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Computes a 2-D convolution given input
and 4-D filters
tensors.
tf.nn.conv2d(
input,
filters,
strides,
padding,
data_format='NHWC',
dilations=None,
name=None
)
The input
tensor may have rank 4
or higher, where shape dimensions [:-3]
are considered batch dimensions (batch_shape
).
Given an input tensor of shape
batch_shape + [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]
.
Usage Example:
x_in = np.array([[
[[2], [1], [2], [0], [1]],
[[1], [3], [2], [2], [3]],
[[1], [1], [3], [3], [0]],
[[2], [2], [0], [1], [1]],
[[0], [0], [3], [1], [2]], ]])
kernel_in = np.array([
[ [[2, 0.1]], [[3, 0.2]] ],
[ [[0, 0.3]], [[1, 0.4]] ], ])
x = tf.constant(x_in, dtype=tf.float32)
kernel = tf.constant(kernel_in, dtype=tf.float32)
tf.nn.conv2d(x, kernel, strides=[1, 1, 1, 1], padding='VALID')
<tf.Tensor: shape=(1, 4, 4, 2), dtype=float32, numpy=..., dtype=float32)>
Args | |
---|---|
input
|
A Tensor . Must be one of the following types:
half , bfloat16 , float32 , float64 .
A Tensor of rank at least 4. The dimension order is interpreted according
to the value of data_format ; with the all-but-inner-3 dimensions acting
as batch dimensions. See below for details.
|
filters
|
A Tensor . Must have the same type as input .
A 4-D tensor of shape
[filter_height, filter_width, in_channels, out_channels]
|
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. See
here
for more information. 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]] .
|
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_shape + [height, width, channels] .
Alternatively, the format could be "NCHW", the data storage order of:
batch_shape + [channels, height, width] .
|
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.
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A Tensor . Has the same type as input and the same outer batch shape.
|