tf.estimator.DNNEstimator

An estimator for TensorFlow DNN models with user-specified head.

Inherits From: Estimator

Example:

sparse_feature_a = sparse_column_with_hash_bucket(...)
sparse_feature_b = sparse_column_with_hash_bucket(...)

sparse_feature_a_emb = embedding_column(sparse_id_column=sparse_feature_a,
                                        ...)
sparse_feature_b_emb = embedding_column(sparse_id_column=sparse_feature_b,
                                        ...)

estimator = tf.estimator.DNNEstimator(
    head=tf.estimator.MultiLabelHead(n_classes=3),
    feature_columns=[sparse_feature_a_emb, sparse_feature_b_emb],
    hidden_units=[1024, 512, 256])

# Or estimator using the ProximalAdagradOptimizer optimizer with
# regularization.
estimator = tf.estimator.DNNEstimator(
    head=tf.estimator.MultiLabelHead(n_classes=3),
    feature_columns=[sparse_feature_a_emb, sparse_feature_b_emb],
    hidden_units=[1024, 512, 256],
    optimizer=tf.compat.v1.train.ProximalAdagradOptimizer(
      learning_rate=0.1,
      l1_regularization_strength=0.001
    ))

# Or estimator using an optimizer with a learning rate decay.
estimator = tf.estimator.DNNEstimator(
    head=tf.estimator.MultiLabelHead(n_classes=3),
    feature_columns=[sparse_feature_a_emb, sparse_feature_b_emb],
    hidden_units=[1024, 512, 256],
    optimizer=lambda: tf.keras.optimizers.Adam(
        learning_rate=tf.compat.v1.train.exponential_decay(
            learning_rate=0.1,
            global_step=tf.compat.v1.train.get_global_step(),
            decay_steps=10000,
            decay_rate=0.96))

# Or estimator with warm-starting from a previous checkpoint.
estimator = tf.estimator.DNNEstimator(
    head=tf.estimator.MultiLabelHead(n_classes=3),
    feature_columns=[sparse_feature_a_emb, sparse_feature_b_emb],
    hidden_units=[1024, 512, 256],
    warm_start_from="/path/to/checkpoint/dir")

# Input builders
def input_fn_train:
  # Returns tf.data.Dataset of (x, y) tuple where y represents label's class
  # index.
  pass
def input_fn_eval:
  # Returns tf.data.Dataset of (x, y) tuple where y represents label's class
  # index.
  pass
def input_fn_predict:
  # Returns tf.data.Dataset of (x, None) tuple.
  pass
estimator.train(input_fn=input_fn_train)
metrics = estimator.evaluate(input_fn=input_fn_eval)
predictions = estimator.predict(input_fn=input_fn_predict)

Input of train and evaluate should have following features, otherwise there will be a KeyError:

  • if weight_column is not None, a feature with key=weight_column whose value is a Tensor.
  • for each column in feature_columns:
    • if column is a CategoricalColumn, a feature with key=column.name whose value is a SparseTensor.
    • if column is a WeightedCategoricalColumn, two features: the first with key the id column name, the second with key the weight column name. Both features' value must be a SparseTensor.
    • if column is a DenseColumn, a feature with key=column.name whose value is a Tensor.

Loss and predicted output are determined by the specified head.

head A _Head instance constructed with a method such as tf.contrib.estimator.multi_label_head.
hidden_units Iterable of number hidden units per layer. All layers are fully connected. Ex. [64, 32] means first layer has 64 nodes and second one has 32.
feature_columns An iterable containing all the feature columns used by the model. All items in the set should be instances of classes derived from _FeatureColumn.
model_dir Directory to save model parameters, graph and etc. This can also be used to load checkpoints from the directory into a estimator to continue training a previously saved model.
optimizer An instance of tf.keras.optimizers.* used to train the model. Can also be a string (one of 'Adagrad', 'Adam', 'Ftrl', 'RMSProp', SGD'), or callable. Defaults to Adagrad optimizer.
activation_fn Activation function applied to each layer. If None, will use tf.nn.relu.
dropout When not None, the probability we will drop out a given coordinate.
config RunConfig object to configure the runtime settings.
warm_start_from A string filepath to a checkpoint to warm-start from, or a WarmStartSettings object to fully configure warm-starting. If the string filepath is provided instead of a WarmStartSettings, then all weights are warm-started, and it is assumed that vocabularies and Tensor names are unchanged.
batch_norm Whether to use batch normalization after each hidden layer.

Eager Compatibility

Estimators can be used while eager execution is enabled. Note that input_fn and all hooks are executed inside a graph context, so they have to be written to be compatible with graph mode. Note that input_fn code using tf.data generally works in both graph and eager modes.

config

export_savedmodel

model_dir

model_fn Returns the model_fn which is bound to self.params.
params

Methods

eval_dir

View source

Shows the directory name where evaluation metrics are dumped.

Args
name Name of the evaluation if user needs to run multiple evaluations on different data sets, such as on training data vs test data. Metrics for different evaluations are saved in separate folders, and appear separately in tensorboard.

Returns
A string which is the path of directory contains evaluation metrics.

evaluate

View source

Evaluates the model given evaluation data input_fn.

For each step, calls input_fn, which returns one batch of data. Evaluates until:

Args
input_fn A function that constructs the input data for evaluation. See Premade Estimators for more information. The function should construct and return one of the following:

  • A tf.data.Dataset object: Outputs of Dataset object must be a tuple (features, labels) with same constraints as below.
  • A tuple (features, labels): Where features is a tf.Tensor or a dictionary of string feature name to Tensor and labels is a Tensor or a dictionary of string label name to Tensor. Both features and labels are consumed by model_fn. They should satisfy the expectation of model_fn from inputs.
steps Number of steps for which to evaluate model. If None, evaluates until input_fn raises an end-of-input exception.
hooks List of tf.train.SessionRunHook subclass instances. Used for callbacks inside the evaluation call.
checkpoint_path Path of a specific checkpoint to evaluate. If None, the latest checkpoint in model_dir is used. If there are no checkpoints in model_dir, evaluation is run with newly initialized Variables instead of ones restored from checkpoint.
name Name of the evaluation if user needs to run multiple evaluations on different data sets, such as on training data vs test data. Metrics for different evaluations are saved in separate folders, and appear separately in tensorboard.

Returns
A dict containing the evaluation metrics specified in model_fn keyed by name, as well as an entry global_step which contains the value of the global step for which this evaluation was performed. For canned estimators, the dict contains the loss (mean loss per mini-batch) and the average_loss (mean loss per sample). Canned classifiers also return the accuracy. Canned regressors also return the label/mean and the prediction/mean.

Raises
ValueError If steps <= 0.

experimental_export_all_saved_models

View source

Exports a SavedModel with tf.MetaGraphDefs for each requested mode.

For each mode passed in via the input_receiver_fn_map, this method builds a new graph by calling the input_receiver_fn to obtain feature and label Tensors. Next, this method calls the Estimator's model_fn in the passed mode to generate the model graph based on those features and labels, and restores the given checkpoint (or, lacking that, the most recent checkpoint) into the graph. Only one of the modes is used for saving variables to the SavedModel (order of preference: tf.estimator.ModeKeys.TRAIN,