{ }
View source on GitHub |
Computes a 2-D convolution given 4-D input
and filter
tensors.
tf.compat.v1.nn.conv2d(
input,
filter=None,
strides=None,
padding=None,
use_cudnn_on_gpu=True,
data_format='NHWC',
dilations=[1, 1, 1, 1],
name=None,
filters=None
)
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.
In detail, with the default NHWC format,
output[b, i, j, k] =
sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q]
* filter[di, dj, q, k]
Must have strides[0] = strides[3] = 1
. For the most common case of the same
horizontal and vertical strides, strides = [1, stride, stride, 1]
.
Returns | |
---|---|
A Tensor . Has the same type as input .
|