Update generated Python Op docs.
Change: 136558601
This commit is contained in:
A. Unique TensorFlower
TensorFlower Gardener
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commit
273f59a19a
tensorflow/g3doc/api_docs/python
@ -20217,8 +20217,8 @@ Additional documentation from `TransformedDistribution`:
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##### <b>`condition_kwargs`</b>:
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* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution.
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* <b>`bijector_kwargs`</b>: Python dictionary of arg names/values forwarded to the bijector.
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* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution.
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##### Args:
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@ -20335,8 +20335,8 @@ Additional documentation from `TransformedDistribution`:
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##### <b>`condition_kwargs`</b>:
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* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution.
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* <b>`bijector_kwargs`</b>: Python dictionary of arg names/values forwarded to the bijector.
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* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution.
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##### Args:
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@ -20419,8 +20419,8 @@ Implements `(log o p o g^{-1})(y) + (log o det o J o g^{-1})(y)`,
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##### <b>`condition_kwargs`</b>:
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* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution.
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* <b>`bijector_kwargs`</b>: Python dictionary of arg names/values forwarded to the bijector.
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* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution.
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##### Args:
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@ -20458,8 +20458,8 @@ Additional documentation from `TransformedDistribution`:
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##### <b>`condition_kwargs`</b>:
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* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution.
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* <b>`bijector_kwargs`</b>: Python dictionary of arg names/values forwarded to the bijector.
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* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution.
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##### Args:
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@ -20611,8 +20611,8 @@ Implements `p(g^{-1}(y)) det|J(g^{-1}(y))|`, where `g^{-1}` is the
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##### <b>`condition_kwargs`</b>:
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* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution.
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* <b>`bijector_kwargs`</b>: Python dictionary of arg names/values forwarded to the bijector.
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* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution.
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##### Args:
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@ -20665,8 +20665,8 @@ Samples from the base distribution and then passes through
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##### <b>`condition_kwargs`</b>:
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* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution.
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* <b>`bijector_kwargs`</b>: Python dictionary of arg names/values forwarded to the bijector.
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* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution.
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##### Args:
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@ -20714,8 +20714,8 @@ Additional documentation from `TransformedDistribution`:
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##### <b>`condition_kwargs`</b>:
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* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution.
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* <b>`bijector_kwargs`</b>: Python dictionary of arg names/values forwarded to the bijector.
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* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution.
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##### Args:
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@ -182,8 +182,8 @@ Additional documentation from `TransformedDistribution`:
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##### <b>`condition_kwargs`</b>:
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* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution.
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* <b>`bijector_kwargs`</b>: Python dictionary of arg names/values forwarded to the bijector.
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* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution.
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##### Args:
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@ -300,8 +300,8 @@ Additional documentation from `TransformedDistribution`:
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##### <b>`condition_kwargs`</b>:
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* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution.
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* <b>`bijector_kwargs`</b>: Python dictionary of arg names/values forwarded to the bijector.
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* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution.
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##### Args:
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@ -384,8 +384,8 @@ Implements `(log o p o g^{-1})(y) + (log o det o J o g^{-1})(y)`,
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##### <b>`condition_kwargs`</b>:
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* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution.
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* <b>`bijector_kwargs`</b>: Python dictionary of arg names/values forwarded to the bijector.
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* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution.
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##### Args:
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@ -423,8 +423,8 @@ Additional documentation from `TransformedDistribution`:
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##### <b>`condition_kwargs`</b>:
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* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution.
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* <b>`bijector_kwargs`</b>: Python dictionary of arg names/values forwarded to the bijector.
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* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution.
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##### Args:
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@ -576,8 +576,8 @@ Implements `p(g^{-1}(y)) det|J(g^{-1}(y))|`, where `g^{-1}` is the
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##### <b>`condition_kwargs`</b>:
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* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution.
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* <b>`bijector_kwargs`</b>: Python dictionary of arg names/values forwarded to the bijector.
