data: A nested structure of `Tensor`s. Returns: A `dict` containing values that will be passed to `tf.keras.callbacks.CallbackList.on_train_batch_end`. Typically, the values of the `Model`'s metrics are returned. Example: `{'loss': 0.2, 'accuracy': 0.7}`. """ x, y, sample_weight = data_adapter.unpack_x_y_sample_weight(data) # Run forward pass. with tf.GradientTape() as tape: y_pred = self(x, training=True) loss = self.compute_loss(x, y, y_pred, sample_weight) self._validate_target_and_loss(y, loss) # Run backwards pass. self.optimizer.minimize(loss, self.trainable_variables, tape=tape) return self.compute_metrics(x, y, y_pred, sample_weight) def compute_loss(self, x=None, y=None, y_pred=None, sample_weight=None): """Compute the total loss, validate it, and return it. Subclasses can optionally override this method to provide custom loss computation logic. Example: ```python class MyModel(tf.keras.Model): def __init__(self, *args, **kwargs): super(MyModel, self).__init__(*args, **kwargs) self.loss_tracker = tf.keras.metrics.Mean(name='loss') def compute_loss(self, x, y, y_pred, sample_weight): loss = tf.reduce_mean(tf.math.squared_difference(y_pred, y)) loss += tf.add_n(self.losses) self.loss_tracker.update_state(loss) return loss def reset_metrics(self): self.loss_tracker.reset_states() @property def metrics(self): return [self.loss_tracker] tensors = tf.random.uniform((10, 10)), tf.random.uniform((10,)) dataset = tf.data.Dataset.from_tensor_slices(tensors).repeat().batch(1) inputs = tf.keras.layers.Input(shape=(10,), name='my_input') outputs = tf.keras.layers.Dense(10)(inputs) model = MyModel(inputs, outputs) model.add_loss(tf.reduce_sum(outputs)) optimizer = tf.keras.optimizers.SGD() model.compile(optimizer, loss='mse', steps_per_execution=10) model.fit(dataset, epochs=2, steps_per_epoch=10) print('My custom loss: ', model.loss_tracker.result().numpy()) ``` Args: x: Input data. y: Target data. y_pred: Predictions returned by the model (output of `model(x)`) sample_weight: Sample weights for weighting the loss function. Returns: The total loss as a `tf.Tensor`, or `None` if no loss results (which is the case when called by `Model.test_step`). """ del x # The default implementation does not use `x`. return self.compiled_loss( y, y_pred, sample_weight, regularization_losses=self.losses ) def compute_metrics(self, x, y, y_pred, sample_weight): """Update metric states and collect all metrics to be returned. Subclasses can optionally override this method to provide custom metric updating and collection logic. Example: ```python class MyModel(tf.keras.Sequential): def compute_metrics(self, x, y, y_pred, sample_weight): # This super call updates `self.compiled_metrics` and returns # results for all metrics listed in `self.metrics`. metric_results = super(MyModel, self).compute_metrics( x, y, y_pred, sample_weight) # Note that `self.custom_metric` is not listed in `self.metrics`. self.custom_metric.update_state(x, y, y_pred, sample_weight) metric_results['custom_metric_name'] = self.custom_metric.result() return metric_results ``` Args: x: Input data. y: Target data. y_pred: Predictions returned by the model (output of `model.call(x)`) sample_weight: Sample weights for weighting the loss function. Returns: A `dict` containing values that will be passed to `tf.keras.callbacks.CallbackList.on_train_batch_end()`. Typically, the values of the metrics listed in `self.metrics` are returned. Example: `{'loss': 0.2, 'accuracy': 0.7}`. """ del x # The default implementation does not use `x`. self.compiled_metrics.update_state(y, y_pred, sample_weight) # Collect metrics to return return_metrics = {} for metric in self.metrics: result = metric.result() if isinstance(result, dict): return_metrics.update(result) else: return_metrics[metric.name] = result return return_metrics def make_train_function(self, force=False): """Creates a function that executes one step of training. This method can be overridden to support custom training logic. This method is called by `Model.fit` and `Model.train_on_batch`. Typically, this method directly controls `tf.function` and `tf.distribute.Strategy` settings, and delegates the actual training logic to `Model.train_step`. This function is cached the first time `Model.fit` or `Model.train_on_batch` is called. The cache is cleared whenever `Model.compile` is called. You can skip the cache and generate again the function with `force=True`. Args: force: Whether to regenerate the train function and skip the cached function if available. Returns: Function. The function created by this method should accept a `tf.data.Iterator`, and return a `dict` containing values that will be passed to `tf.keras.Callbacks.on_train_batch_end`, such as `{'loss': 0.2, 'accuracy': 0.7}`. """ if self.train_function is not None and not force: return self.train_function def step_function(model, iterator): """Runs a single training step.""" def run_step(data): outputs = model.train_step(data) # Ensure counter is updated only if `train_step` succeeds. with tf.control_dependencies(_minimum_control_deps(outputs)): model._train_counter.assign_add(1) return outputs if self._jit_compile: run_step = tf.function( run_step, jit_compile=True, reduce_retracing=True ) data = next(iterator) outputs = model.distribute_strategy.run(run_step, args=(data,)) outputs = reduce_per_replica( outputs, self.distribute_strategy, reduction="first" ) return outputs # Special case if steps_per_execution is one. if ( self._steps_per_execution is None or self._steps_per_execution.numpy().item() == 1 ): def train_function(iterator): """Runs a training execution with a single step.""" return step_function(self, iterator) if not self.run_eagerly: train_function = tf.function( train_function, reduce_retracing=True ) self.train_tf_function = train_function if self._cluster_coordinator: self.train_function = ( lambda it: self._cluster_coordinator.schedule( train_function, args=(it,) ) ) else: self.train_function = train_function # If we're using a coordinator, use the value of # self._steps_per_execution at the time the function is # called/scheduled, and not when it is actually executed. elif self._cluster_coordinator: def train_function(iterator, steps_per_execution): """Runs a training execution with multiple steps.""" for _ in tf.range(steps_per_execution): outputs = step_function(self, iterator) return outputs if not self.run_eagerly: train_function = tf.function( train_function, reduce_retracing=True ) self.train_tf_function = train_function self.train_function = lambda it: self._cluster_coordinator.schedule( train_function, args=(it, self._steps_per_execution.value()) ) else: def train_function(iterator): """Runs a training execution with multiple steps.""" for _ in tf.range(self._steps_per_execution): outputs = step_function(self, iterator) return outputs if not self.run_eagerly: train_function = tf.function( train_function, reduce_retracing=True ) self.train_tf_function = train_function self.train_function = train_function return self.train_function @traceback_utils.filter_traceback def fit( self, x=None, y=None, batch_size=None, epochs=1, verbose="auto", callbacks=None, validation_split=0.0, validation_data=None, shuffle=True, class_weight=None, sample_weight=None, initial_epoch=0, steps_per_epoch=None, validation_steps=None, validation_batch_size=None, validation_freq=1, max_queue_size=10, workers=1, use_multiprocessing=False, ): """Trains the model for a fixed number of epochs (iterations on a dataset). Args: x: Input data. It could be: - A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs). - A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs). - A dict mapping input names to the corresponding array/tensors, if the model has named inputs. - A `tf.data` dataset. Should return a tuple of either `(inputs, targets)` or `(inputs, targets, sample_weights)`. - A generator or `keras.utils.Sequence` returning `(inputs, targets)` or `(inputs, targets, sample_weights)`. - A `tf.keras.utils.experimental.DatasetCreator`, which wraps a callable that takes a single argument of type `tf.distribute.InputContext`, and returns a `tf.data.Dataset`. `DatasetCreator` should be used when users prefer to specify the per-replica batching and sharding logic for the `Dataset`. See `tf.keras.utils.experimental.DatasetCreator` doc for more information. A more detailed description of unpacking behavior for iterator types (Dataset, generator, Sequence) is given below. If these include `sample_weights` as a third component, note that sample weighting applies to the `weighted_metrics` argument but not the `metrics` argument in `compile()`. If using `tf.distribute.experimental.ParameterServerStrategy`, only `DatasetCreator` type is supported for `x`. y: Target data. Like the input data `x`, it could be either Numpy array(s) or TensorFlow tensor(s). It should be consistent with `x` (you cannot have Numpy inputs and tensor targets, or inversely). If `x` is a dataset, generator, or `keras.utils.Sequence` instance, `y` should not be specified (since targets will be obtained from `x`). batch_size: Integer or `None`. Number of samples per gradient update. If unspecified, `batch_size` will default to 32. Do not specify the `batch_size` if your data is in the form of datasets, generators, or `keras.utils.Sequence` instances (since they generate batches). epochs: Integer. Number of epochs to train the model. An epoch is an iteration over the entire `x` and `y` data provided (unless the `steps_per_epoch` flag is set to something other than None). Note that in conjunction with `initial_epoch`, `epochs` is to be understood as "final epoch". The model is not trained for a number of iterations given by `epochs`, but merely until the epoch of index `epochs` is reached. verbose: 'auto', 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch. 'auto' defaults to 1 for most cases, but 2 when used with `ParameterServerStrategy`. Note that the progress bar is not particularly useful when logged to a file, so verbose=2 is recommended when not running interactively (eg, in a production environment). callbacks: List of `keras.callbacks.Callback` instances. List of callbacks to apply during training. See `tf.keras.callbacks`. Note `tf.keras.callbacks.ProgbarLogger` and `tf.keras.callbacks.History` callbacks are created automatically and need not be passed into `model.fit`. `tf.keras.callbacks.ProgbarLogger` is created or not based on `verbose` argument to `model.fit`. Callbacks with batch-level calls are currently unsupported with `tf.distribute.experimental.ParameterServerStrategy`, and users are advised to implement epoch-level calls instead with an appropriate `steps_per_epoch` value. validation_split: Float between 0 and 1. Fraction of the training data to be used as validation data. The model will set apart this fraction of the training data, will not train on it, and will evaluate the loss and any model metrics on this data at the end of each epoch. The validation data is selected from the last samples in the `x` and `y` data provided, before shuffling. This argument is not supported when `x` is a dataset, generator or `keras.utils.Sequence` instance. If both `validation_data` and `validation_split` are provided, `validation_data` will override `validation_split`. `validation_split` is not yet supported with `tf.distribute.experimental.ParameterServerStrategy`. validation_data: Data on which to evaluate the loss and any model metrics at the end of each epoch. The model will not be trained on this data. Thus, note the fact that the validation loss of data provided using `validation_split` or `validation_data` is not affected by regularization layers like noise and dropout. `validation_data` will override `validation_split`. `validation_data` could be: - A tuple `(x_val, y_val)` of Numpy arrays or tensors. - A tuple `(x_val, y_val, val_sample_weights)` of NumPy arrays. - A `tf.data.Dataset`. - A Python generator or `keras.utils.Sequence` returning `(inputs, targets)` or `(inputs, targets, sample_weights)`. `validation_data` is not yet supported with `tf.distribute.experimental.ParameterServerStrategy`. shuffle: Boolean (whether to shuffle the training data before each epoch) or str (for 'batch'). This argument is ignored when `x` is a generator or an object of tf.data.Dataset. 