View source on GitHub

Creates a Head for multi class single label classification. (deprecated)

The Head uses softmax cross entropy loss.

This head expects to be fed integer labels specifying the class index. But if label_keys is specified, then labels must be strings from this vocabulary, and the predicted classes will be strings from the same vocabulary.

n_classes Integer, number of classes, must be >= 2
label_name String, name of the key in label dict. Can be null if label is a tensor (single headed models).
weight_column_name A string defining feature column name representing weights. It is used to down weight or boost examples during training. It will be multiplied by the loss of the example.
enable_centered_bias A bool. If True, estimator will learn a centered bias variable for each class. Rest of the model structure learns the residual after centered bias.
head_name name of the head. If provided, predictions, summary and metrics keys will be suffixed by "/" + head_name and the default variable scope will be head_name.
thresholds thresholds for eval metrics, defaults to [.5]
metric_class_ids List of class IDs for which we should report per-class metrics. Must all be in the range [0, n_classes). Invalid if n_classes is 2.
loss_fn Optional function that takes (labels, logits, weights) as parameter and returns a weighted scalar loss. weights should be optional. See tf.losses
label_keys Optional list of strings with size [n_classes] defining the label vocabulary. Only supported for n_classes > 2.

An instance of Head for multi class classification.

ValueError if n_classes is < 2.
ValueError If metric_class_ids is provided when n_classes is 2.
ValueError If len(label_keys) != n_classes.