Given an input tensor of shape [batch, in_height, in_width, in_channels]
and a filter / kernel tensor of shape
[filter_height, filter_width, in_channels, out_channels], this op
performs the following:
Flattens the filter to a 2-D matrix with shape
[filter_height * filter_width * in_channels, output_channels].
Extracts image patches from the input tensor to form a virtual
tensor of shape [batch, out_height, out_width,
filter_height * filter_width * in_channels].
For each patch, right-multiplies the filter matrix and the image patch
vector.
Must have strides[0] = strides[3] = 1. For the most common case of the same
horizontal and vertices strides, strides = [1, stride, stride, 1].
Args
input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64, int32.
A 4-D tensor. The dimension order is interpreted according to the value
of data_format, see below for details.
filter
A Tensor. Must have the same type as input.
A 4-D tensor of shape
[filter_height, filter_width, in_channels, out_channels]
strides
A list of ints.
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
A string from: "SAME", "VALID", "EXPLICIT".
The type of padding algorithm to use.
use_cudnn_on_gpu
An optional bool. Defaults to True.
explicit_paddings
An optional list of ints. Defaults to [].
If padding is "EXPLICIT", the list of explicit padding amounts. For the ith
dimension, the amount of padding inserted before and after the dimension is
explicit_paddings[2 * i] and explicit_paddings[2 * i + 1], respectively. If
padding is not "EXPLICIT", explicit_paddings must be empty.
data_format
An optional string from: "NHWC", "NCHW". Defaults to "NHWC".
Specify the data format of the input and output data. With the
default format "NHWC", the data is stored in the order of:
[batch, height, width, channels].
Alternatively, the format could be "NCHW", the data storage order of:
[batch, channels, height, width].
dilations
An optional list of ints. Defaults to [1, 1, 1, 1].
1-D tensor of length 4. The dilation factor for each dimension of
input. If set to k > 1, there will be k-1 skipped cells between each
filter element on that dimension. The dimension order is determined by the
value of data_format, see above for details. Dilations in the batch and
depth dimensions must be 1.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-01-23 UTC."],[],[]]