'batch' is a special option for dealing with the limitations of HDF5 data; it shuffles in batch-sized chunks. Has no effect when `steps_per_epoch` is not `None`. class_weight: Optional dictionary mapping class indices (integers) to a weight (float) value, used for weighting the loss function (during training only). This can be useful to tell the model to "pay more attention" to samples from an under-represented class. sample_weight: Optional Numpy array of weights for the training samples, used for weighting the loss function (during training only). You can either pass a flat (1D) Numpy array with the same length as the input samples (1:1 mapping between weights and samples), or in the case of temporal data, you can pass a 2D array with shape `(samples, sequence_length)`, to apply a different weight to every timestep of every sample. This argument is not supported when `x` is a dataset, generator, or `keras.utils.Sequence` instance, instead provide the sample_weights as the third element of `x`. Note that sample weighting does not apply to metrics specified via the `metrics` argument in `compile()`. To apply sample weighting to your metrics, you can specify them via the `weighted_metrics` in `compile()` instead. initial_epoch: Integer. Epoch at which to start training (useful for resuming a previous training run). steps_per_epoch: Integer or `None`. Total number of steps (batches of samples) before declaring one epoch finished and starting the next epoch. When training with input tensors such as TensorFlow data tensors, the default `None` is equal to the number of samples in your dataset divided by the batch size, or 1 if that cannot be determined. If x is a `tf.data` dataset, and 'steps_per_epoch' is None, the epoch will run until the input dataset is exhausted. When passing an infinitely repeating dataset, you must specify the `steps_per_epoch` argument. If `steps_per_epoch=-1` the training will run indefinitely with an infinitely repeating dataset. This argument is not supported with array inputs. When using `tf.distribute.experimental.ParameterServerStrategy`: * `steps_per_epoch=None` is not supported. validation_steps: Only relevant if `validation_data` is provided and is a `tf.data` dataset. Total number of steps (batches of samples) to draw before stopping when performing validation at the end of every epoch. If 'validation_steps' is None, validation will run until the `validation_data` dataset is exhausted. In the case of an infinitely repeated dataset, it will run into an infinite loop. If 'validation_steps' is specified and only part of the dataset will be consumed, the evaluation will start from the beginning of the dataset at each epoch. This ensures that the same validation samples are used every time. validation_batch_size: Integer or `None`. Number of samples per validation batch. If unspecified, will default to `batch_size`. Do not specify the `validation_batch_size` if your data is in the form of datasets, generators, or `keras.utils.Sequence` instances (since they generate batches). validation_freq: Only relevant if validation data is provided. Integer or `collections.abc.Container` instance (e.g. list, tuple, etc.). If an integer, specifies how many training epochs to run before a new validation run is performed, e.g. `validation_freq=2` runs validation every 2 epochs. If a Container, specifies the epochs on which to run validation, e.g. `validation_freq=[1, 2, 10]` runs validation at the end of the 1st, 2nd, and 10th epochs. max_queue_size: Integer. Used for generator or `keras.utils.Sequence` input only. Maximum size for the generator queue. If unspecified, `max_queue_size` will default to 10. workers: Integer. Used for generator or `keras.utils.Sequence` input only. Maximum number of processes to spin up when using process-based threading. If unspecified, `workers` will default to 1. use_multiprocessing: Boolean. Used for generator or `keras.utils.Sequence` input only. If `True`, use process-based threading. If unspecified, `use_multiprocessing` will default to `False`. Note that because this implementation relies on multiprocessing, you should not pass non-picklable arguments to the generator as they can't be passed easily to children processes. Unpacking behavior for iterator-like inputs: A common pattern is to pass a tf.data.Dataset, generator, or tf.keras.utils.Sequence to the `x` argument of fit, which will in fact yield not only features (x) but optionally targets (y) and sample weights. Keras requires that the output of such iterator-likes be unambiguous. The iterator should return a tuple of length 1, 2, or 3, where the optional second and third elements will be used for y and sample_weight respectively. Any other type provided will be wrapped in a length one tuple, effectively treating everything as 'x'. When yielding dicts, they should still adhere to the top-level tuple structure. e.g. `({"x0": x0, "x1": x1}, y)`. Keras will not attempt to separate features, targets, and weights from the keys of a single dict. A notable unsupported data type is the namedtuple. The reason is that it behaves like both an ordered datatype (tuple) and a mapping datatype (dict). So given a namedtuple of the form: `namedtuple("example_tuple", ["y", "x"])` it is ambiguous whether to reverse the order of the elements when interpreting the value. Even worse is a tuple of the form: `namedtuple("other_tuple", ["x", "y", "z"])` where it is unclear if the tuple was intended to be unpacked into x, y, and sample_weight or passed through as a single element to `x`. As a result the data processing code will simply raise a ValueError if it encounters a namedtuple. (Along with instructions to remedy the issue.) Returns: A `History` object. Its `History.history` attribute is a record of training loss values and metrics values at successive epochs, as well as validation loss values and validation metrics values (if applicable). Raises: RuntimeError: 1. If the model was never compiled or, 2. If `model.fit` is wrapped in `tf.function`. ValueError: In case of mismatch between the provided input data and what the model expects or when the input data is empty. """ base_layer.keras_api_gauge.get_cell("fit").set(True) # Legacy graph support is contained in `training_v1.Model`. version_utils.disallow_legacy_graph("Model", "fit") self._assert_compile_was_called() self._check_call_args("fit") _disallow_inside_tf_function("fit") verbose = _get_verbosity(verbose, self.distribute_strategy) if validation_split and validation_data is None: # Create the validation data using the training data. Only supported # for `Tensor` and `NumPy` input. ( x, y, sample_weight, ), validation_data = data_adapter.train_validation_split( (x, y, sample_weight), validation_split=validation_split ) if validation_data: ( val_x, val_y, val_sample_weight, ) = data_adapter.unpack_x_y_sample_weight(validation_data) if self.distribute_strategy._should_use_with_coordinator: self._cluster_coordinator = ( tf.distribute.experimental.coordinator.ClusterCoordinator( self.distribute_strategy ) ) with self.distribute_strategy.scope(), training_utils.RespectCompiledTrainableState( # noqa: E501 self ): # Creates a `tf.data.Dataset` and handles batch and epoch iteration. data_handler = data_adapter.get_data_handler( x=x, y=y, sample_weight=sample_weight, batch_size=batch_size, steps_per_epoch=steps_per_epoch, initial_epoch=initial_epoch, epochs=epochs, shuffle=shuffle, class_weight=class_weight, max_queue_size=max_queue_size, workers=workers, use_multiprocessing=use_multiprocessing, model=self, steps_per_execution=self._steps_per_execution, ) # Container that configures and calls `tf.keras.Callback`s. if not isinstance(callbacks, callbacks_module.CallbackList): callbacks = callbacks_module.CallbackList( callbacks, add_history=True, add_progbar=verbose != 0, model=self, verbose=verbose, epochs=epochs, steps=data_handler.inferred_steps, ) self.stop_training = False self.train_function = self.make_train_function() self._train_counter.assign(0) callbacks.on_train_begin() training_logs = None # Handle fault-tolerance for multi-worker. # TODO(omalleyt): Fix the ordering issues that mean this has to # happen after `callbacks.on_train_begin`. steps_per_epoch_inferred = ( steps_per_epoch or data_handler.inferred_steps ) ( data_handler._initial_epoch, data_handler._initial_step, ) = self._maybe_load_initial_counters_from_ckpt( steps_per_epoch_inferred, initial_epoch ) logs = None for epoch, iterator in data_handler.enumerate_epochs(): self.reset_metrics() callbacks.on_epoch_begin(epoch) with data_handler.catch_stop_iteration(): data_handler._initial_step = data_handler._initial_step or ( self._maybe_load_initial_step_from_ckpt() ) for step in data_handler.steps(): with tf.profiler.experimental.Trace( "train", epoch_num=epoch, step_num=step, batch_size=batch_size, _r=1, ): callbacks.on_train_batch_begin(step) tmp_logs = self.train_function(iterator) if data_handler.should_sync: context.async_wait() # No error, now safe to assign to logs. logs = tmp_logs end_step = step + data_handler.step_increment callbacks.on_train_batch_end(end_step, logs) if self.stop_training: break logs = tf_utils.sync_to_numpy_or_python_type(logs) if logs is None: raise ValueError( "Unexpected result of `train_function` " "(Empty logs). Please use " "`Model.compile(..., run_eagerly=True)`, or " "`tf.config.run_functions_eagerly(True)` for more " "information of where went wrong, or file a " "issue/bug to `tf.keras`." ) epoch_logs = copy.copy(logs) # Run validation. if validation_data and self._should_eval( epoch, validation_freq ): # Create data_handler for evaluation and cache it. if getattr(self, "_eval_data_handler", None) is None: self._eval_data_handler = data_adapter.get_data_handler( x=val_x, y=val_y, sample_weight=val_sample_weight, batch_size=validation_batch_size or batch_size, steps_per_epoch=validation_steps, initial_epoch=0, epochs=1, max_queue_size=max_queue_size, workers=workers, use_multiprocessing=use_multiprocessing, model=self, steps_per_execution=self._steps_per_execution, ) val_logs = self.evaluate( x=val_x, y=val_y, sample_weight=val_sample_weight, batch_size=validation_batch_size or batch_size, steps=validation_steps, callbacks=callbacks, max_queue_size=max_queue_size, workers=workers, use_multiprocessing=use_multiprocessing, return_dict=True, _use_cached_eval_dataset=True, ) val_logs = { "val_" + name: val for name, val in val_logs.items() } epoch_logs.update(val_logs) callbacks.on_epoch_end(epoch, epoch_logs) training_logs = epoch_logs if self.stop_training: break if isinstance(self.optimizer, optimizer_experimental.Optimizer): self.optimizer.finalize_variable_values( self.trainable_variables ) # If eval data_handler exists, delete it after all epochs are done. if getattr(self, "_eval_data_handler", None) is not None: del self._eval_data_handler callbacks.on_train_end(logs=training_logs) return self.history def test_step(self, data): """The logic for one evaluation step. This method can be overridden to support custom evaluation logic. This method is called by `Model.make_test_function`. This function should contain the mathematical logic for one step of evaluation. This typically includes the forward