# 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. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import weakref import numpy as np from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.ops import iterator_ops from tensorflow.python.eager import context from tensorflow.python.framework import errors from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_util from tensorflow.python.keras import backend as K from tensorflow.python.keras import losses from tensorflow.python.keras import metrics as metrics_module from tensorflow.python.keras import optimizers from tensorflow.python.keras.engine import base_layer from tensorflow.python.keras.engine import distributed_training_utils from tensorflow.python.keras.engine import training_arrays from tensorflow.python.keras.engine import training_distributed from tensorflow.python.keras.engine import training_eager from tensorflow.python.keras.engine import training_generator from tensorflow.python.keras.engine import training_utils from tensorflow.python.keras.engine.network import Network from tensorflow.python.keras.utils import data_utils from tensorflow.python.keras.utils.generic_utils import slice_arrays from tensorflow.python.ops import math_ops from tensorflow.python.ops import weights_broadcast_ops from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import optimizer as tf_optimizer_module from tensorflow.python.training.checkpointable import base as checkpointable from tensorflow.python.util import nest from tensorflow.python.util.tf_export import tf_export @tf_export('keras.models.Model', 'keras.Model') class Model(Network): """`Model` groups layers into an object with training and inference features. 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) ``` 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(MyModel, self).__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(MyModel, self).__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() ``` """ def __init__(self, *args, **kwargs): super(Model, self).__init__(*args, **kwargs) # Create a cache for iterator get_next op. self._iterator_get_next = weakref.WeakKeyDictionary() # Create a cache for dataset - uninitialized iterators self._dataset_iterator_cache = weakref.WeakKeyDictionary() # initializing _distribution_strategy here since it is possible to call # predict on a model without compiling it. self._distribution_strategy = None def _set_sample_weight_attributes(self, sample_weight_mode, skip_target_weighing_indices): """Sets sample weight related attributes on the model.""" sample_weights, sample_weight_modes = training_utils.prepare_sample_weights( self.output_names, sample_weight_mode, skip_target_weighing_indices) self.sample_weights = sample_weights self.sample_weight_modes = sample_weight_modes self._feed_sample_weight_modes = [ sample_weight_modes[i] for i in range(len(self.outputs)) if i not in skip_target_weighing_indices ] self._feed_sample_weights = [ sample_weights[i] for i in range(len(sample_weights)) if i not in skip_target_weighing_indices ] def _cache_output_metric_attributes(self, metrics, weighted_metrics): """Caches metric name and function attributes for every model output.""" output_shapes = [ None if output is None else output.get_shape().as_list() for output in self.outputs ] self._per_output_metrics = training_utils.collect_per_output_metric_info( metrics, self.output_names, output_shapes, self.loss_functions) self._per_output_weighted_metrics = \ training_utils.collect_per_output_metric_info( weighted_metrics, self.output_names, output_shapes, self.loss_functions, self.sample_weights) def _add_unique_metric_name(self, metric_name, output_index): """Makes the metric name unique and adds it to the model's metric name list. If there are multiple outputs for which the metrics are calculated, the metric names have to be made unique by appending an integer. Arguments: metric_name: Metric name that corresponds to the metric specified by the user. For example: 'acc'. output_index: The index of the model output for which the metric name is being added. Returns: string, name of the model's unique metric name """ if len(self.output_names) > 1: metric_name = '%s_%s' % (self.output_names[output_index], metric_name) j = 1 base_metric_name = metric_name while metric_name in self.metrics_names: metric_name = '%s_%d' % (base_metric_name, j) j += 1 return metric_name def _init_metric_attributes(self): """Initialized model metric attributes.""" self.metrics_names = ['loss'] self.metrics_tensors = [] self.metrics_updates = [] self.stateful_metric_names = [] self.stateful_metric_functions = [] def _set_per_output_metric_attributes(self, metrics_dict, output_index): """Sets the metric attributes on the model for the given output. Arguments: metrics_dict: A dict with metric names as keys and metric fns as values. output_index: The index of the model output for which the metric attributes are added. """ for metric_name, metric_fn in metrics_dict.items(): metric_name = self._add_unique_metric_name(metric_name, output_index) # Keep track of metric name. self.metrics_names.append(metric_name) # Keep track of stateful metric attributes (name and metric function). if isinstance(metric_fn, base_layer.Layer) and metric_fn.stateful: self.stateful_metric_names.append(metric_name) self.stateful_metric_functions.append(metric_fn) def _set_metric_attributes(self, outputs, skip_target_indices=None): """Sets the metric attributes on the model for all the model outputs.""" skip_target_indices = skip_target_indices or [] for i in range(len(outputs)): if i in skip_target_indices: continue self._set_per_output_metric_attributes(self._per_output_metrics[i], i) self._set_per_output_metric_attributes( self._per_output_weighted_metrics[i], i) def _handle_per_output_metrics(self, metrics_dict, y_true, y_pred, mask, weights=None): """Calls metric functions for a single output. Arguments: metrics_dict: A dict with metric names as keys and metric fns as values. y_true: Target output. y_pred: Predicted output. mask: Computed mask value for the current output. weights: Weights to be applied on the current output. Returns: A list of metric result tensors. """ metric_results = [] for metric_name, metric_fn in metrics_dict.items(): with K.name_scope(metric_name): if isinstance(metric_fn, metrics_module.Metric): # Call the stateful metric function. if mask is not None: mask = math_ops.cast(mask, y_pred.dtype) # Update weights with mask. if weights is None: weights = mask else: # Update shape of weights if possible before adding mask. # Update dimensions of weights to match with mask if possible. mask, _, weights = metrics_module.squeeze_or_expand_dimensions( mask, None, weights) try: # Broadcast weights if possible. weights = weights_broadcast_ops.broadcast_weights(weights, mask) except ValueError: pass # TODO(psv): Handle case when mask and weight shapes are not # compatible. weights *= mask metric_result = metric_fn(y_true, y_pred, weights) else: # Call the stateless metric function. weighted_metric_fn = training_utils.weighted_masked_objective( metric_fn) metric_result = weighted_metric_fn( y_true, y_pred, weights=weights, mask=mask) if not context.executing_eagerly(): # Keep track of metric result tensor. self.metrics_tensors.append(metric_result) metric_results.append(metric_result) is_stateful = isinstance(metric_fn, base_layer.Layer) and metric_fn.stateful if is_stateful and not context.executing_eagerly(): # Keep track of updates created by stateful metrics. self.metrics_updates += metric_fn.updates return metric_results def _handle_metrics(self, outputs, skip_target_indices=None, targets=None, sample_weights=None, masks=None): """Handles calling metric functions. Arguments: outputs: List of outputs (predictions). skip_target_indices: Optional. List of target ids to skip. targets: List of targets. sample_weights: Optional list of sample weight arrays. masks: List of computed output mask values. Returns: A list of metric result tensors. """ skip_target_indices = skip_target_indices or [] metric_results = [] with K.name_scope('metrics'): for i in range(len(outputs)): if i in skip_target_indices: continue output = outputs[i] if outputs else None target = targets[i] if targets else None output_mask = masks[i] if masks else None metric_results.extend( self._handle_per_output_metrics(self._per_output_metrics[i], target, output, output_mask)) metric_results.extend( self._handle_per_output_metrics( self._per_output_weighted_metrics[i], target, output, output_mask, weights=sample_weights[i])) return metric_results @checkpointable.no_automatic_dependency_tracking def compile(self, optimizer, loss=None, metrics=None, loss_weights=None, sample_weight_mode=None, weighted_metrics=None, target_tensors=None, distribute=None, **kwargs): """Configures the model for training. Arguments: optimizer: String (name of optimizer) or optimizer instance. See [optimizers](/api_docs/python/tf/keras/optimizers). loss: String (name of objective function) or objective function. See [losses](/api_docs/python/tf/losses). 