tf.estimator.DNNLinearCombinedClassifier

An estimator for TensorFlow Linear and DNN joined classification models.

Inherits From: Estimator, Estimator

Example:

numeric_feature = numeric_column(...)
categorical_column_a = categorical_column_with_hash_bucket(...)
categorical_column_b = categorical_column_with_hash_bucket(...)

categorical_feature_a_x_categorical_feature_b = crossed_column(...)
categorical_feature_a_emb = embedding_column(
    categorical_column=categorical_feature_a, ...)
categorical_feature_b_emb = embedding_column(
    categorical_id_column=categorical_feature_b, ...)

estimator = tf.estimator.DNNLinearCombinedClassifier(
    # wide settings
    linear_feature_columns=[categorical_feature_a_x_categorical_feature_b],
    linear_optimizer=tf.keras.optimizers.Ftrl(...),
    # deep settings
    dnn_feature_columns=[
        categorical_feature_a_emb, categorical_feature_b_emb,
        numeric_feature],
    dnn_hidden_units=[1000, 500, 100],
    dnn_optimizer=tf.keras.optimizers.Adagrad(...),
    # warm-start settings
    warm_start_from="/path/to/checkpoint/dir")

# To apply L1 and L2 regularization, you can set dnn_optimizer to:
tf.compat.v1.train.ProximalAdagradOptimizer(
    learning_rate=0.1,
    l1_regularization_strength=0.001,
    l2_regularization_strength=0.001)
# To apply learning rate decay, you can set dnn_optimizer to a callable:
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)
# It is the same for linear_optimizer.

# 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, steps=100)
metrics = estimator.evaluate(input_fn=input_fn_eval, steps=10)
predictions = estimator.predict(input_fn=input_fn_predict)

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

  • for each column in dnn_feature_columns + linear_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 is calculated by using softmax cross entropy.

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.
linear_feature_columns An iterable containing all the feature columns used by linear part of the model. All items in the set must be instances of classes derived from FeatureColumn.
linear_optimizer An instance of tf.keras.optimizers.* used to apply gradients to the linear part of the model. Can also be a string (one of 'Adagrad', 'Adam', 'Ftrl', 'RMSProp', 'SGD'), or callable. Defaults to FTRL optimizer.
dnn_feature_columns An iterable containing all the feature columns used by deep part of the model. All items in the set must be instances of classes derived from FeatureColumn.
dnn_optimizer An instance of tf.keras.optimizers.* used to apply gradients to the deep part of the model. Can also be a string (one of 'Adagrad', 'Adam', 'Ftrl', 'RMSProp', 'SGD'), or callable. Defaults to Adagrad optimizer.
dnn_hidden_units List of hidden units per layer. All layers are fully connected.
dnn_activation_fn Activation function applied to each layer. If None, will use tf.nn.relu.
dnn_dropout When not None, the probability we will drop out a given coordinate.
n_classes Number of label classes. Defaults to 2, namely binary classification. Must be > 1.
weight_column A string or a _NumericColumn created by tf.feature_column.numeric_column defining feature column representing weights. It is used to down weight or boost examples during training. It will be multiplied by the loss of the example. If it is a string, it is used as a key to fetch weight tensor from the features. If it is a _NumericColumn, raw tensor is fetched by key weight_column.key, then weight_column.normalizer_fn is applied o