<NME> training.py <BEF> # 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 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 pickle_utils from keras.saving import saving_api from keras.saving.experimental import saving_lib from keras.saving.legacy import serialization from keras.saving.legacy.saved_model import json_utils from keras.saving.legacy.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): """A model grouping layers into an object with training/inference features. Args: inputs: The input(s) of the model: a `keras.Input` object or a combination of `keras.Input` objects in a dict, list or tuple. outputs: The output(s) of the model: a tensor that originated from `keras.Input` objects or a combination of such tensors in a dict, list or tuple. 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._distribute_reduction_method = 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 shapes of the targets and of the model output. 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. 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) self._compile_config = generic_utils.Config( optimizer=optimizer, loss=loss, metrics=metrics, loss_weights=loss_weights, weighted_metrics=weighted_metrics, run_eagerly=run_eagerly, steps_per_execution=steps_per_execution, jit_compile=jit_compile, ) 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): """Return metrics added using `compile()` or `add_metric()`. 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 @property def jit_compile(self): """Specify whether to compile the model with XLA. [XLA](https://www.tensorflow.org/xla) is an optimizing compiler for machine learning. `jit_compile` is not enabled by default. 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. """ return self._jit_compile @jit_compile.setter def jit_compile(self, value): # Function remains cached with previous jit_compile settings if self._jit_compile == value: # Avoid reseting compiler cache if possible if the value is the same return self._jit_compile = value # Setting `jit_compile` should invalidate previously cached functions. self._reset_compile_cache() @property def distribute_reduction_method(self): """The method employed to reduce per-replica values during training. Unless specified, the value "auto" will be assumed, indicating that the reduction strategy should be chosen based on the current running environment. See `reduce_per_replica` function for more details. """ return self._distribute_reduction_method or "auto" @distribute_reduction_method.setter def distribute_reduction_method(self, value): self._distribute_reduction_method = 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) return self.get_metrics_result() def get_metrics_result(self): """Returns the model's metrics values as a dict. If any of the metric result is a dict (containing multiple metrics), each of them gets added to the top level returned dict of this method. Returns: A `dict` containing values of the metrics listed in `self.metrics`. Example: `{'loss': 0.2, 'accuracy': 0.7}`. """ # 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 _validate_and_get_metrics_result(self, logs): """Returns model metrics as a dict if the keys match with input logs. When the training / evalution is performed with asynchronous steps, such as the case with `tf.distribute.ParameterServerStrategy`, the last scheduled `train / test_step` may not give the latest metrics because it is not guaranteed to be executed the last. This method gets metrics from the model directly instead of relying on the return from last step function. It logs a warning if the metric results could not be overridden when used with `tf.distribute.ParameterServerStrategy`. When the user has custom train / test step functions, the metrics returned may be different from `Model.metrics`. In those instances, this function will be no-op and return the logs. Args: logs: A `dict` of metrics returned by train / test step function. Returns: A `dict` containing values of the metrics listed in `self.metrics` when logs and model metrics keys match. Otherwise it returns input `logs`. """ PSS_WARN_MSG = "Could not get Model metric results. \ Using the results of last step function could lead to incorrect \ results when used with ParameterServerStrategy" try: metric_logs = self.get_metrics_result() except TypeError: if self._cluster_coordinator: logging.warning(PSS_WARN_MSG) else: # Verify that train / test step logs passed and metric logs have # matching keys. Could be different when using custom step functions if isinstance(logs, dict) and set(logs.keys()) == set( metric_logs.keys() ): logs = tf_utils.sync_to_numpy_or_python_type(metric_logs) elif self._cluster_coordinator: logging.warning(PSS_WARN_MSG) return logs 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=self.distribute_reduction_method, ) 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 str(x[0].shape[0]) + ' samples') return x, y, sample_weights def fit(self, x=None, y=None, batch_size=32, 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 (dataset iterations). 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. f = self.train_function # Prepare display labels. out_labels = self.metrics_names # Rename duplicated metrics name # (can happen with an output layer shared among multiple dataflows). deduped_out_labels = [] for i, label in enumerate(out_labels): new_label = label if out_labels.count(label) > 1: dup_idx = out_labels[:i].count(label) new_label += '_' + str(dup_idx + 1) deduped_out_labels.append(new_label) out_labels = deduped_out_labels if do_validation: callback_metrics = copy.copy(out_labels) + ['val_' + n for n in out_labels] 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(): 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`." ) # Override with model metrics instead of last step logs logs = self._validate_and_get_metrics_result(logs) 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) and epochs > 0 ): 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 'you must specify a value for ' '`validation_steps`.') out_labels = self.metrics_names callback_metrics = out_labels + ['val_' + n for n in out_labels] # prepare callbacks 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=self.distribute_reduction_method, ) 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
{'addition_count': 17, 'commit_subject': 'Fix Model.fit_generator for multiple outputs with same name. (#6239)', 'deletion_count': 13, 'file_extension': '.py', 'lang': 'py', 'license': 'apache-2.0', 'repo_name': 'keras-team/keras'}