From c28b1ec2ef5e5e6fa8cc452a8d63c5c24a7cd781 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 2 Mar 2017 13:34:54 -0800 Subject: [PATCH] Go: Update generated wrapper functions for TensorFlow ops. Change: 149040924 --- tensorflow/go/op/wrappers.go | 18 ++++++++++++------ 1 file changed, 12 insertions(+), 6 deletions(-) diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go index 33b59e2d406..62ecf963f59 100644 --- a/tensorflow/go/op/wrappers.go +++ b/tensorflow/go/op/wrappers.go @@ -10134,9 +10134,9 @@ func Conv2DUseCudnnOnGpu(value bool) Conv2DAttr { // // value: Specify the data format of the input and output data. With the // default format "NHWC", the data is stored in the order of: -// [batch, in_height, in_width, in_channels]. +// [batch, height, width, channels]. // Alternatively, the format could be "NCHW", the data storage order of: -// [batch, in_channels, in_height, in_width]. +// [batch, channels, height, width]. // If not specified, defaults to s:"NHWC" func Conv2DDataFormat(value string) Conv2DAttr { return func(m optionalAttr) { @@ -10169,11 +10169,17 @@ func Conv2DDataFormat(value string) Conv2DAttr { // horizontal and vertices strides, `strides = [1, stride, stride, 1]`. // // Arguments: -// -// -// strides: 1-D of length 4. The stride of the sliding window for each dimension -// of `input`. Must be in the same order as the dimension specified with format. +// input: A 4-D tensor. The dimension order is interpreted according to the value +// of `data_format`, see below for details. +// filter: A 4-D tensor of shape +// `[filter_height, filter_width, in_channels, out_channels]` +// strides: 1-D tensor of length 4. The stride of the sliding window for each +// dimension of `input`. The dimension order is determined by the value of +// `data_format`, see below for details. // padding: The type of padding algorithm to use. +// +// Returns A 4-D tensor. The dimension order is determined by the value of +// `data_format`, see below for details. func Conv2D(scope *Scope, input tf.Output, filter tf.Output, strides []int64, padding string, optional ...Conv2DAttr) (output tf.Output) { if scope.Err() != nil { return