tf.conv

Computes a N-D convolution given (N+1+batch_dims)-D input and (N+2)-D filter tensors.

tf.conv(
    input: Annotated[Any, TV_Conv_T],
    filter: Annotated[Any, TV_Conv_T],
    strides,
    padding: str,
    explicit_paddings=[],
    data_format: str = 'CHANNELS_LAST',
    dilations=[],
    batch_dims: int = 1,
    groups: int = 1,
    name=None
) -> Annotated[Any, TV_Conv_T]

General function for computing a N-D convolution. It is required that 1 <= N <= 3.

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.
name A name for the operation (optional).

A Tensor. Has the same type as input.