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tf.estimator.DNNClassifier

A classifier for TensorFlow DNN models.

Inherits From: Estimator, Estimator

Used in the notebooks

Used in the tutorials

Example:

categorical_feature_a = categorical_column_with_hash_bucket(...)
categorical_feature_b = categorical_column_with_hash_bucket(...)

categorical_feature_a_emb = embedding_column(
    categorical_column=categorical_feature_a, ...)
categorical_feature_b_emb = embedding_column(
    categorical_column=categorical_feature_b, ...)

estimator = tf.estimator.DNNClassifier(
    feature_columns=[categorical_feature_a_emb, categorical_feature_b_emb],
    hidden_units=[1024, 512, 256])

# Or estimator using the ProximalAdagradOptimizer optimizer with
# regularization.
estimator = tf.estimator.DNNClassifier(
    feature_columns=[categorical_feature_a_emb, categorical_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.DNNClassifier(
    feature_columns=[categorical_feature_a_emb, categorical_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.DNNClassifier(
    feature_columns=[categorical_feature_a_emb, categorical_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