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. metrics: List of metrics to be evaluated by the model during training and testing. Typically you will use `metrics=['accuracy']`. To specify different metrics for different outputs of a multi-output model, you could also pass a dictionary, such as `metrics={'output_a': 'accuracy'}`. 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 tensor, it is expected to map output names (strings) to scalar coefficients. sample_weight_mode: If you need to do timestep-wise sample weighting (2D weights), set this to `"temporal"`. `None` defaults to sample-wise weights (1D). If the model has multiple outputs, you can use a different `sample_weight_mode` on each output by passing a dictionary or a list of modes. weighted_metrics: List of metrics to be evaluated and weighted by sample_weight or class_weight during training and testing. target_tensors: By default, Keras will create placeholders for the model's target, which will be fed with the target data during training. If instead you would like to use your own target tensors (in turn, Keras will not expect external Numpy data for these targets at training time), you can specify them via the `target_tensors` argument. It can be a single tensor (for a single-output model), a list of tensors, or a dict mapping output names to target tensors. distribute: The DistributionStrategy instance that we want to use to distribute the training of the model. **kwargs: These arguments are passed to `tf.Session.run`. Raises: ValueError: In case of invalid arguments for `optimizer`, `loss`, `metrics` or `sample_weight_mode`. """ # Validate that arguments passed by the user to `compile` are supported by # DistributionStrategy. if distribute: if not isinstance( optimizer, (tf_optimizer_module.Optimizer, optimizers.TFOptimizer)): raise NotImplementedError( 'optimizer must be an instance of ' 'tf.train.Optimizer, not a %s' % type(optimizer)) if context.executing_eagerly(): raise NotImplementedError('DistributionStrategy is not supported ' 'when eager execution is enabled.') if sample_weight_mode: raise NotImplementedError('sample_weight_mode is not supported with ' 'DistributionStrategy.') if weighted_metrics: raise NotImplementedError('weighted_metrics is not supported with ' 'DistributionStrategy.') if target_tensors: raise ValueError('target_tensors is not supported with ' 'DistributionStrategy.') loss = loss or {} if context.executing_eagerly() and not isinstance( optimizer, (tf_optimizer_module.Optimizer, optimizers.TFOptimizer)): raise ValueError( 'optimizer must be an instance of tf.train.Optimizer, not ' 'a %s' % type(optimizer)) self.optimizer = optimizers.get(optimizer) # We've disabled automatic dependency tracking for this method, but do want # to add a checkpoint dependency on the optimizer if it's checkpointable. if isinstance(self.optimizer, checkpointable.CheckpointableBase): self._track_checkpointable( self.optimizer, name='optimizer', overwrite=True) self.loss = loss self.metrics = metrics or [] self.loss_weights = loss_weights self.sample_weight_mode = sample_weight_mode self.weighted_metrics = weighted_metrics if context.executing_eagerly() and target_tensors is not None: raise ValueError('target_tensors is not supported in Eager mode.') self.target_tensors = target_tensors # Set DistributionStrategy specific parameters. self._distribution_strategy = distribute # Reset the value of grouped_model self._grouped_model = None if self._distribution_strategy is not None: distributed_training_utils.configure_and_create_session( self._distribution_strategy) if not self.built: # Model is not compilable because it does not know its number of inputs # and outputs, nor their shapes and names. We will compile after the first # time the model gets called on training data. return self._is_compiled = True # Prepare loss functions. if isinstance(loss, dict): for name in loss: if name not in self.output_names: raise ValueError( 'Unknown entry in loss ' 'dictionary: "' + name + '". ' 'Only expected the following keys: ' + str(self.output_names)) loss_functions = [] for name in self.output_names: if name not in loss: logging.warning( 'Output "' + name + '" missing from loss dictionary. We assume ' 'this was done on purpose. The fit and evaluate APIs will not be ' 'expecting any data to be passed to "' + name + '".') loss_functions.append(losses.get(loss.get(name))) elif isinstance(loss, list): if len(loss) != len(self.outputs): raise ValueError('When passing a list as loss, ' 'it should have one entry per model outputs. ' 'The model has ' + str(len(self.outputs)) + ' outputs, but you passed loss=' + str(loss)) loss_functions = [losses.get(l) for l in loss] else: loss_function = losses.get(loss) loss_functions = [loss_function for _ in range(len(self.outputs))] self.loss_functions = loss_functions weighted_losses = [training_utils.weighted_masked_objective(fn) for fn in loss_functions] skip_target_indices = [] skip_target_weighing_indices = [] self._feed_outputs = [] self._feed_output_names = [] self._feed_output_shapes = [] self._feed_loss_fns = [] for i in range(len(weighted_losses)): if weighted_losses[i] is None: skip_target_indices.append(i) skip_target_weighing_indices.append(i) # Prepare output masks. if not context.executing_eagerly(): masks = [getattr(x, '_keras_mask', None) for x in self.outputs] if not isinstance(masks, list): masks = [masks] # Prepare loss weights. if loss_weights is None: loss_weights_list = [1. for _ in range(len(self.outputs))] elif isinstance(loss_weights, dict): for name in loss_weights: if name not in self.output_names: raise ValueError( 'Unknown entry in loss_weights ' 'dictionary: "' + name + '". ' 'Only expected the following keys: ' + str(self.output_names)) loss_weights_list = [] for name in self.output_names: loss_weights_list.append(loss_weights.get(name, 1.)) elif isinstance(loss_weights, list): if len(loss_weights) != len(self.outputs): raise ValueError( 'When passing a list as loss_weights, ' 'it should have one entry per model output. ' 'The model has ' + str(len(self.outputs)) + ' outputs, but you passed loss_weights=' + str(loss_weights)) loss_weights_list = loss_weights else: raise TypeError('Could not interpret loss_weights argument: ' + str(loss_weights) + ' - expected a list of dicts.') self.loss_weights_list = loss_weights_list # Initialize model metric attributes. self._init_metric_attributes() # Initialization for Eager mode execution. if context.executing_eagerly(): # Prepare sample weights. self._set_sample_weight_attributes(sample_weight_mode, skip_target_weighing_indices) # Save all metric attributes per output of the model. self._cache_output_metric_attributes(metrics, weighted_metrics) if target_tensors is not None: raise ValueError('target_tensors are not currently supported in Eager ' 'mode.') self.total_loss = None for i in range(len(self.outputs)): if len(self.outputs) > 1: self.metrics_names.append(self.output_names[i] + '_loss') # Set metric attributes on model. self._set_metric_attributes( self.outputs, skip_target_indices=skip_target_indices, ) self.targets = [] for i in range(len(self.outputs)): self._feed_output_names.append(self.output_names[i]) self._collected_trainable_weights = self.trainable_weights return # Prepare targets of model. self.targets = [] self._feed_targets = [] if target_tensors not in (None, []): if isinstance(target_tensors, list): if len(target_tensors) != len(self.outputs): raise ValueError( 'When passing a list as `target_tensors`, ' 'it should have one entry per model output. ' 'The model has ' + str(len(self.outputs)) + ' outputs, but you passed target_tensors=' + str(target_tensors)) elif isinstance(target_tensors, dict): for name in target_tensors: if name not in self.output_names: raise ValueError( 'Unknown entry in `target_tensors` ' 'dictionary: "' + name + '". ' 'Only expected the following keys: ' + str(self.output_names)) tmp_target_tensors = [] for name in self.output_names: tmp_target_tensors.append(target_tensors.get(name, None)) target_tensors = tmp_target_tensors elif tensor_util.is_tensor(target_tensors): target_tensors = [target_tensors] else: raise TypeError('Expected `target_tensors` to be a list or tuple or ' 'dict or a single tensor, but got:', target_tensors) for i in range(len(self.outputs)): if i in skip_target_indices: self.targets.append(None) else: shape = K.int_shape(self.outputs[i]) name = self.output_names[i] if target_tensors not in (None, []): target = target_tensors[i] else: target = None if target is None or K.is_placeholder(target): if target is None: target = K.placeholder( ndim=len(shape), name=name + '_target', sparse=K.is_sparse(self.outputs[i]), dtype=K.dtype(self.outputs[i])) self._feed_targets.append(target) self._feed_outputs.append(self.outputs[i]) self._feed_output_names.append(name) self._feed_output_shapes.append(shape) self._feed_loss_fns.append(self.loss_functions[i]) else: skip_target_weighing_indices.append(i) self.targets.append(target) # Prepare sample weights. self._set_sample_weight_attributes(sample_weight_mode, skip_target_weighing_indices) # Save all metric attributes per output of the model. self._cache_output_metric_attributes(metrics, weighted_metrics) # Compute total loss. total_loss = None with K.name_scope('loss'): for i in range(len(self.outputs)): if i in skip_target_indices: continue y_true = self.targets[i] y_pred = self.outputs[i] weighted_loss = weighted_losses[i] sample_weight = self.sample_weights[i] mask = masks[i] loss_weight = loss_weights_list[i] with K.name_scope(self.output_names[i] + '_loss'): output_loss = weighted_loss(y_true, y_pred, sample_weight, mask) if len(self.outputs) > 1: self.metrics_tensors.append(output_loss) self.metrics_names.append(self.output_names[i] + '_loss') if total_loss is None: total_loss = loss_weight * output_loss else: total_loss += loss_weight * output_loss if total_loss is None: if not self.losses: raise ValueError('The model cannot be compiled ' 'because it has no loss to optimize.') else: total_loss = 0. # Add regularization penalties # and other layer-specific losses. for loss_tensor in self.losses: total_loss += loss_tensor # Set metric attributes on model. self._set_metric_attributes( self.outputs, skip_target_indices=skip_target_indices, ) # Invoke metric functions for all the outputs. self._handle_metrics( self.outputs, masks=masks, targets=self.targets, skip_target_indices=skip_target_indices, sample_weights=self.sample_weights) # Prepare gradient updates and state updates. self.total_loss = total_loss # Functions for train, test and predict will # be compiled lazily when required. # This saves time when the user is not using all functions. self._function_kwargs = kwargs self.train_function = None self.test_function = None self.predict_function = None # Collected trainable weights, sorted in topological order. trainable_weights = self.trainable_weights self._collected_trainable_weights = trainable_weights def _check_trainable_weights_consistency(self): """Check trainable weights count consistency. This will raise a warning if `trainable_weights` and `_collected_trainable_weights` are inconsistent (i.e. have different number of parameters). Inconsistency will typically arise when one modifies `model.trainable` without calling `model.compile` again. """ if not hasattr(self, '_collected_trainable_weights'): return if len(self.trainable_weights) != len(self._collected_trainable_weights): logging.warning( UserWarning( 'Discrepancy between trainable weights and collected trainable' ' weights, did you set `model.trainable` without calling' ' `model.compile` after ?')) def _make_train_function(self): if not hasattr(self, 'train_function'): raise RuntimeError('You must compile your model before using it.') self._check_trainable_weights_consistency() if self.train_function is None: inputs = (self._feed_inputs + self._feed_targets + self._feed_sample_weights) if self.uses_learning_phase and not isinstance(K.learning_phase(), int): inputs += [K.learning_phase()] with K.name_scope('training'): with K.name_scope(self.optimizer.__class__.__name__): # Training updates updates = self.optimizer.get_updates( params=self._collected_trainable_weights, loss=self.total_loss) # Unconditional updates updates += self.get_updates_for(None) # Conditional updates relevant to this model updates += self.get_updates_for(self.inputs) # Stateful metrics updates updates += self.metrics_updates # Gets loss and metrics. Updates weights at each call. self.train_function = K.function( inputs, [self.total_loss] + self.metrics_tensors, updates=updates, name='train_function', **self._function_kwargs) def _make_test_function(self): if not hasattr(self, 'test_function'): raise RuntimeError('You must compile your model before using it.') if self.test_function is None: inputs = (self._feed_inputs + self._feed_targets + self._feed_sample_weights) if self.uses_learning_phase and not isinstance(K.learning_phase(), int): inputs += [K.learning_phase()] # Return loss and metrics, no gradient updates. # Does update the network states. self.test_function = K.function( inputs, [self.total_loss] + self.metrics_tensors, updates=self.state_updates + self.metrics_updates, name='test_function', **self._function_kwargs) def _make_predict_function(self): if not hasattr(self, 'predict_function'): self.predict_function = None if self.predict_function is None: if self.uses_learning_phase and not isinstance(K.learning_phase(), int): inputs = self._feed_inputs + [K.learning_phase()] else: inputs = self._feed_inputs # Gets network outputs. Does not update weights. # Does update the network states. kwargs = getattr(self, '_function_kwargs', {}) self.predict_function = K.function( inputs, self.outputs, updates=self.state_updates, name='predict_function', **kwargs) def _get_iterator_get_next_tensors(self, iterator): get_next_op = self._iterator_get_next.get(iterator, None) if get_next_op is None: get_next_op = iterator.get_next() self._iterator_get_next[iterator] = get_next_op return get_next_op def _distribution_standardize_user_data(self, x, y=None, sample_weight=None, class_weight=None, batch_size=None, check_steps=False, steps_name='steps', steps=None, validation_split=0): """Runs validation checks on input and target data passed by the user. This is called when using DistributionStrategy to train, evaluate or serve the model. Args: x: Input data. A numpy array or `tf.data` dataset. y: Target data. A numpy array or None if x is a `tf.data` dataset. sample_weight: An optional sample-weight array passed by the user to weight the importance of each sample in `x`. class_weight: An optional class-weight array by the user to weight the importance of samples in `x` based on the class they belong to, as conveyed by `y`. batch_size: Integer batch size. If provided, it is used to run additional validation checks on stateful models. check_steps: boolean, True if we want to check for validity of `steps` and False, otherwise. steps_name: The public API's parameter name for `steps`. steps: Integer or `None`. Total number of steps (batches of samples) to execute. validation_split: Float between 0 and 1. Fraction of the training data to be used as validation data. Returns: Iterator for reading the dataset `x`. Raises: ValueError: In case of invalid user-provided data. RuntimeError: If the model was never compiled. """ if sample_weight is not None and sample_weight.all(): raise NotImplementedError('`sample_weight` is currently not supported ' 'when using DistributionStrategy.') if class_weight: raise NotImplementedError('`class_weight` is currently not supported ' 'when using DistributionStrategy.') # Validates `steps` argument right at the beginning since we use it to # construct the dataset object. # TODO(anjalisridhar): This may not be a valid error since we now accept # numpy array inputs. We still want to assert that we have a populated steps # parameter. if check_steps: if steps is None: raise ValueError('When using DistributionStrategy, ' 'you should specify the `{steps_name}` argument.' .format(steps_name=steps_name)) first_x_value = nest.flatten(x)[0] if isinstance(first_x_value, np.ndarray): x_shape = first_x_value.shape if batch_size is None: batch_size = x_shape[0] // steps # We need to use the drop_remainder argument to allow for a static # input shape which is required for TPUs. drop_remainder = self._distribution_strategy.require_static_shapes if y is not None: var_x = distributed_training_utils.get_var_for_numpy( self._distribution_strategy, x) var_y = distributed_training_utils.get_var_for_numpy( self._distribution_strategy, y) x = dataset_ops.Dataset.from_tensor_slices((var_x, var_y)) # TODO(anjalisridhar): What should the buffer size be? x = x.shuffle(10000) x = x.repeat() x = x.batch(batch_size, drop_remainder=drop_remainder) y = None else: # This case is for the predict call where the dataset only contains # inputs and no targets i.e it does not return a tuple. # TODO(anjalisridhar): Raise an error if we are not able to process # all the predict samples. This can happen if the number of batches is # not evenly divisible by the number of worker devices. var_x = distributed_training_utils.get_var_for_numpy( self._distribution_strategy, x) x = dataset_ops.Dataset.from_tensor_slices(var_x) x = x.repeat() x = x.batch(batch_size, drop_remainder=drop_remainder) # TODO(anjalisridhar): Can we use the iterator and getnext op cache? # We require users to pass Datasets since we distribute the dataset across # multiple devices. assert isinstance(x, dataset_ops.Dataset) # TODO(anjalisridhar): We want distribute_dataset() to accept a Dataset or a # function which returns a Dataset. Currently distribute_dataset() only # accepts a function that returns a Dataset. Once we add support for being # able to clone a Dataset on multiple workers we can remove this lambda. result = self._distribution_strategy.distribute_dataset(lambda: x) iterator = result.make_initializable_iterator() with self._distribution_strategy.scope(): K.get_session().run(iterator.initializer) training_utils.validate_iterator_input(x, y, sample_weight, validation_split) return iterator def _standardize_user_data(self, x, y=None, sample_weight=None, class_weight=None, batch_size=None, check_steps=False, steps_name='steps', steps=None, validation_split=0): """Runs validation checks on input and target data passed by the user. Also standardizes the data to lists of arrays, in order. Also builds and compiles the model on the fly if it is a subclassed model that has never been called before (and thus has no inputs/outputs). This is a purely internal method, subject to refactoring at any time. 