tf.contrib.losses.softmax_cross_entropy()

tf.contrib.losses.softmax_cross_entropy(logits, onehot_labels, weight=1.0, label_smoothing=0, scope=None)

Creates a cross-entropy loss using tf.nn.softmax_cross_entropy_with_logits.

weight acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If weight is a tensor of size [batch_size], then the loss weights apply to each corresponding sample.

If label_smoothing is nonzero, smooth the labels towards 1/num_classes: new_onehot_labels = onehot_labels * (1 - label_smoothing) + label_smoothing / num_classes

Args:

• logits: [batch_size, num_classes] logits outputs of the network .
• onehot_labels: [batch_size, num_classes] target one_hot_encoded labels.
• weight: Coefficients for the loss. The tensor must be a scalar or a tensor of shape [batch_size].
• label_smoothing: If greater than 0 then smooth the labels.
• scope: the scope for the operations performed in computing the loss.

Returns:

A scalar Tensor representing the loss value.

Raises:

• ValueError: If the shape of logits doesn't match that of onehot_labels or if the shape of weight is invalid or if weight is None.