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* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution.
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##### Args:
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@ -630,8 +630,8 @@ Samples from the base distribution and then passes through
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##### <b>`condition_kwargs`</b>:
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* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution.
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* <b>`bijector_kwargs`</b>: Python dictionary of arg names/values forwarded to the bijector.
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* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution.
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##### Args:
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@ -679,8 +679,8 @@ Additional documentation from `TransformedDistribution`:
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##### <b>`condition_kwargs`</b>:
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* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution.
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* <b>`bijector_kwargs`</b>: Python dictionary of arg names/values forwarded to the bijector.
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* <b>`distribution_kwargs`</b>: Python dictionary of arg names/values forwarded to the distribution.
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##### Args:
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@ -2,19 +2,27 @@
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Computes a 1-D convolution given 3-D input and filter tensors.
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Given an input tensor of shape [batch, in_width, in_channels]
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Given an input tensor of shape
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[batch, in_width, in_channels]
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if data_format is "NHWC", or
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[batch, in_channels, in_width]
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if data_format is "NCHW",
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and a filter / kernel tensor of shape
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[filter_width, in_channels, out_channels], this op reshapes
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the arguments to pass them to conv2d to perform the equivalent
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convolution operation.
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Internally, this op reshapes the input tensors and invokes
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`tf.nn.conv2d`. A tensor of shape [batch, in_width, in_channels]
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is reshaped to [batch, 1, in_width, in_channels], and the filter
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is reshaped to [1, filter_width, in_channels, out_channels].
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The result is then reshaped back to [batch, out_width, out_channels]
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(where out_width is a function of the stride and padding as in
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conv2d) and returned to the caller.
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Internally, this op reshapes the input tensors and invokes `tf.nn.conv2d`.
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For example, if `data_format` does not start with "NC", a tensor of shape
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[batch, in_width, in_channels]
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is reshaped to
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[batch, 1, in_width, in_channels],
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and the filter is reshaped to
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[1, filter_width, in_channels, out_channels].
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The result is then reshaped back to
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[batch, out_width, out_channels]
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(where out_width is a function of the stride and padding as in conv2d) and
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returned to the caller.
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##### Args:
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@ -35,3 +43,8 @@ conv2d) and returned to the caller.
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A `Tensor`. Has the same type as input.
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##### Raises:
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* <b>`ValueError`</b>: if `data_format` is invalid.
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@ -1,4 +1,4 @@
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### `tf.nn.convolution(input, filter, padding, strides=None, dilation_rate=None, name=None)` {#convolution}
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### `tf.nn.convolution(input, filter, padding, strides=None, dilation_rate=None, name=None, data_format=None)` {#convolution}
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Computes sums of N-D convolutions (actually cross-correlation).
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@ -8,7 +8,8 @@ convolution, based on the French word "trous" meaning holes in English) via
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the optional `dilation_rate` parameter. Currently, however, output striding
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is not supported for atrous convolutions.
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Specifically, given rank (N+2) `input` Tensor of shape
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Specifically, in the case that `data_format` does not start with "NC", given
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a rank (N+2) `input` Tensor of shape
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[num_batches,
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input_spatial_shape[0],
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@ -33,25 +34,36 @@ position (x[0], ..., x[N-1]):
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sum_{z[0], ..., z[N-1], q}
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filters[z[0], ..., z[N-1], q, k] *
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filter[z[0], ..., z[N-1], q, k] *
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padded_input[b,
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x[0]*strides[0] + dilation_rate[0]*z[0],
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...,
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x[N-1]*strides[N-1] + dilation_rate[N-1]*z[N-1],
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q],
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q]
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where `padded_input` is obtained by zero padding the input using an effective
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spatial filter shape of `(spatial_filter_shape-1) * dilation_rate + 1` and
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output striding `strides` as described in the
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[comment here](https://www.tensorflow.org/api_docs/python/nn.html#convolution).