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 or a dataset iterator. 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 or a dataset iterator, `y` should not be specified (since targets will be obtained from the iterator). sample_weight: An optional sample-weight array passed by the user to weight the importance of each sample in `x`. class_weight: An optional class-weight array by the user to weight the importance of samples in `x` based on the class they belong to, as conveyed by `y`. batch_size: Integer batch size. If provided, it is used to run additional validation checks on stateful models. check_steps: boolean, True if we want to check for validity of `steps` and False, otherwise. For example, when we are standardizing one batch of data for train_on_batch/predict_on_batch/test_on_batch APIs, `steps` value is not required and we should not check for its validity in these cases. steps_name: The public API's parameter name for `steps`. steps: Integer or `None`. Total number of steps (batches of samples) to execute. validation_split: Float between 0 and 1. Fraction of the training data to be used as validation data. Returns: A tuple of 3: inputs (arrays or dicts, depending on whether `x` was a dict or not), target arrays, sample-weight arrays. If the model's input and targets are symbolic, these lists are empty (since the model takes no user-provided data, instead the data comes from the symbolic inputs/targets). Raises: ValueError: In case of invalid user-provided data. RuntimeError: If the model was never compiled. """ if self._distribution_strategy: iterator = self._distribution_standardize_user_data( x, y, sample_weight=sample_weight, class_weight=class_weight, batch_size=batch_size, check_steps=check_steps, steps_name=steps_name, steps=steps, validation_split=validation_split) return iterator, None, None if isinstance(x, dataset_ops.Dataset): if context.executing_eagerly(): x = x.make_one_shot_iterator() else: if x in self._dataset_iterator_cache: x = self._dataset_iterator_cache[x] else: iterator = x.make_initializable_iterator() self._dataset_iterator_cache[x] = iterator x = iterator K.get_session().run(x.initializer) # Validates `steps` argument based on x's type. if check_steps: training_utils.check_steps_argument(x, steps, steps_name) is_x_eager_iterator = isinstance(x, iterator_ops.EagerIterator) is_x_iterator = isinstance(x, iterator_ops.Iterator) # Validate user inputs when data is given as a dataset or dataset iterator. if is_x_iterator or is_x_eager_iterator: training_utils.validate_iterator_input(x, y, sample_weight, validation_split) # For eager iterators, when we have to process multiple batches of samples, # we will standardize the data when we actually loop over iterator and get # the batches. For now, we just return the iterator as is. if is_x_eager_iterator and steps is not None: return x, y, sample_weight # If input data is a dataset iterator in graph mode or if it is an eager # iterator and only one batch of samples is required, we fetch the data # tensors from the iterator and then standardize them. if is_x_iterator or is_x_eager_iterator: try: if is_x_iterator: next_element = self._get_iterator_get_next_tensors(x) else: next_element = x.get_next() except errors.OutOfRangeError: raise RuntimeError('Your dataset iterator ran out of data; ' 'Make sure that your dataset can generate ' 'required number of samples.') if isinstance(next_element, (list, tuple)): if len(next_element) not in [2, 3]: raise ValueError( 'Please provide model inputs as a list or tuple of 2 or 3' 'elements: (input, target) or (input, target, sample_weights)' 'Received %s' % next_element) if len(next_element) == 2: x, y = next_element else: x, y, sample_weight = next_element else: x = next_element x, y, sample_weights = self._standardize_weights(x, y, sample_weight, class_weight, batch_size) return x, y, sample_weights def _standardize_weights(self, x, y, sample_weight=None, class_weight=None, batch_size=None,): # TODO(sourabhbajaj): Split input validation from weight standardization. if sample_weight is not None and class_weight is not None: logging.warning( 'Received both a `sample_weight` and `class_weight` argument. ' 'The `class_weight` argument will be ignored.') # First, we build/compile the model on the fly if necessary. all_inputs = [] is_build_called = False is_compile_called = False dict_inputs = False if not self.inputs: # We need to use `x` to set the model inputs. # We type-check that `x` and `y` are either single arrays # or lists of arrays. if isinstance(x, (list, tuple)): if not all(isinstance(v, np.ndarray) or tensor_util.is_tensor(v) for v in x): raise ValueError('Please provide as model inputs either a single ' 'array or a list of arrays. You passed: x=' + str(x)) all_inputs += list(x) elif isinstance(x, dict): dict_inputs = True keys = sorted(x.keys()) all_inputs = [x[k] for k in keys] else: if not isinstance(x, np.ndarray) and not tensor_util.is_tensor(x): raise ValueError('Please provide as model inputs either a single ' 'array or a list of arrays. You passed: x=' + str(x)) all_inputs.append(x) # Build the model using the retrieved inputs (value or symbolic). # If values, then in symbolic-mode placeholders will be created # to match the value shapes. if not self.inputs: is_build_called = True self._set_inputs(x) else: dict_inputs = isinstance(self.inputs, dict) if y is not None: if not self.optimizer: raise RuntimeError('You must compile a model before ' 'training/testing. ' 'Use `model.compile(optimizer, loss)`.') if not self._is_compiled: # On-the-fly compilation of the model. # We need to use `y` to set the model targets. if isinstance(y, (list, tuple)): if not all(isinstance(v, np.ndarray) or tensor_util.is_tensor(v) for v in y): raise ValueError('Please provide as model targets either a single ' 'array or a list of arrays. ' 'You passed: y=' + str(y)) all_inputs += list(y) elif isinstance(y, dict): raise ValueError('Please do not pass a dictionary as model targets.') else: if not isinstance(y, np.ndarray) and not tensor_util.is_tensor(y): raise ValueError('Please provide as model targets either a single ' 'array or a list of arrays. ' 'You passed: y=' + str(y)) all_inputs.append(y) # Typecheck that all inputs are *either* value *or* symbolic. # TODO(fchollet): this check could be removed in Eager mode? if any(tensor_util.is_tensor(v) for v in all_inputs): if not all(tensor_util.is_tensor(v) for v in all_inputs): raise ValueError('Do not pass inputs that mix Numpy arrays and ' 'TensorFlow tensors. ' 'You passed: x=' + str(x) + '; y=' + str(y)) if context.executing_eagerly(): target_tensors = None else: # Handle target tensors if any passed. if not isinstance(y, (list, tuple)): y = [y] target_tensors = [v for v in y if tensor_util.is_tensor(v)] is_compile_called = True self.compile(optimizer=self.optimizer, loss=self.loss, metrics=self.metrics, loss_weights=self.loss_weights, target_tensors=target_tensors) # In graph mode, if we had just set inputs and targets as symbolic tensors # by invoking build and compile on the model respectively, we do not have to # feed anything to the model. Model already has input and target data as # part of the graph. # Note: in this case, `any` and `all` are equivalent since we disallow # mixed symbolic/value inputs. if (not context.executing_eagerly() and is_build_called and is_compile_called and any(tensor_util.is_tensor(v) for v in all_inputs)): return [], [], [] # What follows is input validation and standardization to list format, # in the case where all inputs are value arrays. if context.executing_eagerly(): # In eager mode, do not do shape validation # since the network has no input nodes (placeholders) to be