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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].
Args | |
|---|---|
input
|
A Tensor. Must be one of the following types:
half, bfloat16, float32, float64.
A 4-D tensor. The dimension order is interpreted according to the value
of data_format, see below for details.
|
filter
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A Tensor. Must have the same type as input.
A 4-D tensor of shape
[filter_height, filter_width, in_channels, out_channels]
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strides
|
An int or list of ints that has length 1, 2 or 4. The
stride of the sliding window for each dimension of input. If a single
value is given it is replicated in the H and W dimension. By default
the N and C dimensions are set to 1. The dimension order is determined
by the value of data_format, see below for details.
|
padding
|
Either the string "SAME" or "VALID" indicating the type of
padding algorithm to use, or a list indicating the explicit paddings at
the start and end of each dimension. When explicit padding is used and
data_format is "NHWC", this should be in the form [[0, 0], [pad_top,
pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used
and data_format is "NCHW", this should be in the form [[0, 0], [0, 0],
[pad_top, pad_bottom], [pad_left, pad_right]].
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use_cudnn_on_gpu
|
An optional bool. Defaults to True.
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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].
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dilations
|
An int or list of ints that has length 1, 2 or 4,
defaults to 1. The dilation factor for each dimension ofinput. If a
single value is given it is replicated in the H and W dimension. By
default the N and C dimensions are set to 1. 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 if a 4-d tensor
must be 1.
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name
|
A name for the operation (optional). |
filters
|
Alias for filter. |
Returns | |
|---|---|
A Tensor. Has the same type as input.
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