def __init__(self, base_model, graph_reg_config=None): """Class initializer. Args: base_model: Unregularized model to which the loss term resulting from graph regularization will be added. graph_reg_config: Instance of `GraphRegConfig` that contains configuration for graph regularization. """ super(GraphRegularization, self).__init__(name='GraphRegularization') self.base_model = base_model self.graph_reg_config = (nsl_configs.GraphRegConfig() if graph_reg_config is None else graph_reg_config) self.nbr_features_layer = nsl_layers.NeighborFeatures( self.graph_reg_config.neighbor_config) self.regularizer = nsl_layers.PairwiseDistance( self.graph_reg_config.distance_config, name='graph_loss')
def _create_and_compile_graph_reg_model(model_fn, weight, max_neighbors): """Creates and compiles a graph regularized model. Args: model_fn: A function that builds a linear regression model. weight: Initial value for the weights variable in the linear regressor. max_neighbors: The maximum number of neighbors for graph regularization. Returns: A pair containing the unregularized model and the graph regularized model as `tf.keras.Model` instances. """ model = model_fn((2, ), weight) graph_reg_config = configs.GraphRegConfig( configs.GraphNeighborConfig(max_neighbors=max_neighbors), multiplier=1) graph_reg_model = graph_regularization.GraphRegularization( model, graph_reg_config) graph_reg_model.compile( optimizer=keras.optimizers.SGD(LEARNING_RATE), loss='MSE') return model, graph_reg_model
def add_graph_regularization(estimator, embedding_fn, optimizer_fn=None, graph_reg_config=None): """Adds graph regularization to a `tf.estimator.Estimator`. Args: estimator: An object of type `tf.estimator.Estimator`. embedding_fn: A function that accepts the input layer (dictionary of feature names and corresponding batched tensor values) as its first argument, an instance of `tf.estimator.ModeKeys` as its second argument to indicate if the mode is training, evaluation, or prediction, and an optional third argument named `params` which is a `dict` similar to the `params` argument of `tf.estimator.Estimator`'s `model_fn`, and returns the corresponding embeddings or logits to be used for graph regularization. The `params` argument will receive what was passed to `estimator` at the time of its creation as its `params` argument. optimizer_fn: A function that accepts no arguments and returns an instance of `tf.train.Optimizer`. graph_reg_config: An instance of `nsl.configs.GraphRegConfig` that specifies various hyperparameters for graph regularization. Returns: A modified `tf.estimator.Estimator` object with graph regularization incorporated into its loss. """ if not graph_reg_config: graph_reg_config = configs.GraphRegConfig() base_model_fn = estimator._model_fn # pylint: disable=protected-access try: base_model_fn_args = inspect.signature(base_model_fn).parameters.keys() except AttributeError: # For Python 2 compatibility base_model_fn_args = inspect.getargspec(base_model_fn).args # pylint: disable=deprecated-method def graph_reg_model_fn(features, labels, mode, params=None, config=None): """The graph-regularized model function. Args: features: This is the first item returned from the `input_fn` passed to `train`, `evaluate`, and `predict`. This should be a dictionary containing sample features as well as corresponding neighbor features and neighbor weights. labels: This is the second item returned from the `input_fn` passed to `train`, `evaluate`, and `predict`. This should be a single `Tensor` or `dict` of same (for multi-head models). If mode is `tf.estimator.ModeKeys.PREDICT`, `labels=None` will be passed. If the `model_fn`'s signature does not accept `mode`, the `model_fn` must still be able to handle `labels=None`. mode: Optional. Specifies if this is training, evaluation, or prediction. See `tf.estimator.ModeKeys`. params: Optional `dict` of hyperparameters. Will receive what is passed to Estimator in the `params` parameter. This allows users to configure Estimators from hyper parameter tuning. config: Optional `tf.estimator.RunConfig` object. Will receive what is passed to