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Improve formatting of shapes in tf.losses documentation #16921

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Feb 12, 2018
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Improve formatting of shapes in tf.losses documentation #16921

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10 changes: 5 additions & 5 deletions tensorflow/python/ops/losses/losses_impl.py
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Expand Up @@ -377,7 +377,7 @@ def huber_loss(labels, predictions, weights=1.0, delta=1.0, scope=None,

`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 total loss for each sample of the batch is rescaled
`[batch_size]`, then the total loss for each sample of the batch is rescaled
by the corresponding element in the `weights` vector. If the shape of
`weights` matches the shape of `predictions`, then the loss of each
measurable element of `predictions` is scaled by the corresponding value of
Expand Down Expand Up @@ -435,7 +435,7 @@ def log_loss(labels, predictions, weights=1.0, epsilon=1e-7, scope=None,

`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 total loss for each sample of the batch is rescaled
`[batch_size]`, then the total loss for each sample of the batch is rescaled
by the corresponding element in the `weights` vector. If the shape of
`weights` matches the shape of `predictions`, then the loss of each
measurable element of `predictions` is scaled by the corresponding value of
Expand Down Expand Up @@ -502,7 +502,7 @@ def mean_pairwise_squared_error(

`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 total loss for each sample of the batch is rescaled
`[batch_size]`, then the total loss for each sample of the batch is rescaled
by the corresponding element in the `weights` vector.

Args:
Expand Down Expand Up @@ -576,7 +576,7 @@ def mean_squared_error(

`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 total loss for each sample of the batch is rescaled
`[batch_size]`, then the total loss for each sample of the batch is rescaled
by the corresponding element in the `weights` vector. If the shape of
`weights` matches the shape of `predictions`, then the loss of each
measurable element of `predictions` is scaled by the corresponding value of
Expand Down Expand Up @@ -796,7 +796,7 @@ def sparse_softmax_cross_entropy(

`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 shape [`batch_size`], then the loss weights apply to each
tensor of shape `[batch_size]`, then the loss weights apply to each
corresponding sample.

Args:
Expand Down