tf.contrib.losses.sigmoid_cross_entropy( logits, multi_class_labels, weights=1.0, label_smoothing=0, scope=None )
Creates a cross-entropy loss using tf.nn.sigmoid_cross_entropy_with_logits. (deprecated)
THIS FUNCTION IS DEPRECATED. It will be removed after 2016-12-30. Instructions for updating: Use tf.losses.sigmoid_cross_entropy instead. Note that the order of the predictions and labels arguments has been changed.
weights acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If
weights is a tensor of size [
batch_size], then the loss weights apply to each corresponding sample.
label_smoothing is nonzero, smooth the labels towards 1/2:
new_multiclass_labels = multiclass_labels * (1 - label_smoothing) + 0.5 * label_smoothing
logits: [batch_size, num_classes] logits outputs of the network .
multi_class_labels: [batch_size, num_classes] labels in (0, 1).
weights: Coefficients for the loss. The tensor must be a scalar, a tensor of shape [batch_size] or shape [batch_size, num_classes].
label_smoothing: If greater than 0 then smooth the labels.
scope: The scope for the operations performed in computing the loss.
Tensor representing the loss value.
ValueError: If the shape of
logitsdoesn't match that of
multi_class_labelsor if the shape of
weightsis invalid, or if
© 2018 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.