Estimator as its `config` parameter, or a default value. Allows setting up things in the `model_fn` based on configuration such as `num_ps_replicas`, or `model_dir`. Unused currently. Returns: A `tf.estimator.EstimatorSpec` with graph regularization. """ # Parameters 'params' and 'config' are optional. If they are not passed, # then it is possible for base_model_fn not to accept these arguments. # See documentation for tf.estimator.Estimator for additional context. kwargs = {'mode': mode} embedding_fn_kwargs = dict() if 'params' in base_model_fn_args: kwargs['params'] = params embedding_fn_kwargs['params'] = params if 'config' in base_model_fn_args: kwargs['config'] = config # Uses the same variable scope for calculating the original objective and # the graph regularization loss term. with tf.compat.v1.variable_scope(tf.compat.v1.get_variable_scope(), reuse=tf.compat.v1.AUTO_REUSE, auxiliary_name_scope=False): nbr_features = dict() nbr_weights = None if mode == tf.estimator.ModeKeys.TRAIN: # Extract sample features, neighbor features, and neighbor weights if we # are in training mode. sample_features, nbr_features, nbr_weights = ( utils.unpack_neighbor_features( features, graph_reg_config.neighbor_config)) else: # Otherwise, we strip out all neighbor features and use just the # sample's features. sample_features = utils.strip_neighbor_features( features, graph_reg_config.neighbor_config) base_spec = base_model_fn(sample_features, labels, **kwargs) has_nbr_inputs = nbr_weights is not None and nbr_features # Graph regularization happens only if all the following conditions are # satisfied: # - the mode is training # - neighbor inputs exist # - the graph regularization multiplier is greater than zero. # So, return early if any of these conditions is false. if (not has_nbr_inputs or mode != tf.estimator.ModeKeys.TRAIN or graph_reg_config.multiplier <= 0): return base_spec # Compute sample embeddings. sample_embeddings = embedding_fn(sample_features, mode, **embedding_fn_kwargs) # Compute the embeddings of the neighbors. nbr_embeddings = embedding_fn(nbr_features, mode, **embedding_fn_kwargs) replicated_sample_embeddings = utils.replicate_embeddings( sample_embeddings, graph_reg_config.neighbor_config.max_neighbors) # Compute the distance between the sample embeddings and each of their # corresponding neighbor embeddings. graph_loss = distances.pairwise_distance_wrapper( replicated_sample_embeddings, nbr_embeddings, weights=nbr_weights, distance_config=graph_reg_config.distance_config) scaled_graph_loss = graph_reg_config.multiplier * graph_loss tf.compat.v1.summary.scalar('loss/scaled_graph_loss', scaled_graph_loss) supervised_loss = base_spec.loss tf.compat.v1.summary.scalar('loss/supervised_loss', supervised_loss) total_loss = supervised_loss + scaled_graph_loss if not optimizer_fn: # Default to Adagrad optimizer, the same as the canned DNNEstimator. optimizer = tf.compat.v1.train.AdagradOptimizer( learning_rate=0.05) else: optimizer = optimizer_fn() train_op = optimizer.minimize( loss=total_loss, global_step=tf.compat.v1.train.get_global_step()) update_ops = tf.compat.v1.get_collection( tf.compat.v1.GraphKeys.UPDATE_OPS) if update_ops: train_op = tf.group(train_op, *update_ops) return base_spec._replace(loss=total_loss, train_op=train_op) # Replaces the model_fn while keeping other fields/methods in the estimator. estimator._model_fn = graph_reg_model_fn # pylint: disable=protected-access return estimator
def add_graph_regularization(estimator, embedding_fn, optimizer_fn=None, graph_reg_config=None): """Adds graph regularization to a `tf.estimator.Estimator`. Args: estimator: An object of type `tf.estimator.Estimator`. embedding_fn: A function that accepts the input layer (dictionary of feature names and corresponding batched tensor values) as its first argument and an instance of `tf.estimator.ModeKeys` as its second argument to indicate if the mode is training, evaluation, or prediction, and returns the corresponding embeddings or logits to be used for graph regularization. optimizer_fn: A function that accepts no arguments and returns an instance