pass, loss calculation, and metrics updates. Configuration details for *how* this logic is run (e.g. `tf.function` and `tf.distribute.Strategy` settings), should be left to `Model.make_test_function`, which can also be overridden. Args: data: A nested structure of `Tensor`s. Returns: A `dict` containing values that will be passed to `tf.keras.callbacks.CallbackList.on_train_batch_end`. Typically, the values of the `Model`'s metrics are returned. """ x, y, sample_weight = data_adapter.unpack_x_y_sample_weight(data) y_pred = self(x, training=False) # Updates stateful loss metrics. self.compute_loss(x, y, y_pred, sample_weight) return self.compute_metrics(x, y, y_pred, sample_weight) def make_test_function(self, force=False): """Creates a function that executes one step of evaluation. This method can be overridden to support custom evaluation logic. This method is called by `Model.evaluate` and `Model.test_on_batch`. Typically, this method directly controls `tf.function` and `tf.distribute.Strategy` settings, and delegates the actual evaluation logic to `Model.test_step`. This function is cached the first time `Model.evaluate` or `Model.test_on_batch` is called. The cache is cleared whenever `Model.compile` is called. You can skip the cache and generate again the function with `force=True`. Args: force: Whether to regenerate the test function and skip the cached function if available. Returns: Function. The function created by this method should accept a `tf.data.Iterator`, and return a `dict` containing values that will be passed to `tf.keras.Callbacks.on_test_batch_end`. """ if self.test_function is not None and not force: return self.test_function def step_function(model, iterator): """Runs a single evaluation step.""" def run_step(data): outputs = model.test_step(data) # Ensure counter is updated only if `test_step` succeeds. with tf.control_dependencies(_minimum_control_deps(outputs)): model._test_counter.assign_add(1) return outputs if self._jit_compile: run_step = tf.function( run_step, jit_compile=True, reduce_retracing=True ) data = next(iterator) outputs = model.distribute_strategy.run(run_step, args=(data,)) outputs = reduce_per_replica( outputs, self.distribute_strategy, reduction="first" ) return outputs # Special case if steps_per_execution is one. if ( self._steps_per_execution is None or self._steps_per_execution.numpy().item() == 1 ): def test_function(iterator): """Runs a test execution with a single step.""" return step_function(self, iterator) if not self.run_eagerly: test_function = tf.function( test_function, reduce_retracing=True ) if self._cluster_coordinator: self.test_function = ( lambda it: self._cluster_coordinator.schedule( test_function, args=(it,) ) ) else: self.test_function = test_function # If we're using a coordinator, use the value of # self._steps_per_execution at the time the function is # called/scheduled, and not when it is actually executed. elif self._cluster_coordinator: def test_function(iterator, steps_per_execution): """Runs a test execution with multiple steps.""" for _ in tf.range(steps_per_execution): outputs = step_function(self, iterator) return outputs if not self.run_eagerly: test_function = tf.function( test_function, reduce_retracing=True ) self.test_function = lambda it: self._cluster_coordinator.schedule( test_function, args=(it, self._steps_per_execution.value()) ) else: def test_function(iterator): """Runs a test execution with multiple steps.""" for _ in tf.range(self._steps_per_execution): outputs = step_function(self, iterator) return outputs if not self.run_eagerly: test_function = tf.function( test_function, reduce_retracing=True ) self.test_function = test_function return self.test_function @traceback_utils.filter_traceback def evaluate( self, x=None, y=None, batch_size=None, verbose="auto", sample_weight=None, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False, return_dict=False, **kwargs, ): """Returns the loss value & metrics values for the model in test mode. Computation is done in batches (see the `batch_size` arg.) Args: x: Input data. It could be: - A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs). - A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs). - A dict mapping input names to the corresponding array/tensors, if the model has named inputs. - A `tf.data` dataset. Should return a tuple of either `(inputs, targets)` or `(inputs, targets, sample_weights)`. - A generator or `keras.utils.Sequence` returning `(inputs, targets)` or `(inputs, targets, sample_weights)`. A more detailed description of unpacking behavior for iterator types (Dataset, generator, Sequence) is given in the `Unpacking behavior for iterator-like inputs` section of `Model.fit`. y: Target data. Like the input data `x`, it could be either Numpy array(s) or TensorFlow tensor(s). It should be consistent with `x` (you cannot have Numpy inputs and tensor targets, or inversely). If `x` is a dataset, generator or `keras.utils.Sequence` instance, `y` should not be specified (since targets will be obtained from the iterator/dataset). batch_size: Integer or `None`. Number of samples per batch of computation. If unspecified, `batch_size` will default to 32. Do not specify the `batch_size` if your data is in the form of a dataset, generators, or `keras.utils.Sequence` instances (since they generate batches). verbose: `"auto"`, 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = single line. `"auto"` defaults to 1 for most cases, and to 2 when used with `ParameterServerStrategy`. Note that the progress bar is not particularly useful when logged to a file, so `verbose=2` is recommended when not running interactively (e.g. in a production environment). sample_weight: Optional Numpy array of weights for the test samples, used for weighting the loss function. You can either pass a flat (1D) Numpy array with the same length as the input samples (1:1 mapping between weights and samples), or in the case of temporal data, you can pass a 2D array with shape `(samples, sequence_length)`, to apply a different weight to every timestep of every sample. This argument is not supported when `x` is a dataset, instead pass sample weights as the third element of `x`. steps: Integer or `None`. Total number of steps (batches of samples) before declaring the evaluation round finished. Ignored with the default value of `None`. If x is a `tf.data` dataset and `steps` is None, 'evaluate' will run until the dataset is exhausted. This argument is not supported with array inputs. callbacks: List of `keras.callbacks.Callback` instances. List of callbacks to apply during evaluation. See [callbacks](/api_docs/python/tf/keras/callbacks). max_queue_size: Integer. Used for generator or `keras.utils.Sequence` input only. Maximum size for the generator queue. If unspecified, `max_queue_size` will default to 10. workers: Integer. Used for generator or `keras.utils.Sequence` input only. Maximum number of processes to spin up when using process-based threading. If unspecified, `workers` will default to 1. use_multiprocessing: Boolean. Used for generator or `keras.utils.Sequence` input only. If `True`, use process-based threading. If unspecified, `use_multiprocessing` will default to `False`. Note that because this implementation relies on multiprocessing, you should not pass non-picklable arguments to the generator as they can't be passed easily to children processes. return_dict: If `True`, loss and metric results are returned as a dict, with each key being the name of the metric. If `False`, they are returned as a list. **kwargs: Unused at this time. See the discussion of `Unpacking behavior for iterator-like inputs` for `Model.fit`. Returns: Scalar test loss (if the model has a single output and no metrics) or list of scalars (if the model has multiple outputs and/or metrics). The attribute `model.metrics_names` will give you the display labels for the scalar outputs. Raises: RuntimeError: If `model.evaluate` is wrapped in a `tf.function`. """ base_layer.keras_api_gauge.get_cell("evaluate").set(True) version_utils.disallow_legacy_graph("Model", "evaluate") self._assert_compile_was_called() self._check_call_args("evaluate") self._check_sample_weight_warning(x, sample_weight) _disallow_inside_tf_function("evaluate") use_cached_eval_dataset = kwargs.pop("_use_cached_eval_dataset", False) if kwargs: raise TypeError(f"Invalid keyword arguments: {list(kwargs.keys())}") if self.distribute_strategy._should_use_with_coordinator: self._cluster_coordinator = ( tf.distribute.experimental.coordinator.ClusterCoordinator( self.distribute_strategy ) ) verbose = _get_verbosity(verbose, self.distribute_strategy) with self.distribute_strategy.scope(): # Use cached evaluation data only when it's called in `Model.fit` if ( use_cached_eval_dataset and getattr(self, "_eval_data_handler", None) is not None ): data_handler = self._eval_data_handler else: # Creates a `tf.data.Dataset` and handles batch and epoch # iteration. data_handler = data_adapter.get_data_handler( x=x, y=y, sample_weight=sample_weight, batch_size=batch_size, steps_per_epoch=steps, initial_epoch=0, epochs=1, max_queue_size=max_queue_size, workers=workers, use_multiprocessing=use_multiprocessing, model=self, steps_per_execution=self._steps_per_execution, ) # Container that configures and calls `tf.keras.Callback`s. if not isinstance(callbacks, callbacks_module.CallbackList): callbacks = callbacks_module.CallbackList( callbacks, add_history=True, add_progbar=verbose != 0, model=self, verbose=verbose, epochs=1, steps=data_handler.inferred_steps, ) logs = {} self.test_function = self.make_test_function() self._test_counter.assign(0) callbacks.on_test_begin() for _, iterator in data_handler.enumerate_epochs(): # Single epoch. self.reset_metrics() with data_handler.catch_stop_iteration(): for step in data_handler.steps(): with tf.profiler.experimental.Trace( "test", step_num=step, _r=1 ): callbacks.on_test_batch_begin(step) tmp_logs = self.test_function(iterator) if data_handler.should_sync: context.async_wait() # No error, now safe to assign to logs. logs = tmp_logs end_step = step + data_handler.step_increment callbacks.on_test_batch_end(end_step, logs) logs = tf_utils.sync_to_numpy_or_python_type(logs) callbacks.on_test_end(logs=logs) if return_dict: return logs else: return flatten_metrics_in_order(logs, self.metrics_names) def predict_step(self, data): """The logic for one inference step. This method can be overridden to support custom inference logic. This method is called by `Model.make_predict_function`. This method should contain the mathematical logic for one step of inference. This typically includes the forward pass. Configuration details for *how* this logic is run (e.g. `tf.function` and `tf.distribute.Strategy` settings), should be left to `Model.make_predict_function`, which can also be overridden. Args: data: A nested structure of `Tensor`s. Returns: The result of one inference step, typically the output of calling the `Model` on data. """ x, _, _ = data_adapter.unpack_x_y_sample_weight(data) return self(x, training=False) def make_predict_function(self, force=False): """Creates a function that executes one step of inference. This method can be overridden to support custom inference logic. This method is called by `Model.predict` and `Model.predict_on_batch`. Typically, this method directly controls `tf.function` and `tf.distribute.Strategy` settings, and delegates the actual evaluation logic to `Model.predict_step`. This function is cached the first time `Model.predict` or `Model.predict_on_batch` is called. The cache is cleared whenever `Model.compile` is called. You can skip the cache and generate again the function with `force=True`. Args: force: Whether to regenerate the predict function and