fed. feed_input_names = self.input_names feed_input_shapes = None elif not self._is_graph_network: # Case: symbolic-mode subclassed network. Do not do shape validation. feed_input_names = self._feed_input_names feed_input_shapes = None else: # Case: symbolic-mode graph network. # In this case, we run extensive shape validation checks. feed_input_names = self._feed_input_names feed_input_shapes = self._feed_input_shapes # Standardize the inputs. x = training_utils.standardize_input_data( x, feed_input_names, feed_input_shapes, check_batch_axis=False, # Don't enforce the batch size. exception_prefix='input') if y is not None: if not self._is_graph_network: feed_output_names = self._feed_output_names feed_output_shapes = None # Sample weighting not supported in this case. # TODO(fchollet): consider supporting it. feed_sample_weight_modes = [None for _ in self.outputs] else: feed_output_names = self._feed_output_names feed_sample_weight_modes = self._feed_sample_weight_modes feed_output_shapes = [] for output_shape, loss_fn in zip(self._feed_output_shapes, self._feed_loss_fns): if loss_fn is losses.sparse_categorical_crossentropy: if K.image_data_format() == 'channels_first': feed_output_shapes.append( (output_shape[0], 1) + output_shape[2:]) else: feed_output_shapes.append(output_shape[:-1] + (1,)) elif (not hasattr(loss_fn, '__name__') or getattr(losses, loss_fn.__name__, None) is None): # If `loss_fn` is not a function (e.g. callable class) # or if it not in the `losses` module, then # it is a user-defined loss and we make no assumptions # about it. feed_output_shapes.append(None) else: feed_output_shapes.append(output_shape) # Standardize the outputs. y = training_utils.standardize_input_data( y, feed_output_names, feed_output_shapes, check_batch_axis=False, # Don't enforce the batch size. exception_prefix='target') # Generate sample-wise weight values given the `sample_weight` and # `class_weight` arguments. sample_weights = training_utils.standardize_sample_weights( sample_weight, feed_output_names) class_weights = training_utils.standardize_class_weights( class_weight, feed_output_names) sample_weights = [ training_utils.standardize_weights(ref, sw, cw, mode) for (ref, sw, cw, mode) in zip(y, sample_weights, class_weights, feed_sample_weight_modes) ] # Check that all arrays have the same length. if not self._distribution_strategy: training_utils.check_array_lengths(x, y, sample_weights) if self._is_graph_network and not context.executing_eagerly(): # Additional checks to avoid users mistakenly using improper loss fns. training_utils.check_loss_and_target_compatibility( y, self._feed_loss_fns, feed_output_shapes) else: y = [] sample_weights = [] if self.stateful and batch_size: # Check that for stateful networks, number of samples is a multiple # of the static batch size. if x[0].shape[0] % batch_size != 0: raise ValueError('In a stateful network, ' 'you should only pass inputs with ' 'a number of samples that can be ' 'divided by the batch size. Found: ' + str(x[0].shape[0]) + ' samples') # If dictionary inputs were provided, we return a dictionary as well. if dict_inputs: x = dict(zip(feed_input_names, x)) return x, y, sample_weights @checkpointable.no_automatic_dependency_tracking def _set_inputs(self, inputs, training=None): """Set model's input and output specs based on the input data received. This is to be used for Model subclasses, which do not know at instantiation time what their inputs look like. Args: inputs: Single array, or list of arrays. The arrays could be placeholders, Numpy arrays, or data tensors. - if placeholders: the model is built on top of these placeholders, and we expect Numpy data to be fed for them when calling `fit`/etc. - if Numpy data: we create placeholders matching the shape of the Numpy arrays. We expect Numpy data to be fed for these placeholders when calling `fit`/etc. - if data tensors: the model is built on top of these tensors. We do not expect any Numpy data to be provided when calling `fit`/etc. training: Boolean or None. Only relevant in symbolic mode. Specifies whether to build the model's graph in inference mode (False), training mode (True), or using the Keras learning phase (None). Raises: ValueError: If dict inputs are passed to a Sequential Model where the first layer isn't FeatureLayer. """ call_convention = getattr( self, '_call_convention', base_layer.CallConvention.EXPLICIT_INPUTS_ARGUMENT) if call_convention not in ( base_layer.CallConvention.EXPLICIT_INPUTS_ARGUMENT, base_layer.CallConvention.SINGLE_POSITIONAL_ARGUMENT): raise NotImplementedError( 'Subclassed Models without "inputs" (or single positional arguments) ' 'in their call() signatures do not yet support shape inference. File ' 'a feature request if this limitation bothers you.') if self.__class__.__name__ == 'Sequential': if tensor_util.is_tensor(inputs): input_shape = (None,) + tuple(inputs.get_shape().as_list()[1:]) self.build(input_shape=input_shape) elif isinstance(inputs, dict): # We assert that the first layer is a FeatureLayer. if not training_utils.is_feature_layer(self.layers[0]): raise ValueError('Passing a dictionary input to a Sequential Model ' 'which doesnt have FeatureLayer as the first layer ' 'is an error') input_shape = (None,) self.build(input_shape=input_shape) else: input_shape = (None,) + inputs.shape[1:] self.build(input_shape=input_shape) if context.executing_eagerly(): self._eager_set_inputs(inputs) else: self._symbolic_set_inputs(inputs, training=training) @checkpointable.no_automatic_dependency_tracking def _eager_set_inputs(self, inputs): """Set model's input and output specs based on the input data received. This is to be used for Model subclasses, which do not know at instantiation time what their inputs look like. We assume the number and ndim of outputs does not change over different calls. Args: inputs: Argument `x` (input data) passed by the user upon first model use. Raises: ValueError: If the model's inputs are already set. """ assert context.executing_eagerly() if self.inputs: raise ValueError('Model inputs are already set.') # On-the-fly setting of model inputs/outputs as DeferredTensors, # to keep track of number of inputs and outputs and their ndim. model_inputs = training_utils.ModelInputs(inputs) dummy_input_values = model_inputs.get_input_values() dummy_output_values = self.call(dummy_input_values) self.inputs = model_inputs.get_symbolic_inputs(return_single_as_list=True) self.input_names = model_inputs.get_input_names() dummy_output_values = nest.flatten(dummy_output_values) self.outputs = [ base_layer.DeferredTensor(shape=(None for _ in v.shape), dtype=v.dtype) for v in dummy_output_values ] self.output_names = [ 'output_%d' % (i + 1) for i in range(len(dummy_output_values))] self.built = True @checkpointable.no_automatic_dependency_tracking def _symbolic_set_inputs(self, inputs, outputs=None, training=None): """Set model's inputs and output specs based. This is to be used for Model subclasses, which do not know at instantiation time what their inputs look like. Args: inputs: Argument `x` (input data) passed by the user upon first model use. outputs: None, a data tensor, or a list of data tensors. If None, the outputs will be determined by invoking self.call(), otherwise the provided value will be used. training: Boolean or None. Only relevant in symbolic mode. Specifies whether to build the model's graph in inference mode (False), training mode (True), or using the Keras learning phase (None). Raises: ValueError: If the model's inputs are already set. """ assert not context.executing_eagerly() if self.inputs: raise ValueError('Model inputs are already set.') # On-the-fly setting of symbolic model inputs (either by using the tensor # provided, or by creating a placeholder if Numpy data was provided). model_inputs = training_utils.ModelInputs(inputs) dummy_input_values = model_inputs.get_symbolic_inputs() self.inputs = model_inputs.get_symbolic_inputs(return_single_as_list=True) self.input_names = model_inputs.get_input_names() self._feed_inputs = [] self._feed_input_names = [] self._feed_input_shapes = [] for k, v in model_inputs.as_dict(): if K.is_placeholder(v): self._feed_inputs.append(v) self._feed_input_names.append(k) self._feed_input_shapes.append(K.int_shape(v)) if outputs is None: # Obtain symbolic outputs by calling the model. if self._expects_training_arg: outputs = self.call(dummy_input_values, training=training) else: outputs = self.call(dummy_input_values) outputs = nest.flatten(outputs) self.outputs = outputs self.output_names = [ 'output_%d' % (i + 1) for i in range(len(self.outputs))] self.built = True def fit(self, x=None, y=None, batch_size=None, epochs=1, verbose=1, callbacks=None, validation_split=0., validation_data=None, shuffle=True, class_weight=None, sample_weight=None, initial_epoch=0, steps_per_epoch=None, validation_steps=None, max_queue_size=10, workers=1, use_multiprocessing=False, **kwargs): """Trains the model for a fixed number of epochs (iterations on a dataset). Arguments: 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 or a dataset iterator. 