General function for computing a N-D convolution. It is required that
1 <= N <= 3.
Args
input
A Tensor. Must be one of the following types: half, bfloat16, float32, float64, int32.
Tensor of type T and shape batch_shape + spatial_shape + [in_channels] in the
case that channels_last_format = true or shape
batch_shape + [in_channels] + spatial_shape if channels_last_format = false.
spatial_shape is N-dimensional with N=2 or N=3.
Also note that batch_shape is dictated by the parameter batch_dims
and defaults to 1.
filter
A Tensor. Must have the same type as input.
An (N+2)-D Tensor with the same type as input and shape
spatial_filter_shape + [in_channels, out_channels], where spatial_filter_shape
is N-dimensional with N=2 or N=3.
strides
A list of ints.
1-D tensor of length N+2. The stride of the sliding window for each
dimension of input. Must have strides[0] = strides[N+1] = 1.
padding
A string from: "SAME", "VALID", "EXPLICIT".
The type of padding algorithm to use.
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: "CHANNELS_FIRST", "CHANNELS_LAST". Defaults to "CHANNELS_LAST".
Used to set the data format. By default CHANNELS_FIRST, uses
NHWC (2D) / NDHWC (3D) or if CHANNELS_LAST, uses NCHW (2D) / NCDHW (3D).
dilations
An optional list of ints. Defaults to [].
1-D tensor of length N+2. 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 channels_last_format, see above for details. Dilations in the batch
and depth dimensions must be 1.
batch_dims
An optional int. Defaults to 1.
A positive integer specifying the number of batch dimensions for the input
tensor. Should be less than the rank of the input tensor.
groups
An optional int. Defaults to 1.
A positive integer specifying the number of groups in which the input is split
along the channel axis. Each group is convolved separately with
filters / groups filters. The output is the concatenation of all the groups
results along the channel axis. Input channels and filters must both be
divisible by groups.
[[["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."],[],[]]