skip the cached function if available. Returns: Function. The function created by this method should accept a `tf.data.Iterator`, and return the outputs of the `Model`. """ if self.predict_function is not None and not force: return self.predict_function def step_function(model, iterator): """Runs a single evaluation step.""" def run_step(data): outputs = model.predict_step(data) # Ensure counter is updated only if `test_step` succeeds. with tf.control_dependencies(_minimum_control_deps(outputs)): model._predict_counter.assign_add(1) return outputs if self._jit_compile: run_step = tf.function( run_step, jit_compile=True, reduce_retracing=True ) data = next(iterator) outputs = model.distribute_strategy.run(run_step, args=(data,)) outputs = reduce_per_replica( outputs, self.distribute_strategy, reduction="concat" ) return outputs # Special case if steps_per_execution is one. if ( self._steps_per_execution is None or self._steps_per_execution.numpy().item() == 1 ): def predict_function(iterator): """Runs an evaluation execution with a single step.""" return step_function(self, iterator) else: def predict_function(iterator): """Runs an evaluation execution with multiple steps.""" outputs = step_function(self, iterator) for _ in tf.range(self._steps_per_execution - 1): tf.autograph.experimental.set_loop_options( shape_invariants=[ ( outputs, tf.nest.map_structure( lambda t: tf_utils.get_tensor_spec( t, dynamic_batch=True ).shape, outputs, ), ) ] ) step_outputs = step_function(self, iterator) outputs = tf.nest.map_structure( lambda t1, t2: concat([t1, t2]), outputs, step_outputs ) return outputs if not self.run_eagerly: predict_function = tf.function( predict_function, reduce_retracing=True ) self.predict_function = predict_function return self.predict_function @traceback_utils.filter_traceback def predict( self, x, batch_size=None, verbose="auto", steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False, ): """Generates output predictions for the input samples. Computation is done in batches. This method is designed for batch processing of large numbers of inputs. It is not intended for use inside of loops that iterate over your data and process small numbers of inputs at a time. For small numbers of inputs that fit in one batch, directly use `__call__()` for faster execution, e.g., `model(x)`, or `model(x, training=False)` if you have layers such as `tf.keras.layers.BatchNormalization` that behave differently during inference. You may pair the individual model call with a `tf.function` for additional performance inside your inner loop. If you need access to numpy array values instead of tensors after your model call, you can use `tensor.numpy()` to get the numpy array value of an eager tensor. Also, note the fact that test loss is not affected by regularization layers like noise and dropout. Note: See [this FAQ entry]( https://keras.io/getting_started/faq/#whats-the-difference-between-model-methods-predict-and-call) for more details about the difference between `Model` methods `predict()` and `__call__()`. Args: x: Input samples. It could be: - A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs). - A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs). - A `tf.data` dataset. - A generator or `keras.utils.Sequence` instance. A more detailed description of unpacking behavior for iterator types (Dataset, generator, Sequence) is given in the `Unpacking behavior for iterator-like inputs` section of `Model.fit`. batch_size: Integer or `None`. Number of samples per batch. If unspecified, `batch_size` will default to 32. Do not specify the `batch_size` if your data is in the form of dataset, generators, or `keras.utils.Sequence` instances (since they generate batches). verbose: `"auto"`, 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = single line. `"auto"` defaults to 1 for most cases, and to 2 when used with `ParameterServerStrategy`. Note that the progress bar is not particularly useful when logged to a file, so `verbose=2` is recommended when not running interactively (e.g. in a production environment). steps: Total number of steps (batches of samples) before declaring the prediction round finished. Ignored with the default value of `None`. If x is a `tf.data` dataset and `steps` is None, `predict()` will run until the input dataset is exhausted. callbacks: List of `keras.callbacks.Callback` instances. List of callbacks to apply during prediction. See [callbacks](/api_docs/python/tf/keras/callbacks). max_queue_size: Integer. Used for generator or `keras.utils.Sequence` input only. Maximum size for the generator queue. If unspecified, `max_queue_size` will default to 10. workers: Integer. Used for generator or `keras.utils.Sequence` input only. Maximum number of processes to spin up when using process-based threading. If unspecified, `workers` will default to 1. use_multiprocessing: Boolean. Used for generator or `keras.utils.Sequence` input only. If `True`, use process-based threading. If unspecified, `use_multiprocessing` will default to `False`. Note that because this implementation relies on multiprocessing, you should not pass non-picklable arguments to the generator as they can't be passed easily to children processes. See the discussion of `Unpacking behavior for iterator-like inputs` for `Model.fit`. Note that Model.predict uses the same interpretation rules as `Model.fit` and `Model.evaluate`, so inputs must be unambiguous for all three methods. Returns: Numpy array(s) of predictions. Raises: RuntimeError: If `model.predict` is wrapped in a `tf.function`. ValueError: In case of mismatch between the provided input data and the model's expectations, or in case a stateful model receives a number of samples that is not a multiple of the batch size. """ base_layer.keras_api_gauge.get_cell("predict").set(True) version_utils.disallow_legacy_graph("Model", "predict") self._check_call_args("predict") _disallow_inside_tf_function("predict") # TODO(yashkatariya): Cache model on the coordinator for faster # prediction. If running under PSS, then swap it with OneDeviceStrategy # so that execution will run on the coordinator. original_pss_strategy = None if self.distribute_strategy._should_use_with_coordinator: original_pss_strategy = self.distribute_strategy self._distribution_strategy = None # Cluster coordinator is set by `.fit()` and `.evaluate()` which is not # needed in `.predict()` because all the predictions happen on the # coordinator/locally. if self._cluster_coordinator: self._cluster_coordinator = None verbose = _get_verbosity(verbose, self.distribute_strategy) outputs = None with self.distribute_strategy.scope(): # Creates a `tf.data.Dataset` and handles batch and epoch iteration. dataset_types = (tf.compat.v1.data.Dataset, tf.data.Dataset) if ( self._in_multi_worker_mode() or _is_tpu_multi_host(self.distribute_strategy) ) and isinstance(x, dataset_types): try: options = tf.data.Options() data_option = tf.data.experimental.AutoShardPolicy.DATA options.experimental_distribute.auto_shard_policy = ( data_option ) x = x.with_options(options) except ValueError: warnings.warn( "Using Model.predict with MultiWorkerMirroredStrategy " "or TPUStrategy and AutoShardPolicy.FILE might lead to " "out-of-order result. Consider setting it to " "AutoShardPolicy.DATA.", stacklevel=2, ) data_handler = data_adapter.get_data_handler( x=x, batch_size=batch_size, steps_per_epoch=steps, initial_epoch=0, epochs=1, max_queue_size=max_queue_size, workers=workers, use_multiprocessing=use_multiprocessing, model=self, steps_per_execution=self._steps_per_execution, ) # Container that configures and calls `tf.keras.Callback`s. if not isinstance(callbacks, callbacks_module.CallbackList): callbacks = callbacks_module.CallbackList( callbacks, add_history=True, add_progbar=verbose != 0, model=self, verbose=verbose, epochs=1, steps=data_handler.inferred_steps, ) self.predict_function = self.make_predict_function() self._predict_counter.assign(0) callbacks.on_predict_begin() batch_outputs = None for _, iterator in data_handler.enumerate_epochs(): # Single epoch. with data_handler.catch_stop_iteration(): for step in data_handler.steps(): callbacks.on_predict_batch_begin(step) tmp_batch_outputs = self.predict_function(iterator) if data_handler.should_sync: context.async_wait() batch_outputs = ( tmp_batch_outputs # No error, now safe to assign. ) if outputs is None: outputs = tf.nest.map_structure( lambda batch_output: [batch_output], batch_outputs, ) else: tf.__internal__.nest.map_structure_up_to( batch_outputs, lambda output, batch_output: output.append( batch_output ), outputs, batch_outputs, ) end_step = step + data_handler.step_increment callbacks.on_predict_batch_end( end_step, {"outputs": batch_outputs} ) if batch_outputs is None: raise ValueError( "Unexpected result of `predict_function` " "(Empty batch_outputs). Please use " "`Model.compile(..., run_eagerly=True)`, or " "`tf.config.run_functions_eagerly(True)` for more " "information of where went wrong, or file a " "issue/bug to `tf.keras`." ) callbacks.on_predict_end() all_outputs = tf.__internal__.nest.map_structure_up_to( batch_outputs, potentially_ragged_concat, outputs ) # If originally PSS strategy was used, then replace it back since # predict is running under `OneDeviceStrategy` after the swap and once # its done we need to replace it back to PSS again. if original_pss_strategy is not None: self._distribution_strategy = original_pss_strategy return tf_utils.sync_to_numpy_or_python_type(all_outputs) def reset_metrics(self): """Resets the state of all the metrics in the model. Examples: >>> inputs = tf.keras.layers.Input(shape=(3,)) >>> outputs = tf.keras.layers.Dense(2)(inputs) >>> model = tf.keras.models.Model(inputs=inputs, outputs=outputs) >>> model.compile(optimizer="Adam", loss="mse", metrics=["mae"]) >>> x = np.random.random((2, 3)) >>> y = np.random.randint(0, 2, (2, 2)) >>> _ = model.fit(x, y, verbose=0) >>> assert all(float(m.result()) for m in model.metrics) >>> model.reset_metrics() >>> assert all(float(m.result()) == 0 for m in model.metrics) """ for m in self.metrics: m.reset_state() def train_on_batch( self, x, y=None, sample_weight=None, class_weight=None, reset_metrics=True, return_dict=False, ): """Runs a single gradient update on a single batch of data. Args: x: Input data. It could be: - A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs). - A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs). - A dict mapping input names to the corresponding array/tensors, if the model has named inputs. y: Target data. Like the input data `x`, it could be either Numpy array(s) or TensorFlow tensor(s). sample_weight: Optional array of the same length as x, containing weights to apply to the model's loss for each sample. In the case of temporal data, you can pass a 2D array with shape (samples, sequence_length), to apply a different weight to every timestep of every sample. class_weight: Optional dictionary mapping class indices (integers) to a weight (float) to apply to the model's loss for the samples from this class during training. This can be useful to tell the model to "pay more attention" to samples from an under-represented class. reset_metrics: If `True`, the metrics returned will be only for this batch. If `False`, the metrics will be statefully accumulated across batches. return_dict: If `True`, loss and metric results are returned as a dict, with each key being the name of the metric. If `False`, they are returned as a list. Returns: Scalar training loss (if the model has a single output and no metrics) or list of scalars (if the model has multiple outputs and/or metrics). The attribute `model.metrics_names` will give you the display labels for the scalar outputs. Raises: RuntimeError: If `model.train_on_batch` is wrapped in a `tf.function`. """ self._assert_compile_was_called() self._check_call_args("train_on_batch") _disallow_inside_tf_function("train_on_batch") if reset_metrics: self.reset_metrics() with self.distribute_strategy.scope(), training_utils.RespectCompiledTrainableState( # noqa: E501 self ): iterator = data_adapter.single_batch_iterator( self.distribute_strategy, x, y, sample_weight, class_weight ) self.train_function = self.make_train_function() logs = self.train_function(iterator) logs = tf_utils.sync_to_numpy_or_python_type(logs) if return_dict: return logs else: return flatten_metrics_in_order(logs, self.metrics_names) def test_on_batch( self, x, y=None, sample_weight=None, reset_metrics=True, return_dict=False, ): """Test the model on a single batch of samples. Args: x: Input data. It could be: - A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs). - A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs). - A dict mapping input names to the corresponding array/tensors, if the model has named inputs. y: Target data. Like the input data `x`, it could be either Numpy array(s) or TensorFlow tensor(s). It should be consistent with `x` (you cannot have Numpy inputs and tensor targets, or inversely). sample_weight: Optional array of the same length as x, containing weights to apply to the model's loss for each sample. In the case of temporal data, you can pass a 2D array with shape (samples, sequence_length), to apply a different weight to every timestep of every sample. reset_metrics: If `True`, the metrics returned will be only for this batch. If `False`, the metrics will be statefully accumulated across batches. return_dict: If `True`, loss and metric results are returned as a dict, with each key being the name of the metric. If `False`, they are returned as a list. Returns: Scalar test loss (if the model has a single output and no metrics) or list of scalars (if the model has multiple outputs and/or metrics). The attribute `model.metrics_names` will give you the display labels for the scalar outputs. Raises: RuntimeError: If `model.test_on_batch` is wrapped in a `tf.function`. """ self._assert_compile_was_called() self._check_call_args("test_on_batch") _disallow_inside_tf_function("test_on_batch") if reset_metrics: self.reset_metrics() with self.distribute_strategy.scope(): iterator = data_adapter.single_batch_iterator( self.distribute_strategy, x, y, sample_weight ) self.test_function = self.make_test_function() logs = self.test_function(iterator) logs = tf_utils.sync_to_numpy_or_python_type(logs) if return_dict: return logs else: return flatten_metrics_in_order(logs, self.metrics_names) def predict_on_batch(self, x): """Returns predictions for a single batch of samples. Args: x: Input data. It could be: - A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs). - A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs). Returns: Numpy array(s) of predictions. Raises: RuntimeError: If `model.predict_on_batch` is wrapped in a `tf.function`. """ self._check_call_args("predict_on_batch") _disallow_inside_tf_function("predict_on_batch") with self.distribute_strategy.scope(): iterator = data_adapter.single_batch_iterator( self.distribute_strategy, x ) self.predict_function = self.make_predict_function() outputs = self.predict_function(iterator) return tf_utils.sync_to_numpy_or_python_type(outputs) @doc_controls.do_not_generate_docs def fit_generator( self, generator, steps_per_epoch=None, epochs=1, verbose=1, callbacks=None, validation_data=None, validation_steps=None, validation_freq=1, class_weight=None, max_queue_size=10, workers=1, use_multiprocessing=False, shuffle=True, initial_epoch=0, ): """Fits the model on data yielded batch-by-batch by a Python generator. DEPRECATED: `Model.fit` now supports generators, so there is no longer any need to use this endpoint. """ warnings.warn( "`Model.fit_generator` is deprecated and " "will be removed in a future version. " "Please use `Model.fit`, which supports generators.", stacklevel=2, ) return self.fit( generator, steps_per_epoch=steps_per_epoch, epochs=epochs, verbose=verbose, callbacks=callbacks, validation_data=validation_data, validation_steps=validation_steps, validation_freq=validation_freq, class_weight=class_weight, max_queue_size=max_queue_size, workers=workers, use_multiprocessing=use_multiprocessing, shuffle=shuffle, initial_epoch=initial_epoch, ) @doc_controls.do_not_generate_docs def evaluate_generator( self, generator, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False, verbose=0, ): """Evaluates the model on a data generator. DEPRECATED: `Model.evaluate` now supports generators, so there is no longer any need to use this endpoint. """ warnings.warn( "`Model.evaluate_generator` is deprecated and " "will be removed in a future version. " "Please use `Model.evaluate`, which supports generators.", stacklevel=2, ) self._check_call_args("evaluate_generator") return self.evaluate( generator, steps=steps, max_queue_size=max_queue_size, workers=workers, use_multiprocessing=use_multiprocessing, verbose=verbose, callbacks=callbacks, ) @doc_controls.do_not_generate_docs def predict_generator( self, generator, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False, verbose=0, ): """Generates predictions for the input samples from a data generator. DEPRECATED: `Model.predict` now supports generators, so there is no longer any need to use this endpoint. """ warnings.warn( "`Model.predict_generator` is deprecated and " "will be removed in a future version. " "Please use `Model.predict`, which supports generators.", stacklevel=2, ) return self.predict( generator, steps=steps, max_queue_size=max_queue_size, workers=workers, use_multiprocessing=use_multiprocessing, verbose=verbose, callbacks=callbacks, ) ###################################################################### # Functions below are not training related. They are for model weights # tracking, save/load, serialization, etc. ###################################################################### @property def trainable_weights(self): self._assert_weights_created() if not self._trainable: return [] trainable_variables = [] for trackable_obj in self._self_tracked_trackables: trainable_variables += trackable_obj.trainable_variables trainable_variables += self._trainable_weights return self._dedup_weights(trainable_variables) @property def non_trainable_weights(self): self._assert_weights_created() non_trainable_variables = [] for trackable_obj in self._self_tracked_trackables: non_trainable_variables += trackable_obj.non_trainable_variables if not self._trainable: # Return order is all trainable vars, then all non-trainable vars. trainable_variables = [] for trackable_obj in self._self_tracked_trackables: trainable_variables += trackable_obj.trainable_variables non_trainable_variables = ( trainable_variables + self._trainable_weights + non_trainable_variables + self._non_trainable_weights ) else: non_trainable_variables = ( non_trainable_variables + self._non_trainable_weights ) return self._dedup_weights(non_trainable_variables) def get_weights(self): """Retrieves the weights of the model. Returns: A flat list of Numpy arrays. """ with self.distribute_strategy.scope(): return super().get_weights() @traceback_utils.filter_traceback def save( self, filepath, overwrite=True, include_optimizer=True, save_format=None, signatures=None, options=None, save_traces=True, ): """Saves the model to Tensorflow SavedModel or a single HDF5 file. Please see `tf.keras.models.save_model` or the [Serialization and Saving guide]( https://keras.io/guides/serialization_and_saving/) for details. Args: filepath: String, PathLike, path to SavedModel or H5 file to save the model. 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. save_format: Either `'tf'` or `'h5'`, indicating whether to save the model to Tensorflow SavedModel or HDF5. Defaults to 'tf' in TF 2.X, and 'h5' in TF 1.X. signatures: Signatures to save with the SavedModel. Applicable to the 'tf' format only. Please see the `signatures` argument in `tf.saved_model.save` for details. options: (only applies to SavedModel format) `tf.saved_model.SaveOptions` object that specifies options for saving to SavedModel. save_traces: (only applies to SavedModel format) When enabled, the SavedModel will store the function traces for each layer. This can be disabled, so that only the configs of each layer are stored. Defaults to `True`. Disabling this will decrease serialization time and reduce file size, but it requires that all custom layers/models implement a `get_config()` method. 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') ``` """ save.save_model( self, filepath, overwrite, include_optimizer, save_format, signatures, options, save_traces, ) @traceback_utils.filter_traceback def save_weights( self, filepath, overwrite=True, save_format=None, options=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. While the formats are the same, do not mix `save_weights` and `tf.train.Checkpoint`. Checkpoints saved by `Model.save_weights` should be loaded using `Model.load_weights`. Checkpoints saved using `tf.train.Checkpoint.save` should be restored using the corresponding `tf.train.Checkpoint.restore`. Prefer `tf.train.Checkpoint` over `save_weights` for training checkpoints. The TensorFlow format matches objects and variables by starting at a root object, `self` for `save_weights`, and greedily matching attribute names. For `Model.save` this is the `Model`, and for `Checkpoint.save` this is the `Checkpoint` even if the `Checkpoint` has a model attached. This means saving a `tf.keras.Model` using `save_weights` and loading into a `tf.train.Checkpoint` with a `Model` attached (or vice versa) will not match the `Model`'s variables. See the [guide to training checkpoints]( https://www.tensorflow.org/guide/checkpoint) for details on the TensorFlow format. Args: filepath: String or PathLike, 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'. options: Optional `tf.train.CheckpointOptions` object that specifies options for saving weights. Raises: ImportError: If `h5py` is not available when attempting to save in HDF5 format. """ self._assert_weights_created() filepath = io_utils.path_to_string(filepath) filepath_is_h5 = saving_utils.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( f"Unknown format. Received: `save_format`={save_format}. " 'Was expecting one of {"tf", "h5"}.' ) if save_format == "tf" and filepath_is_h5: raise ValueError( 'save_weights got save_format="tf"/"tensorflow", but the ' f"filepath ({filepath}) looks like an HDF5 file. " 'Omit the ".h5"/".keras" when saving in TensorFlow format.' ) if save_format == "h5" and h5py is None: raise ImportError( "`save_weights` requires h5py when saving in hdf5, but h5py is " "not available. Try installing h5py package." ) 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 = io_utils.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) else: if not tf.executing_eagerly(): # Call `get_session` to initialize any uninitialized variables. backend.get_session() self._checkpoint.write(filepath, options=options) # Record this checkpoint so it's visible from # tf.train.latest_checkpoint. tf.__internal__.train.update_checkpoint_state( save_dir=os.path.dirname(filepath), model_checkpoint_path=filepath, save_relative_paths=True, all_model_checkpoint_paths=[filepath], ) @traceback_utils.filter_traceback def load_weights( self, filepath, by_name=False, skip_mismatch=False, options=None ): """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. Args: 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`). This can also be a path to a SavedModel saved from `model.save`. by_name: Boolean, whether to load weights by name or by topological order. Only topological loading is supported for weight files in TensorFlow format. skip_mismatch: Boolean, whether to skip loading of layers where there is a mismatch in the number of weights, or a mismatch in the shape of the weight (only valid when `by_name=True`). options: Optional `tf.train.CheckpointOptions` object that specifies options for loading weights. 