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)`. 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, dataset iterator, 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 symbolic tensors, dataset, dataset iterators, 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. 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: Integer. 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch. callbacks: List of `keras.callbacks.Callback` instances. List of callbacks to apply during training. See [callbacks](/api_docs/python/tf/keras/callbacks). 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, dataset iterator, generator or `keras.utils.Sequence` instance. 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. `validation_data` will override `validation_split`. `validation_data` could be: - tuple `(x_val, y_val)` of Numpy arrays or tensors - tuple `(x_val, y_val, val_sample_weights)` of Numpy arrays - dataset or a dataset iterator For the first two cases, `batch_size` must be provided. For the last case, `validation_steps` must be provided. shuffle: Boolean (whether to shuffle the training data before each epoch) or str (for 'batch'). '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. In this case you should make sure to specify `sample_weight_mode="temporal"` in `compile()`. This argument is not supported when `x` is a dataset, dataset iterator, generator, or `keras.utils.Sequence` instance, instead provide the sample_weights as the third element of `x`. 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. validation_steps: Only relevant if `validation_data` is provided and is a dataset or dataset iterator. Total number of steps (batches of samples) to draw before stopping when performing validation at the end of every epoch. 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. If 0, will execute the generator on the main thread. 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. **kwargs: Used for backwards compatibility. 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: If the model was never compiled. ValueError: In case of mismatch between the provided input data and what the model expects. """ # TODO(fchollet): this method may be creating reference cycles, which would # lead to accumulating garbage in memory when called in a loop. Investigate. if data_utils.is_generator_or_sequence(x): training_utils.check_generator_arguments(y, sample_weight) return self.fit_generator( x, steps_per_epoch=steps_per_epoch, epochs=epochs, verbose=verbose, callbacks=callbacks, validation_data=validation_data, validation_steps=validation_steps, class_weight=class_weight, max_queue_size=max_queue_size, workers=workers, use_multiprocessing=use_multiprocessing, shuffle=shuffle, initial_epoch=initial_epoch) # Backwards compatibility if batch_size is None and steps_per_epoch is None: batch_size = 32 # Legacy support if 'nb_epoch' in kwargs: logging.warning( 'The `nb_epoch` argument in `fit` ' 'has been renamed `epochs`.') epochs = kwargs.pop('nb_epoch') if kwargs: raise TypeError('Unrecognized keyword arguments: ' + str(kwargs)) # Validate and standardize user data. if self._distribution_strategy: distributed_training_utils.validate_callbacks(callbacks) distributed_training_utils.validate_inputs( x, y, self._distribution_strategy) first_x_value = nest.flatten(x)[0] if not steps_per_epoch and isinstance(first_x_value, np.ndarray): steps_per_epoch = distributed_training_utils.get_input_batch_params( first_x_value, batch_size, self._distribution_strategy) x, y, sample_weights = self._standardize_user_data( x, y, sample_weight=sample_weight, class_weight=class_weight, batch_size=batch_size, check_steps=True, steps_name='steps_per_epoch', steps=steps_per_epoch, validation_split=validation_split) # Prepare validation data. if validation_data: if (isinstance(validation_data, iterator_ops.Iterator) or isinstance(validation_data, iterator_ops.EagerIterator) or isinstance(validation_data, dataset_ops.Dataset)): val_x = validation_data val_y = None val_sample_weight = None elif len(validation_data) == 2: val_x, val_y = validation_data # pylint: disable=unpacking-non-sequence val_sample_weight = None elif len(validation_data) == 3: val_x, val_y, val_sample_weight = validation_data # pylint: disable=unpacking-non-sequence else: raise ValueError( 'When passing a `validation_data` argument, ' 'it must contain either 2 items (x_val, y_val), ' 'or 3 items (x_val, y_val, val_sample_weights), ' 'or alternatively it could be a dataset or a ' 'dataset or a dataset iterator. ' 'However we received `validation_data=%s`' % validation_data) # Validate and standardize validation data. if self._distribution_strategy: distributed_training_utils.validate_inputs( val_x, val_y, self._distribution_strategy) first_valx_value = nest.flatten(val_x)[0] if not validation_steps and isinstance(first_valx_value, np.ndarray): validation_steps = distributed_training_utils.get_input_batch_params( first_valx_value, batch_size, self._distribution_strategy) val_x, val_y, val_sample_weights = self._standardize_user_data( val_x, val_y, sample_weight=val_sample_weight, batch_size=batch_size, steps=validation_steps) elif validation_split and 0. < validation_split < 1.: if training_utils.has_symbolic_tensors(x): raise ValueError('If your data is in the form of symbolic tensors, ' 'you cannot use `validation_split`.') if hasattr(x[0], 'shape'): split_at = int(x[0].shape[0] * (1. - validation_split)) else: split_at = int(len(x[0]) * (1. - validation_split)) x, val_x = (slice_arrays(x, 0, split_at), slice_arrays(x, split_at)) y, val_y = (slice_arrays(y, 0, split_at), slice_arrays(y, split_at)) sample_weights, val_sample_weights = (slice_arrays( sample_weights, 0, split_at), slice_arrays(sample_weights, split_at)) elif validation_steps: val_x = [] val_y = [] val_sample_weights = [] else: val_x = None val_y = None val_sample_weights = None if context.executing_eagerly(): return training_eager.fit_loop( self, inputs=x, targets=y, sample_weights=sample_weights, class_weight=class_weight, batch_size=batch_size, epochs=epochs, verbose=verbose, callbacks=callbacks, val_inputs=val_x, val_targets=val_y, val_sample_weights=val_sample_weights, shuffle=shuffle, initial_epoch=initial_epoch, steps_per_epoch=steps_per_epoch, validation_steps=validation_steps) elif self._distribution_strategy: return training_distributed.fit_loop( self, x, epochs=epochs, verbose=verbose, callbacks=callbacks, val_iterator=val_x, initial_epoch=initial_epoch, steps_per_epoch=steps_per_epoch, validation_steps=validation_steps) else: return training_arrays.fit_loop( self, x, y, sample_weights=sample_weights, batch_size=batch_size, epochs=epochs, verbose=verbose, callbacks=callbacks, val_inputs=val_x, val_targets=val_y, val_sample_weights=val_sample_weights, shuffle=shuffle, initial_epoch=initial_epoch, steps_per_epoch=steps_per_epoch, validation_steps=validation_steps) def evaluate(self, x=None, y=None, batch_size=None, verbose=1, sample_weight=None, steps=None, max_queue_size=10, workers=1, use_multiprocessing=False): """Returns the loss value & metrics values for the model in test mode. Computation is done in batches. Arguments: 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 or a dataset iterator. - A generator or `keras.utils.Sequence` instance. 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, dataset iterator, 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 gradient update. If unspecified, `batch_size` will default to 32. Do not specify the `batch_size` is your data is in the form of symbolic tensors, dataset, dataset iterators, generators, or `keras.utils.Sequence` instances (since they generate batches). verbose: 0 or 1. Verbosity mode. 0 = silent, 1 = progress bar. 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. In this case you should make sure to specify `sample_weight_mode="temporal"` in `compile()`. This argument is not supported when `x` is a dataset or a dataset iterator, 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`. 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. If 0, will execute the generator on the main thread. 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. 