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In the case that `data_format` does start with `"NC"`, the `input` and output
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(but not the `filter`) are simply transposed as follows:
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convolution(input, data_format, **kwargs) =
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tf.transpose(convolution(tf.transpose(input, [0] + range(2,N+2) + [1]),
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**kwargs),
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[0, N+1] + range(1, N+1))
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It is required that 1 <= N <= 3.
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##### Args:
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* <b>`input`</b>: An N-D `Tensor` of type `T`, of shape
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`[batch_size] + input_spatial_shape + [in_channels]`.
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`[batch_size] + input_spatial_shape + [in_channels]` if data_format does
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not start with "NC" (default), or
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`[batch_size, in_channels] + input_spatial_shape` if data_format starts
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with "NC".
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* <b>`filter`</b>: An N-D `Tensor` with the same type as `input` and shape
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`spatial_filter_shape + [in_channels, out_channels]`.
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* <b>`padding`</b>: A string, either `"VALID"` or `"SAME"`. The padding algorithm.
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@ -67,13 +79,24 @@ It is required that 1 <= N <= 3.
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filter in each spatial dimension i. If any value of dilation_rate is > 1,
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then all values of strides must be 1.
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* <b>`name`</b>: Optional name for the returned tensor.
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* <b>`data_format`</b>: A string or None. Specifies whether the channel dimension of
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the `input` and output is the last dimension (default, or if `data_format`
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does not start with "NC"), or the second dimension (if `data_format`
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starts with "NC"). For N=1, the valid values are "NWC" (default) and
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"NCW". For N=2, the valid values are "NHWC" (default) and "NCHW". For
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N=3, the valid value is "NDHWC".
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##### Returns:
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A `Tensor` with the same type as `value` of shape
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A `Tensor` with the same type as `input` of shape
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`[batch_size] + output_spatial_shape + [out_channels]`,
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`[batch_size] + output_spatial_shape + [out_channels]`
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if data_format is None or does not start with "NC", or
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`[batch_size, out_channels] + output_spatial_shape`
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if data_format starts with "NC",
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where `output_spatial_shape` depends on the value of `padding`.
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If padding == "SAME":
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@ -88,6 +111,6 @@ It is required that 1 <= N <= 3.
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##### Raises:
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* <b>`ValueError`</b>: If input/output depth does not match `filter` shape, or if
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padding is other than `"VALID"` or `"SAME"`.
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* <b>`ValueError`</b>: If input/output depth does not match `filter` shape, if padding
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is other than `"VALID"` or `"SAME"`, or if data_format is invalid.
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@ -1,8 +1,8 @@
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### `tf.nn.pool(input, window_shape, pooling_type, padding, dilation_rate=None, strides=None, name=None)` {#pool}
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### `tf.nn.pool(input, window_shape, pooling_type, padding, dilation_rate=None, strides=None, name=None, data_format=None)` {#pool}
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Performs an N-D pooling operation.
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Computes for
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In the case that `data_format` does not start with "NC", computes for
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0 <= b < batch_size,
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0 <= x[i] < output_spatial_shape[i],
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0 <= c < num_channels:
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@ -20,12 +20,22 @@ and pad_before is defined based on the value of `padding` as described in the
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[comment here](https://www.tensorflow.org/api_docs/python/nn.html#convolution).
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The reduction never includes out-of-bounds positions.
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In the case that `data_format` starts with `"NC"`, the `input` and output are
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simply transposed as follows:
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pool(input, data_format, **kwargs) =
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tf.transpose(pool(tf.transpose(input, [0] + range(2,N+2) + [1]),
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**kwargs),
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[0, N+1] + range(1, N+1))
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##### Args:
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* <b>`input`</b>: Tensor of rank N+2, of shape
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[batch_size] + input_spatial_shape + [num_channels].
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Pooling happens over the spatial dimensions only.
