From 273f59a19ae06fa5c30d44cb0dd54e6655a4b489 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 18 Oct 2016 21:08:42 -0800 Subject: [PATCH] Update generated Python Op docs. Change: 136558601 --- .../api_docs/python/contrib.distributions.md | 14 +-- ...b.distributions.TransformedDistribution.md | 14 +-- .../shard1/tf.nn.conv1d.md | 29 +++-- .../shard8/tf.nn.convolution.md | 41 +++++-- .../shard8/tf.nn.pool.md | 32 +++++- tensorflow/g3doc/api_docs/python/nn.md | 102 ++++++++++++++---- 6 files changed, 174 insertions(+), 58 deletions(-) diff --git a/tensorflow/g3doc/api_docs/python/contrib.distributions.md b/tensorflow/g3doc/api_docs/python/contrib.distributions.md index f0e5da3d982..34ae9e2d941 100644 --- a/tensorflow/g3doc/api_docs/python/contrib.distributions.md +++ b/tensorflow/g3doc/api_docs/python/contrib.distributions.md @@ -20217,8 +20217,8 @@ Additional documentation from `TransformedDistribution`: ##### `condition_kwargs`: -* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. * `bijector_kwargs`: Python dictionary of arg names/values forwarded to the bijector. +* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. ##### Args: @@ -20335,8 +20335,8 @@ Additional documentation from `TransformedDistribution`: ##### `condition_kwargs`: -* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. * `bijector_kwargs`: Python dictionary of arg names/values forwarded to the bijector. +* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. ##### Args: @@ -20419,8 +20419,8 @@ Implements `(log o p o g^{-1})(y) + (log o det o J o g^{-1})(y)`, ##### `condition_kwargs`: -* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. * `bijector_kwargs`: Python dictionary of arg names/values forwarded to the bijector. +* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. ##### Args: @@ -20458,8 +20458,8 @@ Additional documentation from `TransformedDistribution`: ##### `condition_kwargs`: -* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. * `bijector_kwargs`: Python dictionary of arg names/values forwarded to the bijector. +* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. ##### Args: @@ -20611,8 +20611,8 @@ Implements `p(g^{-1}(y)) det|J(g^{-1}(y))|`, where `g^{-1}` is the ##### `condition_kwargs`: -* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. * `bijector_kwargs`: Python dictionary of arg names/values forwarded to the bijector. +* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. ##### Args: @@ -20665,8 +20665,8 @@ Samples from the base distribution and then passes through ##### `condition_kwargs`: -* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. * `bijector_kwargs`: Python dictionary of arg names/values forwarded to the bijector. +* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. ##### Args: @@ -20714,8 +20714,8 @@ Additional documentation from `TransformedDistribution`: ##### `condition_kwargs`: -* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. * `bijector_kwargs`: Python dictionary of arg names/values forwarded to the bijector. +* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. ##### Args: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.TransformedDistribution.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.TransformedDistribution.md index fc76841477a..25a8707b38a 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.TransformedDistribution.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.TransformedDistribution.md @@ -182,8 +182,8 @@ Additional documentation from `TransformedDistribution`: ##### `condition_kwargs`: -* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. * `bijector_kwargs`: Python dictionary of arg names/values forwarded to the bijector. +* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. ##### Args: @@ -300,8 +300,8 @@ Additional documentation from `TransformedDistribution`: ##### `condition_kwargs`: -* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. * `bijector_kwargs`: Python dictionary of arg names/values forwarded to the bijector. +* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. ##### Args: @@ -384,8 +384,8 @@ Implements `(log o p o g^{-1})(y) + (log o det o J o g^{-1})(y)`, ##### `condition_kwargs`: -* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. * `bijector_kwargs`: Python dictionary of arg names/values forwarded to the bijector. +* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. ##### Args: @@ -423,8 +423,8 @@ Additional documentation from `TransformedDistribution`: ##### `condition_kwargs`: -* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. * `bijector_kwargs`: Python dictionary of arg names/values forwarded to the bijector. +* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. ##### Args: @@ -576,8 +576,8 @@ Implements `p(g^{-1}(y)) det|J(g^{-1}(y))|`, where `g^{-1}` is the ##### `condition_kwargs`: -* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. * `bijector_kwargs`: Python dictionary of arg names/values forwarded to the bijector. +* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. ##### Args: @@ -630,8 +630,8 @@ Samples from the base distribution and then passes through ##### `condition_kwargs`: -* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. * `bijector_kwargs`: Python dictionary of arg names/values forwarded to the bijector. +* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. ##### Args: @@ -679,8 +679,8 @@ Additional documentation from `TransformedDistribution`: ##### `condition_kwargs`: -* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. * `bijector_kwargs`: Python dictionary of arg names/values forwarded to the bijector. +* `distribution_kwargs`: Python dictionary of arg names/values forwarded to the distribution. ##### Args: diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.nn.conv1d.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.nn.conv1d.md index f4c93a4328e..d073ce7fb2a 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.nn.conv1d.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.nn.conv1d.md @@ -2,19 +2,27 @@ 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: @@ -35,3 +43,8 @@ conv2d) and returned to the caller. A `Tensor`. Has the same type as input. +##### Raises: + + +* `ValueError`: if `data_format` is invalid. + diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.convolution.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.convolution.md index cff3950ed63..f1ed0e2f534 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.convolution.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.convolution.md @@ -1,4 +1,4 @@ -### `tf.nn.convolution(input, filter, padding, strides=None, dilation_rate=None, name=None)` {#convolution} +### `tf.nn.convolution(input, filter, padding, strides=None, dilation_rate=None, name=None, data_format=None)` {#convolution} Computes sums of N-D convolutions (actually cross-correlation). @@ -8,7 +8,8 @@ convolution, based on the French word "trous" meaning holes in English) via the optional `dilation_rate` parameter. Currently, however, output striding is not supported for atrous convolutions. -Specifically, given rank (N+2) `input` Tensor of shape +Specifically, in the case that `data_format` does not start with "NC", given +a rank (N+2) `input` Tensor of shape [num_batches, input_spatial_shape[0], @@ -33,25 +34,36 @@ position (x[0], ..., x[N-1]): sum_{z[0], ..., z[N-1], q} - filters[z[0], ..., z[N-1], q, k] * + filter[z[0], ..., z[N-1], q, k] * padded_input[b, x[0]*strides[0] + dilation_rate[0]*z[0], ..., x[N-1]*strides[N-1] + dilation_rate[N-1]*z[N-1], - q], + q] where `padded_input` is obtained by zero padding the input using an effective spatial filter shape of `(spatial_filter_shape-1) * dilation_rate + 1` and output striding `strides` as described in the [comment here](https://www.tensorflow.org/api_docs/python/nn.html#convolution). +In the case that `data_format` does start with `"NC"`, the `input` and output +(but not the `filter`) are simply transposed as follows: + + convolution(input, data_format, **kwargs) = + tf.transpose(convolution(tf.transpose(input, [0] + range(2,N+2) + [1]), + **kwargs), + [0, N+1] + range(1, N+1)) + It is required that 1 <= N <= 3. ##### Args: * `input`: An N-D `Tensor` of type `T`, of shape - `[batch_size] + input_spatial_shape + [in_channels]`. + `[batch_size] + input_spatial_shape + [in_channels]` if data_format does + not start with "NC" (default), or + `[batch_size, in_channels] + input_spatial_shape` if data_format starts + with "NC". * `filter`: An N-D `Tensor` with the same type as `input` and shape `spatial_filter_shape + [in_channels, out_channels]`. * `padding`: A string, either `"VALID"` or `"SAME"`. The padding algorithm. @@ -67,13 +79,24 @@ It is required that 1 <= N <= 3. filter in each spatial dimension i. If any value of dilation_rate is > 1, then all values of strides must be 1. * `name`: Optional name for the returned tensor. +* `data_format`: 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": @@ -88,6 +111,6 @@ It is required that 1 <= N <= 3. ##### Raises: -* `ValueError`: If input/output depth does not match `filter` shape, or if - padding is other than `"VALID"` or `"SAME"`. +* `ValueError`: If input/output depth does not match `filter` shape, if padding + is other than `"VALID"` or `"SAME"`, or if data_format is invalid. diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.pool.