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: ValueError: in case of invalid arguments. """ if data_utils.is_generator_or_sequence(x): training_utils.check_generator_arguments(y, sample_weight) return self.evaluate_generator( x, steps=steps, verbose=verbose, max_queue_size=max_queue_size, workers=workers, use_multiprocessing=use_multiprocessing) # Backwards compatibility. if batch_size is None and steps is None: batch_size = 32 # Validate and standardize user data. if self._distribution_strategy: distributed_training_utils.validate_inputs( x, y, self._distribution_strategy) first_x_value = nest.flatten(x)[0] if isinstance(first_x_value, np.ndarray) and not steps: steps = distributed_training_utils.get_input_batch_params( first_x_value, batch_size, self._distribution_strategy) x, y, sample_weights = self._standardize_user_data( x, y, sample_weight=sample_weight, batch_size=batch_size, check_steps=True, steps_name='steps', steps=steps) if context.executing_eagerly(): return training_eager.test_loop( self, inputs=x, targets=y, sample_weights=sample_weights, batch_size=batch_size, verbose=verbose, steps=steps) elif self._distribution_strategy: return training_distributed.test_loop( self, iterator=x, verbose=verbose, steps=steps) else: return training_arrays.test_loop( self, inputs=x, targets=y, sample_weights=sample_weights, batch_size=batch_size, verbose=verbose, steps=steps) def predict(self, x, batch_size=None, verbose=0, steps=None, max_queue_size=10, workers=1, use_multiprocessing=False): """Generates output predictions for the input samples. Computation is done in batches. Arguments: 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 or a dataset iterator. - A generator or `keras.utils.Sequence` instance. 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` is your data is in the form of symbolic tensors, dataset, dataset iterators, generators, or `keras.utils.Sequence` instances (since they generate batches). verbose: Verbosity mode, 0 or 1. steps: Total number of steps (batches of samples) before declaring the prediction round finished. Ignored with the default value of `None`. 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. If 0, will execute the generator on the main thread. 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. Returns: Numpy array(s) of predictions. Raises: 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. """ if data_utils.is_generator_or_sequence(x): return self.predict_generator( x, steps=steps, verbose=verbose, max_queue_size=max_queue_size, workers=workers, use_multiprocessing=use_multiprocessing) # Backwards compatibility. if batch_size is None and steps is None: batch_size = 32 if self._distribution_strategy: # Turn off prefetching since this is currently not deterministic. Once # b/112498930 is fixed we can turn it back on. # `_prefetch_on_device` is currently a property of only # `MirroredStrategy`. if hasattr(self._distribution_strategy, '_prefetch_on_device'): self._distribution_strategy._prefetch_on_device = False # pylint: disable=protected-access distributed_training_utils.validate_inputs( x, None, self._distribution_strategy) first_x_value = nest.flatten(x)[0] if isinstance(first_x_value, np.ndarray) and not steps: steps = distributed_training_utils.get_input_batch_params( first_x_value, batch_size, self._distribution_strategy) # Validate and standardize user data. # TODO(anjalisridhar): We don't pass batch_size here for some reason. This # means that we end up calculating it twice which we should avoid. x, _, _ = self._standardize_user_data( x, check_steps=True, steps_name='steps', steps=steps) if context.executing_eagerly(): return training_eager.predict_loop( self, x, batch_size=batch_size, verbose=verbose, steps=steps) elif self._distribution_strategy: results = training_distributed.predict_loop( self, x, verbose=verbose, steps=steps) # Turn prefetching back on since we turned it off previously. if hasattr(self._distribution_strategy, '_prefetch_on_device'): self._distribution_strategy._prefetch_on_device = True # pylint: disable=protected-access return results else: return training_arrays.predict_loop( self, x, batch_size=batch_size, verbose=verbose, steps=steps) def train_on_batch(self, x, y=None, sample_weight=None, class_weight=None): """Runs a single gradient update on a single batch of data. Arguments: 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 or a dataset iterator. 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 or a dataset iterator, `y` should not be specified (since targets will be obtained from the iterator). 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. In this case you should make sure to specify sample_weight_mode="temporal" in compile(). This argument is not supported when `x` is a dataset or a dataset iterator. 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. 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: ValueError: In case of invalid user-provided arguments. """ if self._distribution_strategy: raise NotImplementedError('`train_on_batch` is not supported for models ' 'compiled with DistributionStrategy.') # Validate and standardize user data. x, y, sample_weights = self._standardize_user_data( x, y, sample_weight=sample_weight, class_weight=class_weight) if context.executing_eagerly(): outputs = training_eager.train_on_batch( self, x, y, sample_weights=sample_weights) else: if self.uses_learning_phase and not isinstance(K.learning_phase(), int): ins = x + y + sample_weights + [1] else: ins = x + y + sample_weights self._make_train_function() outputs = self.train_function(ins) if len(outputs) == 1: return outputs[0] return outputs def test_on_batch(self, x, y=None, sample_weight=None): """Test the model on a single batch of samples. Arguments: 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 or a dataset iterator. 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 or a dataset iterator, `y` should not be specified (since targets will be obtained from the iterator). 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. In this case you should make sure to specify sample_weight_mode="temporal" in compile(). This argument is not supported when `x` is a dataset or a dataset iterator. 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: ValueError: In case of invalid user-provided arguments. """ if self._distribution_strategy: raise NotImplementedError('`test_on_batch` is not supported for models ' 'compiled with DistributionStrategy.') # Validate and standardize user data. x, y, sample_weights = self._standardize_user_data( x, y, sample_weight=sample_weight) if context.executing_eagerly(): outputs = training_eager.test_on_batch( self, x, y, sample_weights=sample_weights) else: if self.uses_learning_phase and not isinstance(K.learning_phase(), int): ins = x + y + sample_weights + [0] else: ins = x + y + sample_weights self._make_test_function() outputs = self.test_function(ins) if len(outputs) == 1: return outputs[0] return outputs def predict_on_batch(self, x): """Returns predictions for a single batch of samples. Arguments: 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 `tf.data` dataset or a dataset iterator. Returns: Numpy array(s) of predictions. Raises: ValueError: In case of mismatch between given number of inputs and expectations of the model. """ if self._distribution_strategy: raise NotImplementedError('`predict_on_batch` is not supported for ' 'models compiled with DistributionStrategy.') # Validate and standardize user data. inputs, _, _ = self._standardize_user_data(x) if context.executing_eagerly(): if (isinstance(x, iterator_ops.EagerIterator) or (isinstance(x, dataset_ops.Dataset) and context.executing_eagerly())): inputs = training_utils.cast_if_floating_dtype(inputs) else: inputs = [ ops.convert_to_tensor(val, dtype=K.floatx()) for val in inputs ] return self(inputs) # pylint: disable=not-callable if not context.executing_eagerly(): if self.uses_learning_phase and not isinstance(K.learning_phase(), int): ins = inputs + [0] else: ins = inputs self._make_predict_function() outputs = self.predict_function(ins) if len(outputs) == 1: return outputs[0] return outputs def fit_generator(self, generator, steps_per_epoch=None, epochs=1, verbose=1, callbacks=None, validation_data=None, validation_steps=None, 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. The generator is run in parallel to the model, for efficiency. For instance, this allows you to do real-time data augmentation on images on CPU in parallel to training your model on GPU. The use of `keras.utils.Sequence` guarantees the ordering and guarantees the single use of every input per epoch when using `use_multiprocessing=True`. Arguments: generator: A generator or an instance of `Sequence` (`keras.utils.Sequence`) object in order to avoid duplicate data when using multiprocessing. The output of the generator must be either - a tuple `(inputs, targets)` - a tuple `(inputs, targets, sample_weights)`. This tuple (a single output of the generator) makes a single batch. Therefore, all arrays in this tuple must have the same length (equal to the size of this batch). Different batches may have different sizes. For example, the last batch of the epoch is commonly smaller than the others, if the size of the dataset is not divisible by the batch size. The