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`[batch_size] + input_spatial_shape + [num_channels]` if data_format does
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not start with "NC" (default), or
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`[batch_size, num_channels] + input_spatial_shape` if data_format starts
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with "NC". Pooling happens over the spatial dimensions only.
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* <b>`window_shape`</b>: Sequence of N ints >= 1.
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* <b>`pooling_type`</b>: Specifies pooling operation, must be "AVG" or "MAX".
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* <b>`padding`</b>: The padding algorithm, must be "SAME" or "VALID".
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@ -37,11 +47,23 @@ The reduction never includes out-of-bounds positions.
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If any value of strides is > 1, then all values of dilation_rate must be
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1.
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* <b>`name`</b>: Optional. Name of the op.
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* <b>`data_format`</b>: A string or None. Specifies whether the channel dimension of
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the `input` and output is the last dimension (default, or if `data_format`
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does not start with "NC"), or the second dimension (if `data_format`
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starts with "NC"). For N=1, the valid values are "NWC" (default) and
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"NCW". For N=2, the valid values are "NHWC" (default) and "NCHW". For
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N=3, the valid value is "NDHWC".
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##### Returns:
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Tensor of rank N+2, of shape
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[batch_size] + output_spatial_shape + [num_channels],
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[batch_size] + output_spatial_shape + [num_channels]
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if data_format is None or does not start with "NC", or
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[batch_size, num_channels] + output_spatial_shape
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if data_format starts with "NC",
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where `output_spatial_shape` depends on the value of padding:
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If padding = "SAME":
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@ -309,7 +309,7 @@ concatenated.
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- - -
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### `tf.nn.convolution(input, filter, padding, strides=None, dilation_rate=None, name=None)` {#convolution}
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### `tf.nn.convolution(input, filter, padding, strides=None, dilation_rate=None, name=None, data_format=None)` {#convolution}
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Computes sums of N-D convolutions (actually cross-correlation).
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@ -319,7 +319,8 @@ convolution, based on the French word "trous" meaning holes in English) via
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the optional `dilation_rate` parameter. Currently, however, output striding
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is not supported for atrous convolutions.
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Specifically, given rank (N+2) `input` Tensor of shape
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Specifically, in the case that `data_format` does not start with "NC", given
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a rank (N+2) `input` Tensor of shape
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[num_batches,
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input_spatial_shape[0],
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@ -344,25 +345,36 @@ position (x[0], ..., x[N-1]):
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sum_{z[0], ..., z[N-1], q}
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filters[z[0], ..., z[N-1], q, k] *
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filter[z[0], ..., z[N-1], q, k] *
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padded_input[b,
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x[0]*strides[0] + dilation_rate[0]*z[0],
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...,
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x[N-1]*strides[N-1] + dilation_rate[N-1]*z[N-1],
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q],
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q]
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where `padded_input` is obtained by zero padding the input using an effective
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spatial filter shape of `(spatial_filter_shape-1) * dilation_rate + 1` and
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output striding `strides` as described in the
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[comment here](https://www.tensorflow.org/api_docs/python/nn.html#convolution).
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In the case that `data_format` does start with `"NC"`, the `input` and output
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(but not the `filter`) are simply transposed as follows:
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convolution(input, data_format, **kwargs) =
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tf.transpose(convolution(tf.transpose(input, [0] + range(2,N+2) + [1]),
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**kwargs),
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[0, N+1] + range(1, N+1))
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It is required that 1 <= N <= 3.
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##### Args:
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* <b>`input`</b>: An N-D `Tensor` of type `T`, of shape
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`[batch_size] + input_spatial_shape + [in_channels]`.
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`[batch_size] + input_spatial_shape + [in_channels]` if data_format does
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not start with "NC" (default), or
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`[batch_size, in_channels] + input_spatial_shape` if data_format starts
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with "NC".
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* <b>`filter`</b>: An N-D `Tensor` with the same type as `input` and shape
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`spatial_filter_shape + [in_channels, out_channels]`.
