tf.estimator.MultiClassHead

Creates a Head for multi class classification.

Inherits From: Head

Uses sparse_softmax_cross_entropy loss.

The head expects logits with shape [D0, D1, ... DN, n_classes]. In many applications, the shape is [batch_size, n_classes].

labels must be a dense Tensor with shape matching logits, namely [D0, D1, ... DN, 1]. If label_vocabulary given, labels must be a string Tensor with values from the vocabulary. If label_vocabulary is not given, labels must be an integer Tensor with values specifying the class index.

If weight_column is specified, weights must be of shape [D0, D1, ... DN], or [D0, D1, ... DN, 1].

The loss is the weighted sum over the input dimensions. Namely, if the input labels have shape [batch_size, 1], the loss is the weighted sum over batch_size.

Also supports custom loss_fn. loss_fn takes (labels, logits) or (labels, logits, features, loss_reduction) as arguments and returns unreduced loss with shape [D0, D1, ... DN, 1]. loss_fn must support integer labels with shape [D0, D1, ... DN, 1]. Namely, the head applies label_vocabulary to the input labels before passing them to loss_fn.

Usage:

n_classes = 3
head = tf.estimator.MultiClassHead(n_classes)
logits = np.array(((10, 0, 0), (0, 10, 0),), dtype=np.float32)
labels = np.array(((1,), (1,)), dtype=np.int64)
features = {'x': np.array(((42,),), dtype=np.int32)}
# expected_loss = sum(cross_entropy(labels, logits)) / batch_size
#               = sum(10, 0) / 2 = 5.
loss = head.loss(labels, logits, features=features)
print('{:.2f}'.format(loss.numpy()))
5.00
eval_metrics = head.metrics()
updated_metrics = head.update_metrics(
  eval_metrics, features, logits, labels)
for k in sorted(updated_metrics):
  print('{} : {:.2f}'.format(k, updated_metrics[k].result().numpy()))
accuracy : 0.50
average_loss : 5.00
preds = head.predictions(logits)
print(preds['logits'])
tf.Tensor(
  [[10.  0.  0.]
   [ 0. 10.  0.]], shape=(2, 3), dtype=float32)

Usage with a canned estimator:

my_head = tf.estimator.MultiClassHead(n_classes=3)
my_estimator = tf.estimator.DNNEstimator(
    head=my_head,
    hidden_units=...,
    feature_columns=...)

It can also be used with a custom model_fn. Example:

def _my_model_fn(features, labels, mode):
  my_head = tf.estimator.MultiClassHead(n_classes=3)
  logits = tf.keras.Model(...)(features)

  return my_head.create_estimator_spec(
      features=features,
      mode=mode,
      labels=labels,
      optimizer=tf.keras.optimizers.Adagrad(lr=0.1),
      logits=logits)

my_estimator = tf.estimator.Estimator(model_fn=_my_model_fn)

n_classes Number of classes, must be greater than 2 (for 2 classes, use BinaryClassHead).
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.
label_vocabulary A list or tuple of strings representing possible label values. If it is not given, that means labels are already encoded as an integer within [0, n_classes). If given, labels must be of string type and have any value in label_vocabulary. Note that errors will be raised if label_vocabulary is not provided but labels are strings. If both n_classes and label_vocabulary are provided, label_vocabulary should contain exactly n_classes items.
loss_reduction One of tf.losses.Reduction except NONE. Decides how to reduce training loss over batch. Defaults to SUM_OVER_BATCH_SIZE, namely weighted sum of losses divided by batch size * label_dimension.
loss_fn Optional loss function.
name Name of the head. If provided, summary and metrics keys will be suffixed by "/" + name. Also used as name_scope when creating ops.

logits_dimension See base_head.Head for details.
loss_reduction See base_head.Head for details.
name See base_head.Head for details.