generator is expected to loop over its data indefinitely. An epoch finishes when `steps_per_epoch` batches have been seen by the model. steps_per_epoch: Total number of steps (batches of samples) to yield from `generator` before declaring one epoch finished and starting the next epoch. It should typically be equal to the number of samples of your dataset divided by the batch size. Optional for `Sequence`: if unspecified, will use the `len(generator)` as a number of steps. epochs: Integer, total number of iterations on the data. verbose: Verbosity mode, 0, 1, or 2. callbacks: List of callbacks to be called during training. validation_data: This can be either - a generator for the validation data - a tuple (inputs, targets) - a tuple (inputs, targets, sample_weights). validation_steps: Only relevant if `validation_data` is a generator. Total number of steps (batches of samples) to yield from `generator` before stopping. Optional for `Sequence`: if unspecified, will use the `len(validation_data)` as a number of steps. class_weight: Dictionary mapping class indices to a weight for the class. max_queue_size: Integer. Maximum size for the generator queue. If unspecified, `max_queue_size` will default to 10. workers: Integer. Maximum number of processes to spin up when using process-based threading. If unspecified, `workers` will default to 1. If 0, will execute the generator on the main thread. use_multiprocessing: Boolean. 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. shuffle: Boolean. Whether to shuffle the order of the batches at the beginning of each epoch. Only used with instances of `Sequence` (`keras.utils.Sequence`). Has no effect when `steps_per_epoch` is not `None`. initial_epoch: Epoch at which to start training (useful for resuming a previous training run) Returns: A `History` object. Example: ```python def generate_arrays_from_file(path): while 1: f = open(path) for line in f: # create numpy arrays of input data # and labels, from each line in the file x1, x2, y = process_line(line) yield ({'input_1': x1, 'input_2': x2}, {'output': y}) f.close() model.fit_generator(generate_arrays_from_file('/my_file.txt'), steps_per_epoch=10000, epochs=10) ``` Raises: ValueError: In case the generator yields data in an invalid format. """ if self._distribution_strategy: raise NotImplementedError('`fit_generator` is not supported for ' 'models compiled with DistributionStrategy.') if not self.built and not self._is_graph_network: raise NotImplementedError( '`fit_generator` is not yet enabled for unbuilt Model subclasses') return training_generator.fit_generator( self, generator, steps_per_epoch=steps_per_epoch, epochs=epochs, verbose=verbose, callbacks=callbacks, validation_data=validation_data, validation_steps=validation_steps, class_weight=class_weight, max_queue_size=max_queue_size, workers=workers, use_multiprocessing=use_multiprocessing, shuffle=shuffle, initial_epoch=initial_epoch) def evaluate_generator(self, generator, steps=None, max_queue_size=10, workers=1, use_multiprocessing=False, verbose=0): """Evaluates the model on a data generator. The generator should return the same kind of data as accepted by `test_on_batch`. Arguments: generator: Generator yielding tuples (inputs, targets) or (inputs, targets, sample_weights) or an instance of `keras.utils.Sequence` object in order to avoid duplicate data when using multiprocessing. steps: Total number of steps (batches of samples) to yield from `generator` before stopping. Optional for `Sequence`: if unspecified, will use the `len(generator)` as a number of steps. max_queue_size: maximum size for the generator queue workers: Integer. Maximum number of processes to spin up when using process-based threading. If unspecified, `workers` will default to 1. If 0, will execute the generator on the main thread. use_multiprocessing: Boolean. 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. verbose: Verbosity mode, 0 or 1. 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: ValueError: in case of invalid arguments. Raises: ValueError: In case the generator yields data in an invalid format. """ if self._distribution_strategy: raise NotImplementedError('`evaluate_generator` is not supported for ' 'models compiled with DistributionStrategy.') if not self.built and not self._is_graph_network: raise NotImplementedError( '`evaluate_generator` is not yet enabled for ' 'unbuilt Model subclasses') return training_generator.evaluate_generator( self, generator, steps=steps, max_queue_size=max_queue_size, workers=workers, use_multiprocessing=use_multiprocessing, verbose=verbose) def predict_generator(self, generator, steps=None, max_queue_size=10, workers=1, use_multiprocessing=False, verbose=0): """Generates predictions for the input samples from a data generator. The generator should return the same kind of data as accepted by `predict_on_batch`. Arguments: generator: Generator yielding batches of input samples or an instance of `keras.utils.Sequence` object in order to avoid duplicate data when using multiprocessing. steps: Total number of steps (batches of samples) to yield from `generator` before stopping. Optional for `Sequence`: if unspecified, will use the `len(generator)` as a number of steps. max_queue_size: Maximum size for the generator queue. workers: Integer. Maximum number of processes to spin up when using process-based threading. If unspecified, `workers` will default to 1. If 0, will execute the generator on the main thread. use_multiprocessing: Boolean. 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. verbose: verbosity mode, 0 or 1. Returns: Numpy array(s) of predictions. Raises: ValueError: In case the generator yields data in an invalid format. """ if self._distribution_strategy: raise NotImplementedError('`predict_generator` is not supported for ' 'models compiled with DistributionStrategy.') if not self.built and not self._is_graph_network: raise NotImplementedError( '`predict_generator` is not yet enabled for unbuilt Model subclasses') return training_generator.predict_generator( self, generator, steps=steps, max_queue_size=max_queue_size, workers=workers, use_multiprocessing=use_multiprocessing, verbose=verbose) def _get_callback_model(self): """Returns the Callback Model for this Model.""" if hasattr(self, '_replicated_model') and self._replicated_model: # When using training_distributed, we set the callback model # to an instance of the `DistributedModel` that we create in # the `compile` call. The `DistributedModel` is initialized # with the first replicated model. We need to set the callback # model to a DistributedModel to allow us to override saving # and loading weights when we checkpoint the model during training. return self._replicated_model if hasattr(self, 'callback_model') and self.callback_model: return self.callback_model return self def _make_callback_model(self): first_replicated_model = self._distribution_strategy.unwrap( self._grouped_model)[0] # We initialize the callback model with the first replicated model. self._replicated_model = DistributedCallbackModel(first_replicated_model) self._replicated_model.set_original_model(self) class DistributedCallbackModel(Model): """Model that is used for callbacks with DistributionStrategy.""" def __init__(self, model): super(DistributedCallbackModel, self).__init__() # TODO(anjalisridhar): Right now the only attributes set are the layer and # weights. We may need to set additional attributes as needed since we have # not called compile on this model. def set_original_model(self, orig_model): self._original_model = orig_model def save_weights(self, filepath, overwrite=True, save_format=None): self._replicated_model.save_weights(filepath, overwrite=overwrite, save_format=save_format) def save(self, filepath, overwrite=True, include_optimizer=True): # save weights from the distributed model to the original model distributed_model_weights = self.get_weights() self._original_model.set_weights(distributed_model_weights) # TODO(anjalisridhar): Do we need to save the original model here? # Saving the first replicated model works as well. self._original_model.save(filepath, overwrite=True, include_optimizer=False) def load_weights(self, filepath, by_name=False): self._original_model.load_weights(filepath, by_name=False) # Copy the weights from the original model to each of the replicated models. orig_model_weights = self._original_model.get_weights() distributed_training_utils.set_weights( self._original_model._distribution_strategy, self, # pylint: disable=protected-access orig_model_weights) def __getattr__(self, item): # Whitelisted atttributes of the model that can be accessed by the user # during a callback. if item not in ['_setattr_tracking']: logging.warning('You are accessing attribute ' + item + ' of the ' 'DistributedCallbackModel that may not have been set ' 'correctly.')