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* <b>`padding`</b>: A string, either `"VALID"` or `"SAME"`. The padding algorithm.
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@ -378,13 +390,24 @@ It is required that 1 <= N <= 3.
|
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filter in each spatial dimension i. If any value of dilation_rate is > 1,
|
||||
then all values of strides must be 1.
|
||||
* <b>`name`</b>: Optional name for the returned tensor.
|
||||
* <b>`data_format`</b>: A string or None. Specifies whether the channel dimension of
|
||||
the `input` and output is the last dimension (default, or if `data_format`
|
||||
does not start with "NC"), or the second dimension (if `data_format`
|
||||
starts with "NC"). For N=1, the valid values are "NWC" (default) and
|
||||
"NCW". For N=2, the valid values are "NHWC" (default) and "NCHW". For
|
||||
N=3, the valid value is "NDHWC".
|
||||
|
||||
##### Returns:
|
||||
|
||||
A `Tensor` with the same type as `value` of shape
|
||||
A `Tensor` with the same type as `input` of shape
|
||||
|
||||
`[batch_size] + output_spatial_shape + [out_channels]`,
|
||||
`[batch_size] + output_spatial_shape + [out_channels]`
|
||||
|
||||
if data_format is None or does not start with "NC", or
|
||||
|
||||
`[batch_size, out_channels] + output_spatial_shape`
|
||||
|
||||
if data_format starts with "NC",
|
||||
where `output_spatial_shape` depends on the value of `padding`.
|
||||
|
||||
If padding == "SAME":
|
||||
@ -399,8 +422,8 @@ It is required that 1 <= N <= 3.
|
||||
##### Raises:
|
||||
|
||||
|
||||
* <b>`ValueError`</b>: If input/output depth does not match `filter` shape, or if
|
||||
padding is other than `"VALID"` or `"SAME"`.
|
||||
* <b>`ValueError`</b>: If input/output depth does not match `filter` shape, if padding
|
||||
is other than `"VALID"` or `"SAME"`, or if data_format is invalid.
|
||||
|
||||
|
||||
- - -
|
||||
@ -710,19 +733,27 @@ deconvolution.
|
||||
|
||||
Computes a 1-D convolution given 3-D input and filter tensors.
|
||||
|
||||
Given an input tensor of shape [batch, in_width, in_channels]
|
||||
Given an input tensor of shape
|
||||
[batch, in_width, in_channels]
|
||||
if data_format is "NHWC", or
|
||||
[batch, in_channels, in_width]
|
||||
if data_format is "NCHW",
|
||||
and a filter / kernel tensor of shape
|
||||
[filter_width, in_channels, out_channels], this op reshapes
|
||||
the arguments to pass them to conv2d to perform the equivalent
|
||||
convolution operation.
|
||||
|
||||
Internally, this op reshapes the input tensors and invokes
|
||||
`tf.nn.conv2d`. A tensor of shape [batch, in_width, in_channels]
|
||||
is reshaped to [batch, 1, in_width, in_channels], and the filter
|
||||
is reshaped to [1, filter_width, in_channels, out_channels].
|
||||
The result is then reshaped back to [batch, out_width, out_channels]
|
||||
(where out_width is a function of the stride and padding as in
|
||||
conv2d) and returned to the caller.
|
||||
Internally, this op reshapes the input tensors and invokes `tf.nn.conv2d`.
|
||||
For example, if `data_format` does not start with "NC", a tensor of shape
|
||||
[batch, in_width, in_channels]
|
||||
is reshaped to
|
||||
[batch, 1, in_width, in_channels],
|
||||
and the filter is reshaped to
|
||||
[1, filter_width, in_channels, out_channels].
|
||||
The result is then reshaped back to
|
||||
[batch, out_width, out_channels]
|
||||
(where out_width is a function of the stride and padding as in conv2d) and
|
||||
returned to the caller.
|
||||
|
||||
##### Args:
|
||||
|
||||
@ -743,6 +774,11 @@ conv2d) and returned to the caller.