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. ValueError: If `skip_mismatch` is set to `True` when `by_name` is `False`. """ if backend.is_tpu_strategy(self._distribution_strategy): if self._distribution_strategy.extended.steps_per_run > 1 and ( not saving_utils.is_hdf5_filepath(filepath) ): spr = self._distribution_strategy.extended.steps_per_run raise ValueError( "Load weights is not implemented with TPUStrategy " "with `steps_per_run` greater than 1. The " f"`steps_per_run` is {spr}" ) if skip_mismatch and not by_name: raise ValueError( "When calling model.load_weights, skip_mismatch can only be " "set to True when by_name is True." ) filepath, save_format = _detect_save_format(filepath) if save_format == "tf": status = self._checkpoint.read(filepath, options) 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 tf.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. tf.__internal__.tracking.streaming_restore( status=status, session=session ) status.assert_nontrivial_match() else: status = None if h5py is None: raise ImportError( "`load_weights` requires h5py package when loading weights " "from HDF5. Try installing h5py." ) if not self._is_graph_network and not self.built: raise ValueError( "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." ) self._assert_weights_created() 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, skip_mismatch ) else: hdf5_format.load_weights_from_hdf5_group(f, self) # Perform any layer defined finalization of the layer state. for layer in self.layers: layer.finalize_state() return status def _updated_config(self): """Util shared between different serialization methods. Returns: Model config with Keras version information added. """ from keras import __version__ as keras_version config = self.get_config() model_config = { "class_name": self.__class__.__name__, "config": config, "keras_version": keras_version, "backend": backend.backend(), } return model_config def get_config(self): """Returns the config of the `Model`. Config is a Python dictionary (serializable) containing the configuration of an object, which in this case is a `Model`. This allows the `Model` to be be reinstantiated later (without its trained weights) from this configuration. Note that `get_config()` does not guarantee to return a fresh copy of dict every time it is called. The callers should make a copy of the returned dict if they want to modify it. Developers of subclassed `Model` are advised to override this method, and continue to update the dict from `super(MyModel, self).get_config()` to provide the proper configuration of this `Model`. The default config is an empty dict. Optionally, raise `NotImplementedError` to allow Keras to attempt a default serialization. Returns: Python dictionary containing the configuration of this `Model`. """ # Return an empty dict here because otherwise subclass model developers # may see their model's `__init__()` be fed with unexpected keyword # argument, if their `__init__()` takes no argument for example, and # they don't override `from_config()`, which would use `cls(**config)` # as a result. config = {} if saving_lib._ENABLED: if self.optimizer: config["optimizer"] = saving_lib.serialize_keras_object( self.optimizer ) if self.compiled_loss: config["loss"] = saving_lib.serialize_keras_object( self.compiled_loss ) if self.built: config["input_shape"] = self._build_input_shape return config @classmethod def from_config(cls, config, custom_objects=None): # Grab the optimizer and loss from the `config` for `compile()` and # `build()`. optimizer, loss = None, None optimizer_dict = config.pop("optimizer", {}) if optimizer_dict: optimizer = saving_lib.deserialize_keras_object(optimizer_dict) loss_dict = config.pop("loss", {}) if loss_dict: loss = saving_lib.deserialize_keras_object(loss_dict) input_shape = config.pop("input_shape", {}) # `from_config` assumes `cls` is either `Functional` or a child class of # `Functional`. In the case that `cls` is meant to behave like a child # class of `Functional` but only inherits from the `Model` class, we # have to call `cls(...)` instead of `Functional.from_config`. from keras.engine import functional with generic_utils.SharedObjectLoadingScope(): functional_model_keys = [ "name", "layers", "input_layers", "output_layers", ] if all(key in config for key in functional_model_keys): inputs, outputs, layers = functional.reconstruct_from_config( config, custom_objects ) model = cls( inputs=inputs, outputs=outputs, name=config.get("name") ) functional.connect_ancillary_layers(model, layers) else: # The config does not contain all the information necessary to # revive a Functional model. This happens when the user creates # subclassed models where `get_config()` is returning # insufficient information to be considered a Functional model. # In this case, we fall back to provide all config into the # constructor of the class. try: model = cls(**config) except TypeError as e: raise TypeError( "Unable to revive model from config. When overriding " "the `get_config()`, make sure that the returned " "config contains all items used as arguments in the " f"constructor to {cls}, which is the default behavior. " "You can override this default behavior by defining a " "`from_config` method to specify how to create an " f"instance of {cls.__name__} from the config. \n\n" f"Error encountered during deserialization:\n{e}" ) if saving_lib._ENABLED: if optimizer or loss: model.compile(optimizer=optimizer, loss=loss) if input_shape: model.build(input_shape) return model 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={})`. Args: **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=json_utils.get_json_type, **kwargs ) def to_yaml(self, **kwargs): """Returns a yaml string containing the network configuration. Note: Since TF 2.6, this method is no longer supported and will raise a RuntimeError. 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. Args: **kwargs: Additional keyword arguments to be passed to `yaml.dump()`. Returns: A YAML string. Raises: RuntimeError: announces that the method poses a security risk """ raise RuntimeError( "Method `model.to_yaml()` has been removed due to security risk of " "arbitrary code execution. Please use `model.to_json()` instead." ) def reset_states(self): for layer in self.layers: if hasattr(layer, "reset_states") and getattr( layer, "stateful", False ): layer.reset_states() @property @doc_controls.do_not_generate_docs def state_updates(self): """Deprecated, do NOT use! Returns the `updates` from all layers that are stateful. This is useful for separating training updates and state updates, e.g. when we need to update a layer's internal state during prediction. Returns: A list of update ops. """ warnings.warn( "`Model.state_updates` will be removed in a future version. " "This property should not be used in TensorFlow 2.0, " "as `updates` are applied automatically.", stacklevel=2, ) state_updates = [] for layer in self.layers: if getattr(layer, "stateful", False): if hasattr(layer, "updates"): state_updates += layer.updates return state_updates @property def weights(self): """Returns the list of all layer variables/weights. Note: This will not track the weights of nested `tf.Modules` that are not themselves Keras layers. Returns: A list of variables. """ return self._dedup_weights(self._undeduplicated_weights) @property def _undeduplicated_weights(self): """Returns the undeduplicated list of all layer variables/weights.""" self._assert_weights_created() weights = [] for layer in self._self_tracked_trackables: weights += layer.variables weights += self._trainable_weights + self._non_trainable_weights return weights def summary( self, line_length=None, positions=None, print_fn=None, expand_nested=False, show_trainable=False, layer_range=None, ): """Prints a string summary of the network. Args: 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. expand_nested: Whether to expand the nested models. If not provided, defaults to `False`. show_trainable: Whether to show if a layer is trainable. If not provided, defaults to `False`. layer_range: a list or tuple of 2 strings, which is the starting layer name and ending layer name (both inclusive) indicating the range of layers to be printed in summary. It also accepts regex patterns instead of exact name. In such case, start predicate will be the first element it matches to `layer_range[0]` and the end predicate will be the last element it matches to `layer_range[1]`. By default `None` which considers all layers of model. 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 by calling " "the model on a batch of data." ) layer_utils.print_summary( self, line_length=line_length, positions=positions, print_fn=print_fn, expand_nested=expand_nested, show_trainable=show_trainable, layer_range=layer_range, ) @property def layers(self): return list(self._flatten_layers(include_self=False, recursive=False)) @layers.setter def layers(self, _): raise AttributeError( "`Model.layers` attribute is reserved and should not be used. " "Please use another name." ) 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). Args: name: String, name of layer. index: Integer, index of layer. Returns: A layer instance. """ # 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 and name is not None: raise ValueError( "Provide only a layer name or a layer index. Received: " f"index={index}, name={name}." ) if index is not None: if len(self.layers) <= index: raise ValueError( f"Was asked to retrieve layer at index {index}" f" but model only has {len(self.layers)}" " layers." ) else: return self.layers[index] if name is not None: for layer in self.layers: if layer.name == name: return layer raise ValueError( f"No such layer: {name}. Existing layers are: " f"{list(layer.name for layer in self.layers)}." ) raise ValueError( "Provide either a layer name or layer index at " "`get_layer`." ) def get_weight_paths(self): """Retrieve all the variables and their paths for the model. The variable path (string) is a stable key to indentify a `tf.Variable` instance owned by the model. It can be used to specify variable-specific configurations (e.g. DTensor, quantization) from a global view. This method returns a dict with weight object paths as keys and the corresponding `tf.Variable` instances as values. Note that if the model is a subclassed model and the weights haven't been initialized, an empty dict will be returned. Returns: A dict where keys are variable paths and values are `tf.Variable` instances. Example: ```python class SubclassModel(tf.keras.Model): def __init__(self, name=None): super().__init__(name=name) self.d1 = tf.keras.layers.Dense(10) self.d2 = tf.keras.layers.Dense(20) def call(self, inputs): x = self.d1(inputs) return self.d2(x) model = SubclassModel() model(tf.zeros((10, 10))) weight_paths = model.get_weight_paths() # weight_paths: # { # 'd1.kernel': model.d1.kernel, # 'd1.bias': model.d1.bias, # 'd2.kernel': model.d2.kernel, # 'd2.bias': model.d2.bias, # } # Functional model inputs = tf.keras.Input((10,), batch_size=10) x = tf.keras.layers.Dense(20, name='d1')(inputs) output = tf.keras.layers.Dense(30, name='d2')(x) model = tf.keras.Model(inputs, output) d1 = model.layers[1] d2 = model.layers[2] weight_paths = model.get_weight_paths() # weight_paths: # { # 'd1.kernel': d1.kernel, # 'd1.bias': d1.bias, # 'd2.kernel': d2.kernel, # 'd2.bias': d2.bias, # } ``` """ result = {} ( descendants, object_paths_dict, ) = tf.