experiments/STT-tensorflow
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Merge pull request from j-bernardi:patch-1

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PiperOrigin-RevId: 347426920
Change-Id: I6ff73e3f55826ba966c1f287be9771d3bd945209
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TensorFlower Gardener 2020-12-14 11:17:03 -08:00
commit 89ac5d4c81
1 changed files with 9 additions and 5 deletions

14
tensorflow/python/keras/losses.py
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@ -658,9 +658,9 @@ class CategoricalCrossentropy(LossFunctionWrapper):
default, we assume that `y_pred` encodes a probability distribution.
**Note - Using from_logits=True is more numerically stable.**
label_smoothing: Float in [0, 1]. When > 0, label values are smoothed,
meaning the confidence on label values are relaxed. e.g.
`label_smoothing=0.2` means that we will use a value of `0.1` for label
`0` and `0.9` for label `1`"
meaning the confidence on label values are relaxed. For example, if
`0.1`, use `0.1 / num_classes` for non-target labels and
`0.9 + 0.1 / num_classes` for target labels.
reduction: (Optional) Type of `tf.keras.losses.Reduction` to apply to
loss. Default value is `AUTO`. `AUTO` indicates that the reduction
option will be determined by the usage context. For almost all cases
@ -1526,7 +1526,9 @@ def categorical_crossentropy(y_true,
y_pred: Tensor of predicted targets.
from_logits: Whether `y_pred` is expected to be a logits tensor. By default,
we assume that `y_pred` encodes a probability distribution.
label_smoothing: Float in [0, 1]. If > `0` then smooth the labels.
label_smoothing: Float in [0, 1]. If > `0` then smooth the labels. For
example, if `0.1`, use `0.1 / num_classes` for non-target labels
and `0.9 + 0.1 / num_classes` for target labels.
Returns:
Categorical crossentropy loss value.
@ -1597,7 +1599,9 @@ def binary_crossentropy(y_true, y_pred, from_logits=False, label_smoothing=0):
y_pred: The predicted values. shape = `[batch_size, d0, .. dN]`.
from_logits: Whether `y_pred` is expected to be a logits tensor. By default,
we assume that `y_pred` encodes a probability distribution.
label_smoothing: Float in [0, 1]. If > `0` then smooth the labels.
label_smoothing: Float in [0, 1]. If > `0` then smooth the labels by
squeezing them towards 0.5 That is, using `1. - 0.5 * label_smoothing`
for the target class and `0.5 * label_smoothing` for the non-target class.
Returns:
Binary crossentropy loss value. shape = `[batch_size, d0, .. dN-1]`.