|
||||
|
||||
A `Tensor`. Has the same type as input.
|
||||
|
||||
##### Raises:
|
||||
|
||||
|
||||
* <b>`ValueError`</b>: if `data_format` is invalid.
|
||||
|
||||
|
||||
- - -
|
||||
|
||||
@ -1116,11 +1152,11 @@ For more details on fractional max pooling, see this paper:
|
||||
|
||||
- - -
|
||||
|
||||
### `tf.nn.pool(input, window_shape, pooling_type, padding, dilation_rate=None, strides=None, name=None)` {#pool}
|
||||
### `tf.nn.pool(input, window_shape, pooling_type, padding, dilation_rate=None, strides=None, name=None, data_format=None)` {#pool}
|
||||
|
||||
Performs an N-D pooling operation.
|
||||
|
||||
Computes for
|
||||
In the case that `data_format` does not start with "NC", computes for
|
||||
0 <= b < batch_size,
|
||||
0 <= x[i] < output_spatial_shape[i],
|
||||
0 <= c < num_channels:
|
||||
@ -1138,12 +1174,22 @@ and pad_before is defined based on the value of `padding` as described in the
|
||||
[comment here](https://www.tensorflow.org/api_docs/python/nn.html#convolution).
|
||||
The reduction never includes out-of-bounds positions.
|
||||
|
||||
In the case that `data_format` starts with `"NC"`, the `input` and output are
|
||||
simply transposed as follows:
|
||||
|
||||
pool(input, data_format, **kwargs) =
|
||||
tf.transpose(pool(tf.transpose(input, [0] + range(2,N+2) + [1]),
|
||||
**kwargs),
|
||||
[0, N+1] + range(1, N+1))
|
||||
|
||||
##### Args:
|
||||
|
||||
|
||||
* <b>`input`</b>: Tensor of rank N+2, of shape
|
||||
[batch_size] + input_spatial_shape + [num_channels].
|
||||
Pooling happens over the spatial dimensions only.
|
||||
`[batch_size] + input_spatial_shape + [num_channels]` if data_format does
|
||||
not start with "NC" (default), or
|
||||
`[batch_size, num_channels] + input_spatial_shape` if data_format starts
|
||||
with "NC". Pooling happens over the spatial dimensions only.
|
||||
* <b>`window_shape`</b>: Sequence of N ints >= 1.
|
||||
* <b>`pooling_type`</b>: Specifies pooling operation, must be "AVG" or "MAX".
|
||||
* <b>`padding`</b>: The padding algorithm, must be "SAME" or "VALID".
|
||||
@ -1155,11 +1201,23 @@ The reduction never includes out-of-bounds positions.
|
||||
If any value of strides is > 1, then all values of dilation_rate must be
|
||||
1.
|
||||
* <b>`name`</b>: Optional. Name of the op.
|
||||
* <b>`data_format`</b>: A string or None. Specifies whether the channel dimension of
|
||||
the `input` and output is the last dimension (default, or if `data_format`
|
||||
does not start with "NC"), or the second dimension (if `data_format`
|
||||
starts with "NC"). For N=1, the valid values are "NWC" (default) and
|
||||
"NCW". For N=2, the valid values are "NHWC" (default) and "NCHW". For
|
||||
N=3, the valid value is "NDHWC".
|
||||
|
||||
##### Returns:
|
||||
|
||||
Tensor of rank N+2, of shape
|
||||
[batch_size] + output_spatial_shape + [num_channels],
|
||||
[batch_size] + output_spatial_shape + [num_channels]
|
||||
|
||||
if data_format is None or does not start with "NC", or
|
||||
|
||||
[batch_size, num_channels] + output_spatial_shape
|
||||
|
||||
if data_format starts with "NC",
|
||||
where `output_spatial_shape` depends on the value of padding:
|
||||
|
||||
If padding = "SAME":
|
||||
|
||||
Loading…
Reference in New Issue
Block a user