__internal__.tracking.ObjectGraphView( self ).breadth_first_traversal() for descendant in descendants: if isinstance(descendant, tf.Variable): trackable_references = object_paths_dict[descendant] object_path = ".".join([t.name for t in trackable_references]) result[object_path] = descendant return result @tf.__internal__.tracking.no_automatic_dependency_tracking def _set_save_spec(self, inputs, args=None, kwargs=None): """Defines the save spec so that serialization is able to trace model call. The TensorSpecs of the call function `inputs`, `args`, and `kwargs` are saved into a tuple of `([inputs] + args, kwargs)`. The input `TensorSpec` names are updated to match the built `input_names`. The specs can be retrieved with the `save_spec` property. Args: inputs: possibly nested inputs passed into the call function. args: a list of positional arguments passed into call. kwargs: a dictionary of keyword arguments passed into call. """ if self._saved_model_inputs_spec is not None: return # Already set. args = args or [] kwargs = kwargs or {} input_names = self.input_names if not input_names: input_names = compile_utils.create_pseudo_input_names(inputs) flat_inputs = tf.nest.flatten(inputs) inputs_spec = [] for name, tensor in zip(input_names, flat_inputs): inputs_spec.append( tf_utils.get_tensor_spec(tensor, dynamic_batch=False, name=name) ) inputs_spec = tf.nest.pack_sequence_as(inputs, inputs_spec) super()._set_save_spec(inputs_spec, args, kwargs) # Store the input shapes if ( self.__class__.__name__ == "Sequential" and self._build_input_shape is None ): self._build_input_shape = tf.nest.map_structure( lambda x: None if x is None else x.shape, inputs_spec ) def save_spec(self, dynamic_batch=True): """Returns the `tf.TensorSpec` of call inputs as a tuple `(args, kwargs)`. This value is automatically defined after calling the model for the first time. Afterwards, you can use it when exporting the model for serving: ```python model = tf.keras.Model(...) @tf.function def serve(*args, **kwargs): outputs = model(*args, **kwargs) # Apply postprocessing steps, or add additional outputs. ... return outputs # arg_specs is `[tf.TensorSpec(...), ...]`. kwarg_specs, in this # example, is an empty dict since functional models do not use keyword # arguments. arg_specs, kwarg_specs = model.save_spec() model.save(path, signatures={ 'serving_default': serve.get_concrete_function(*arg_specs, **kwarg_specs) }) ``` Args: dynamic_batch: Whether to set the batch sizes of all the returned `tf.TensorSpec` to `None`. (Note that when defining functional or Sequential models with `tf.keras.Input([...], batch_size=X)`, the batch size will always be preserved). Defaults to `True`. Returns: If the model inputs are defined, returns a tuple `(args, kwargs)`. All elements in `args` and `kwargs` are `tf.TensorSpec`. If the model inputs are not defined, returns `None`. The model inputs are automatically set when calling the model, `model.fit`, `model.evaluate` or `model.predict`. """ return self._get_save_spec(dynamic_batch, inputs_only=False) def _assert_weights_created(self): """Asserts that all the weights for the model have been created. For a non-dynamic model, the weights must already be created after the layer has been called. For a dynamic model, the exact list of weights can never be known for certain since it may change at any time during execution. We run this check right before accessing weights or getting the Numpy value for the current weights. Otherwise, if the layer has never been called, the user would just get an empty list, which is misleading. Raises: ValueError: if the weights of the network have not yet been created. """ if self.dynamic: return if ( "build" in self.__class__.__dict__ and self.__class__ != Model and not self.built ): # For any model that has customized build() method but hasn't been # invoked yet, this will cover both sequential and subclass model. # Also make sure to exclude Model class itself which has build() # defined. raise ValueError( f"Weights for model {self.name} have not yet been " "created. " "Weights are created when the Model is first called on " "inputs or `build()` is called with an `input_shape`." ) def _check_call_args(self, method_name): """Check that `call()` has only one positional arg.""" # Always allow first arg, regardless of arg name. fullargspec = self._call_spec.full_argspec if fullargspec.defaults: positional_args = fullargspec.args[: -len(fullargspec.defaults)] else: positional_args = fullargspec.args if "training" in positional_args: positional_args.remove("training") # self and first arg can be positional. if len(positional_args) > 2: extra_args = positional_args[2:] raise ValueError( f"Models passed to `{method_name}` can only have `training` " "and the first argument in `call()` as positional arguments, " f"found: {extra_args}." ) def _validate_compile(self, optimizer, metrics, **kwargs): """Performs validation checks for the default `compile()`.""" if any( isinstance(opt, optimizer_v1.Optimizer) for opt in tf.nest.flatten(optimizer) ): raise ValueError( f"`tf.compat.v1.keras` Optimizer ({optimizer}) is " "not supported when eager execution is enabled. Use a " "`tf.keras` Optimizer instead, or disable eager " "execution." ) kwargs.pop("cloning", None) # Legacy DistStrat argument, never used. kwargs.pop("experimental_run_tf_function", None) # Always `True`. distribute_arg = kwargs.pop("distribute", None) if distribute_arg is not None: raise ValueError( "`distribute` argument in compile is not available in TF 2.0. " "Please create the model under the `strategy.scope()`. " f"Received: {distribute_arg}." ) target_tensor_arg = kwargs.pop("target_tensors", None) if target_tensor_arg is not None: raise ValueError( "`target_tensors` argument is not supported when executing " f"eagerly. Received: {target_tensor_arg}." ) invalid_kwargs = set(kwargs) - {"sample_weight_mode"} if invalid_kwargs: raise TypeError( "Invalid keyword argument(s) in `compile()`: " f"{(invalid_kwargs,)}. Valid keyword arguments include " '"cloning", "experimental_run_tf_function", "distribute",' ' "target_tensors", or "sample_weight_mode".' ) # Model must be created and compiled with the same DistStrat. if self.built and tf.distribute.has_strategy(): strategy = tf.distribute.get_strategy() for v in self.variables: if not strategy.extended.variable_created_in_scope(v): raise ValueError( f"Variable ({v}) was not created in the distribution " f"strategy scope of ({strategy}). It is most likely " "because some layers, model, or optimizer was being " "created outside the distribution strategy scope. Try " "to make sure your code looks similar " "to the following.\nwith strategy.scope():\n" " model=_create_model()\n" " model.compile(...)" ) # Model metrics must be created in the same distribution strategy scope # as the model. strategy = self.distribute_strategy for metric in tf.nest.flatten(metrics): for v in getattr(metric, "variables", []): if not strategy.extended.variable_created_in_scope(v): raise ValueError( f"Metric ({metric}) passed to `model.compile` was " "created inside a different distribution strategy " "scope than the model. All metrics must be created " "in the same distribution strategy " f"scope as the model (in this case {strategy}). " "If you pass in a string identifier for a metric to " "compile, the metric will automatically be created " "in the correct distribution strategy scope." ) # Model metrics must be created in the same distribution strategy scope # as the model. for opt in tf.nest.flatten(optimizer): for v in getattr(opt, "_weights", []): if not strategy.extended.variable_created_in_scope(v): raise ValueError( f"Optimizer ({optimizer}) passed to `model.compile` " "was created inside a different distribution strategy " "scope than the model. All optimizers must be created " "in the same distribution strategy scope as the model " f"(in this case {strategy}). If you pass in a string " "identifier for an optimizer to compile, the optimizer " "will automatically be created in the correct " "distribution strategy scope." ) def _maybe_load_initial_counters_from_ckpt( self, steps_per_epoch, initial_epoch ): """Maybe load initial epoch from ckpt considering possible worker recovery. Refer to tensorflow/python/keras/distribute/worker_training_state.py for more information. Args: steps_per_epoch: The number of step per epoch. initial_epoch: The original initial_epoch user passes in `fit()`. mode: The mode for running `model.fit()`. Returns: If the training is recovering from previous failure under multi-worker training setting, return the (epoch, step) the training is supposed to continue at. Otherwise, return the `initial_epoch, initial_step` the user passes in. """ initial_step = 0 if self._training_state is not None: return self._training_state.maybe_load_initial_counters_from_ckpt( steps_per_epoch, initial_epoch, mode=ModeKeys.TRAIN ) return (initial_epoch, initial_step) def _maybe_load_initial_step_from_ckpt(self): if getattr(self, "_callback_step", 0) > 0: return self._callback_step.numpy() + 1 return 0 def _assert_compile_was_called(self): # Checks whether `compile` has been called. If it has been called, # then the optimizer is set. This is different from whether the # model is compiled # (i.e. whether the model is built and its inputs/outputs are set). if not self._is_compiled: raise RuntimeError( "You must compile your model before " "training/testing. " "Use `model.compile(optimizer, loss)`." ) def _check_sample_weight_warning(self, x, sample_weight): # Datasets can include sample weight, by returning a tuple with the # structure of `(x, y, sample_weight)`. sample_weight_present = sample_weight is not None or ( isinstance(x, tf.data.Dataset) and isinstance(x.element_spec, tuple) and len(x.element_spec) == 3 ) if ( sample_weight_present and self.compiled_metrics._user_weighted_metrics is None ): logging.warning( "`evaluate()` received a value for `sample_weight`, but " "`weighted_metrics` were not provided. Did you mean to pass " "metrics to `weighted_metrics` in `compile()`? If this is " "intentional you can pass `weighted_metrics=[]` to `compile()` " "in order to silence this warning." ) def _set_inputs(self, inputs, outputs=None, training=None): """This method is for compat with Modelv1. Only inputs are needed here.""" self._set_save_spec(inputs) @property def _trackable_saved_model_saver(self): return model_serialization.ModelSavedModelSaver(self) def _trackable_children(self, save_type="checkpoint", **kwargs): if save_type == "savedmodel": # SavedModel needs to ignore the execution functions. train_function = self.train_function test_function = self.test_function predict_function = self.predict_function train_tf_function = self.train_tf_function self.train_function = None self.test_function = None self.predict_function = None self.train_tf_function = None children = super()._trackable_children(save_type, **kwargs) if save_type == "savedmodel": self.train_function = train_function self.test_function = test_function self.predict_function = predict_function self.train_tf_function = train_tf_function return children def _should_eval(self, epoch, validation_freq): epoch = epoch + 1 # one-index the user-facing epoch. if isinstance(validation_freq, int): return epoch % validation_freq == 0 elif isinstance(validation_freq, list): return epoch in validation_freq else: raise ValueError( "Expected `validation_freq` to be a list or int. " f"Received: validation_freq={validation_freq} of the " f"type {type(validation_freq)}." ) ###################################################################### # Functions below exist only as v1 / v2 compatibility shims. ###################################################################### def _get_compile_args(self, user_metrics=True): """Used for saving or cloning a Model. Args: user_metrics: Whether to return user-supplied metrics or `Metric` objects. Defaults to returning the user-supplied metrics. Returns: Dictionary of arguments that were used when compiling the model. """ self._assert_compile_was_called() saved_metrics = self.compiled_metrics._user_metrics saved_weighted_metrics = self.compiled_metrics._user_weighted_metrics if not user_metrics: if saved_metrics is not None: saved_metrics = self.compiled_metrics._metrics if saved_weighted_metrics is not None: saved_weighted_metrics = self.compiled_metrics._weighted_metrics compile_args = { "optimizer": self.optimizer, "loss": self.compiled_loss._user_losses, "metrics": saved_metrics, "weighted_metrics": saved_weighted_metrics, "loss_weights": self.compiled_loss._user_loss_weights, } return compile_args def _get_callback_model(self): return self def _in_multi_worker_mode(self): return self.distribute_strategy.extended._in_multi_worker_mode() @property def _compile_was_called(self): return self._is_compiled def _save_new(self, dirpath): return saving_lib.save(self, dirpath) def reduce_per_replica(values, strategy, reduction="first"): """Attempt to reduce the structure `values` to single values. Given `values` (a `tf.Tensor` or a `PerReplica` structure), which represents the values across all the replicas, `reduce_per_replica` attempts to "reduce" those values and returns the corresponding structure that represents only single values. Currently, `reduce_per_replica` is only used for reducing the metric results from `tf.distribute.Strategy.run()`. Depending on the underlying `Strategy` implementation, `values` may be a `PerReplica` object, which can be thought of as a collection of values across the replicas, or a `tf.Tensor`, if the strategy has already conducted the reduction for the downstream library. There are three possible outcomes of reduction: 1) if the `values` is a structure of simple `tf.Tensor`s, meaning that reduction is not actually needed, `reduce_per_replica` returns the structure as-is. 2) else, if `reduction="first"`, then `reduce_per_replica` returns the values of the first replica. This is used in the case of training and evaluation, where `values` is expected to hold the same value across the replicas as a result of `Strategy`'s synchronization across the replicas. `reduce_per_replica` does not synchronize the values. 3) else, if `reduction="concat"`, then `reduce_per_replica` returns the concatenation of the values across the replicas, along the axis of dimension 0. This is used in the inference case (`predict()`). Args: values: Structure of `PerReplica` objects or `tf.Tensor`s. `tf.Tensor`s are returned as-is. strategy: `tf.distribute.Strategy` object. reduction: One of `"first"`, `"concat"`. Returns: Structure of `Tensor`s, representing the result of reduction. Raises: ValueError: if the reduction method is not supported. """ def _reduce(v): """Reduce a single `PerReplica` object.""" if reduction == "concat" and _collective_all_reduce_multi_worker( strategy ): return _multi_worker_concat(v, strategy) if not _is_per_replica_instance(v): return v elif reduction == "first": return strategy.experimental_local_results(v)[0] elif reduction == "concat": if _is_tpu_multi_host(strategy): return _tpu_multi_host_concat(v, strategy) else: return concat(strategy.experimental_local_results(v)) else: raise ValueError( '`reduction` must be "first" or "concat". Received: ' f"reduction={reduction}." ) return tf.nest.map_structure(_reduce, values) def concat(tensors, axis=0): """Concats `tensor`s along `axis`.""" if isinstance(tensors[0], tf.SparseTensor): return tf.sparse.concat(axis=axis, sp_inputs=tensors) return tf.concat(tensors, axis=axis) def potentially_ragged_concat(tensors): """Concats `Tensor`s along their first dimension. Args: tensors: List of `Tensor`s. Returns: Concatenation of the inputs along the first dimension -- of type `Tensor` if all input shapes are compatible, or `RaggedTensor` if not. """ if len(tensors) == 1: return tensors[0] if isinstance(tensors[0], tf.SparseTensor): return tf.sparse.concat(axis=0, sp_inputs=tensors) elif isinstance(tensors[0], tf.RaggedTensor): return tf.concat(tensors, axis=0) elif not tf.__internal__.tf2.enabled(): return tf.concat(tensors, axis=0) non_batch_shapes = tf.stack([tf.shape(tensor)[1:] for tensor in tensors]) constant_dims = tf.math.reduce_all( non_batch_shapes == non_batch_shapes[:1], axis=0 ) if tf.math.reduce_all(constant_dims).numpy().item(): # All non-batch dims are constant return tf.concat(tensors, axis=0) # First, identify constant inner dimensions by finding the # rightmost dimension that is not constant constant_inner_dimensions = ( constant_dims.numpy().tolist()[::-1].index(False) ) # If there are constant inner dimensions, define a constant inner shape if constant_inner_dimensions == 0: constant_inner_shape = None else: constant_inner_shape = tensors[0].shape[-constant_inner_dimensions:] return tf.ragged.constant( [tensor.numpy() for tensor in tensors], inner_shape=constant_inner_shape ).merge_dims(0, 1) def _get_verbosity(verbose, distribute_strategy): """Find the right verbosity value for 'auto'.""" if verbose == 1 and distribute_strategy._should_use_with_coordinator: raise ValueError( "`verbose=1` is not allowed with `ParameterServerStrategy` for " f"performance reasons. Received: verbose={verbose}" ) if verbose == "auto": if ( distribute_strategy._should_use_with_coordinator or not io_utils.is_interactive_logging_enabled() ): # Default to epoch-level logging for PSStrategy or using absl # logging. return 2 else: return 1 # Default to batch-level logging otherwise. return verbose def _is_tpu_multi_host(strategy): return backend.is_tpu_strategy(strategy) and strategy.extended.num_hosts > 1 def _tpu_multi_host_concat(v, strategy): """Correctly order TPU PerReplica objects.""" replicas = strategy.experimental_local_results(v) # When distributed datasets are created from Tensors / NumPy, # TPUStrategy.experimental_distribute_dataset shards data in # (Replica, Host) order, and TPUStrategy.experimental_local_results returns # it in (Host, Replica) order. # TODO(b/150317897): Figure out long-term plan here. num_replicas_per_host = strategy.extended.num_replicas_per_host ordered_replicas = [] for replica_id in range(num_replicas_per_host): ordered_replicas += replicas[replica_id::num_replicas_per_host] return concat(ordered_replicas) def _collective_all_reduce_multi_worker(strategy): return ( isinstance(strategy, tf.distribute.MultiWorkerMirroredStrategy) ) and strategy.extended._in_multi_worker_mode() # TODO(wxinyi): merge this with _tpu_multi_host_concat once we have all_gather # for all strategies def _multi_worker_concat(v, strategy): """Order PerReplica objects for CollectiveAllReduceStrategy and concat.""" replicas = strategy.gather(v, axis=0) # v might not have the same shape on different replicas if _is_per_replica_instance(v): shapes = tf.concat( [ tf.expand_dims(tf.shape(single_value)[0], axis=0) for single_value in v.values ], axis=0, ) all_shapes = strategy.gather(shapes, axis=0) else: # v is a tensor. This may happen when, say, we have 2x1 multi-worker. all_shapes = strategy.gather( tf.expand_dims(tf.shape(v)[0], axis=0), axis=0 ) replicas = tf.split( replicas, num_or_size_splits=all_shapes, num=strategy.num_replicas_in_sync, ) ordered_replicas = [] num_replicas_per_worker = len(strategy.extended.worker_devices) for replica_id in range(num_replicas_per_worker): ordered_replicas += replicas[replica_id::num_replicas_per_worker] return concat(ordered_replicas) def _is_scalar(x): return isinstance(x, (tf.Tensor, tf.Variable)) and x.shape.rank == 0 def _minimum_control_deps(outputs): """Returns the minimum control dependencies to ensure step succeeded.""" if tf.executing_eagerly(): return [] # Control dependencies not needed. outputs = tf.nest.flatten(outputs, expand_composites=True) for out in outputs: # Variables can't be control dependencies. if not isinstance(out, tf.Variable): return [out] # Return first Tensor or Op from outputs. return [] # No viable Tensor or Op to use for control deps. def _disallow_inside_tf_function(method_name): if tf.inside_function(): error_msg = ( "Detected a call to `Model.{method_name}` inside a `tf.function`. " "`Model.{method_name} is a high-level endpoint that manages its " "own `tf.function`. Please move the call to `Model.{method_name}` " "outside of all enclosing `tf.function`s. Note that you can call a " "`Model` directly on `Tensor`s inside a `tf.function` like: " "`model(x)`." ).format(method_name=method_name) raise RuntimeError(error_msg) def _detect_save_format(filepath): """Returns path to weights file and save format.""" filepath = io_utils.path_to_string(filepath) if saving_utils.is_hdf5_filepath(filepath): return filepath, "h5" # Filepath could be a TensorFlow checkpoint file prefix or SavedModel # directory. It's possible for filepath to be both a prefix and directory. # Prioritize checkpoint over SavedModel. if _is_readable_tf_checkpoint(filepath): save_format = "tf" elif tf.saved_model.contains_saved_model(filepath): ckpt_path = os.path.join( filepath, tf.saved_model.VARIABLES_DIRECTORY, tf.saved_model.VARIABLES_FILENAME, ) if _is_readable_tf_checkpoint(ckpt_path): filepath = ckpt_path save_format = "tf" else: raise ValueError( "Unable to load weights. filepath {} appears to be a " "SavedModel directory, but checkpoint either doesn't " "exist, or is incorrectly formatted.".format(filepath) ) else: # Not a TensorFlow checkpoint. This filepath is likely an H5 file that # doesn't have the hdf5/keras extensions. save_format = "h5" return filepath, save_format def _is_readable_tf_checkpoint(filepath): try: tf.compat.v1.train.NewCheckpointReader(filepath) return True except tf.errors.DataLossError: # The checkpoint is not readable in TensorFlow format. return False def flatten_metrics_in_order(logs, metrics_names): """Turns the `logs` dict into a list as per key order of `metrics_names`.""" results = [] for name in metrics_names: if name in logs: results.append(logs[name]) for key in sorted(logs.keys()): if key not in metrics_names: results.append(logs[key]) if len(results) == 1: return results[0] return results def _is_per_replica_instance(obj): return isinstance(obj, tf.distribute.DistributedValues) and isinstance( obj, tf.__internal__.CompositeTensor ) def disable_multi_worker(method): """Decorator that disallows multi-worker use of `method`.""" def _method_wrapper(self, *args, **kwargs): if self._in_multi_worker_mode(): raise ValueError( f"{method.__name__} is not supported in multi-worker " "mode. Please use a non-multi-worker " "`tf.distribute.Strategy` such as " "`tf.distribute.MirroredStrategy`." ) return method(self, *args, **kwargs) return tf.__internal__.decorator.make_decorator( target=method, decorator_func=_method_wrapper ) def inject_functional_model_class(cls): """Inject `Functional` into the hierarchy of this class if needed.""" from keras.engine import functional from keras.engine import training_v1 if cls == Model or cls == training_v1.Model: return functional.Functional # In case there is any multiple inheritance, we stop injecting the # class if keras model is not in its class hierarchy. if cls == object: return object cls.__bases__ = tuple( inject_functional_model_class(base) for base in cls.__bases__ ) # Trigger any `__new__` class swapping that needed to happen on `Functional` # but did not because functional was not in the class hierarchy. cls.__new__(cls) return cls def is_functional_model_init_params(args, kwargs): return ( len(args) == 2 or len(args) == 1 and "outputs" in kwargs or "inputs" in kwargs and "outputs" in kwargs )