of `tf.train.Optimizer`. graph_reg_config: An instance of `nsl.configs.GraphRegConfig` that specifies various hyperparameters for graph regularization. Returns: A modified `tf.estimator.Estimator` object with graph regularization incorporated into its loss. """ if not graph_reg_config: graph_reg_config = configs.GraphRegConfig() base_model_fn = estimator._model_fn # pylint: disable=protected-access def graph_reg_model_fn(features, labels, mode, params=None, config=None): """The graph-regularized model function. Args: features: This is the first item returned from the `input_fn` passed to `train`, `evaluate`, and `predict`. This should be a dictionary containing sample features as well as corresponding neighbor features and neighbor weights. labels: This is the second item returned from the `input_fn` passed to `train`, `evaluate`, and `predict`. This should be a single `Tensor` or `dict` of same (for multi-head models). If mode is `tf.estimator.ModeKeys.PREDICT`, `labels=None` will be passed. If the `model_fn`'s signature does not accept `mode`, the `model_fn` must still be able to handle `labels=None`. mode: Optional. Specifies if this is training, evaluation, or prediction. See `tf.estimator.ModeKeys`. params: Optional `dict` of hyperparameters. Will receive what is passed to Estimator in the `params` parameter. This allows users to configure Estimators from hyper parameter tuning. config: Optional `tf.estimator.RunConfig` object. Will receive what is passed to Estimator as its `config` parameter, or a default value. Allows setting up things in the `model_fn` based on configuration such as `num_ps_replicas`, or `model_dir`. Unused currently. Returns: A `tf.EstimatorSpec` whose loss incorporates graph-based regularization. """ # Uses the same variable scope for calculating the original objective and # the graph regularization loss term. with tf.compat.v1.variable_scope( tf.compat.v1.get_variable_scope(), reuse=tf.compat.v1.AUTO_REUSE, auxiliary_name_scope=False): # Extract sample features, neighbor features, and neighbor weights. sample_features, nbr_features, nbr_weights = ( utils.unpack_neighbor_features(features, graph_reg_config.neighbor_config)) # If no 'params' is passed, then it is possible for base_model_fn not to # accept a 'params' argument. See documentation for tf.estimator.Estimator # for additional context. if params: base_spec = base_model_fn(sample_features, labels, mode, params, config) else: base_spec = base_model_fn(sample_features, labels, mode, config) has_nbr_inputs = nbr_weights is not None and nbr_features # Graph regularization happens only if all the following conditions are # satisfied: # - the mode is training # - neighbor inputs exist # - the graph regularization multiplier is greater than zero. # So, return early if any of these conditions is false. if (not has_nbr_inputs or mode != tf.estimator.ModeKeys.TRAIN or graph_reg_config.multiplier <= 0): return base_spec # Compute sample embeddings. sample_embeddings = embedding_fn(sample_features, mode) # Compute the embeddings of the neighbors. nbr_embeddings = embedding_fn(nbr_features, mode) replicated_sample_embeddings = utils.replicate_embeddings( sample_embeddings, graph_reg_config.neighbor_config.max_neighbors) # Compute the distance between the sample embeddings and each of their # corresponding neighbor embeddings. graph_loss = distances.pairwise_distance_wrapper( replicated_sample_embeddings, nbr_embeddings, weights=nbr_weights, distance_config=graph_reg_config.distance_config) total_loss = base_spec.loss + graph_reg_config.multiplier * graph_loss if not optimizer_fn: # Default to Adagrad optimizer, the same as the canned DNNEstimator. optimizer = tf.train.AdagradOptimizer(learning_rate=0.05) else: optimizer = optimizer_fn() final_train_op = optimizer.minimize( loss=total_loss, global_step=tf.compat.v1.train.get_global_step()) return base_spec._replace(loss=total_loss, train_op=final_train_op) # Replaces the model_fn while keeping other fields/methods in the estimator. estimator._model_fn = graph_reg_model_fn # pylint: disable=protected-access return estimator