tf.contrib.losses.sigmoid_cross_entropy
Stay organized with collections
Save and categorize content based on your preferences.
Creates a cross-entropy loss using tf.nn.sigmoid_cross_entropy_with_logits. (deprecated)
tf.contrib.losses.sigmoid_cross_entropy(
logits, multi_class_labels, weights=1.0, label_smoothing=0, scope=None
)
Warning: 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.
If label_smoothing
is nonzero, smooth the labels towards 1/2:
new_multiclass_labels = multiclass_labels * (1 - label_smoothing)
+ 0.5 * label_smoothing
Args
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.
Returns
A scalar Tensor
representing the loss value.
Raises
ValueError
If the shape of logits
doesn't match that of
multi_class_labels
or if the shape of weights
is invalid, or if
weights
is None.
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License , and code samples are licensed under the Apache 2.0 License . For details, see the Google Developers Site Policies . Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2020-10-01 UTC.
[{
"type": "thumb-down",
"id": "missingTheInformationINeed",
"label":"Missing the information I need"
},{
"type": "thumb-down",
"id": "tooComplicatedTooManySteps",
"label":"Too complicated / too many steps"
},{
"type": "thumb-down",
"id": "outOfDate",
"label":"Out of date"
},{
"type": "thumb-down",
"id": "samplesCodeIssue",
"label":"Samples / code issue"
},{
"type": "thumb-down",
"id": "otherDown",
"label":"Other"
}]
[{
"type": "thumb-up",
"id": "easyToUnderstand",
"label":"Easy to understand"
},{
"type": "thumb-up",
"id": "solvedMyProblem",
"label":"Solved my problem"
},{
"type": "thumb-up",
"id": "otherUp",
"label":"Other"
}]
{"lastModified": "Last updated 2020-10-01 UTC."}
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2020-10-01 UTC."],[],[]]