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.pool.md index b97f4312eea..98a70fde53e 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.pool.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.pool.md @@ -1,8 +1,8 @@ -### `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: @@ -20,12 +20,22 @@ 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: * `input`: 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. * `window_shape`: Sequence of N ints >= 1. * `pooling_type`: Specifies pooling operation, must be "AVG" or "MAX". * `padding`: The padding algorithm, must be "SAME" or "VALID". @@ -37,11 +47,23 @@ The reduction never includes out-of-bounds positions. If any value of strides is > 1, then all values of dilation_rate must be 1. * `name`: Optional. Name of the op. +* `data_format`: 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": diff --git a/tensorflow/g3doc/api_docs/python/nn.md b/tensorflow/g3doc/api_docs/python/nn.md index 7ce8700e36b..6479d5034e3 100644 --- a/tensorflow/g3doc/api_docs/python/nn.md +++ b/tensorflow/g3doc/api_docs/python/nn.md @@ -309,7 +309,7 @@ concatenated. - - - -### `tf.nn.convolution(input, filter, padding, strides=None, dilation_rate=None, name=None)` {#convolution} +### `tf.nn.convolution(input, filter, padding, strides=None, dilation_rate=None, name=None, data_format=None)` {#convolution} Computes sums of N-D convolutions (actually cross-correlation). @@ -319,7 +319,8 @@ convolution, based on the French word "trous" meaning holes in English) via the optional `dilation_rate` parameter. Currently, however, output striding is not supported for atrous convolutions. -Specifically, given rank (N+2) `input` Tensor of shape +Specifically, in the case that `data_format` does not start with "NC", given +a rank (N+2) `input` Tensor of shape [num_batches, input_spatial_shape[0], @@ -344,25 +345,36 @@ position (x[0], ..., x[N-1]): sum_{z[0], ..., z[N-1], q} - filters[z[0], ..., z[N-1], q, k] * + filter[z[0], ..., z[N-1], q, k] * padded_input[b, x[0]*strides[0] + dilation_rate[0]*z[0], ..., x[N-1]*strides[N-1] + dilation_rate[N-1]*z[N-1], - q], + q] where `padded_input` is obtained by zero padding the input using an effective spatial filter shape of `(spatial_filter_shape-1) * dilation_rate + 1` and output striding `strides` as described in the [comment here](https://www.tensorflow.org/api_docs/python/nn.html#convolution). +In the case that `data_format` does start with `"NC"`, the `input` and output +(but not the `filter`) are simply transposed as follows: + + convolution(input, data_format, **kwargs) = + tf.transpose(convolution(tf.transpose(input, [0] + range(2,N+2) + [1]), + **kwargs), + [0, N+1] + range(1, N+1)) + It is required that 1 <= N <= 3. ##### Args: * `input`: An N-D `Tensor` of type `T`, of shape - `[batch_size] + input_spatial_shape + [in_channels]`. + `[batch_size] + input_spatial_shape + [in_channels]` if data_format does + not start with "NC" (default), or + `[batch_size, in_channels] + input_spatial_shape` if data_format starts + with "NC". * `filter`: An N-D `Tensor` with the same type as `input` and shape `spatial_filter_shape + [in_channels, out_channels]`. * `padding`: A string, either `"VALID"` or `"SAME"`. The padding algorithm. @@ -378,13 +390,24 @@ It is required that 1 <= N <= 3. filter in each spatial dimension i. If any value of dilation_rate is > 1, then all values of strides must be 1. * `name`: Optional name for the returned tensor. +* `data_format`: 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: -* `ValueError`: If input/output depth does not match `filter` shape, or if - padding is other than `"VALID"` or `"SAME"`. +* `ValueError`: 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: + + +* `ValueError`: 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: * `input`: 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. * `window_shape`: Sequence of N ints >= 1. * `pooling_type`: Specifies pooling operation, must be "AVG" or "MAX". * `padding`: 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. * `name`: Optional. Name of the op. +* `data_format`: 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":