tf.raw_ops.Conv

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

tf.raw_ops.Conv(
    input,
    filter,
    strides,
    padding,
    explicit_paddings=[],
    data_format='CHANNELS_LAST',
    dilations=[],
    batch_dims=1,
    groups=1,
    name=None
)

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

inputA 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.
filterA 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.
stridesA 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.
paddingA string from: "SAME", "VALID", "EXPLICIT". The type of padding algorithm to use.
explicit_paddingsAn 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_formatAn 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).
dilationsAn 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_dimsAn 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.
groupsAn 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.
nameA name for the operation (optional).

A Tensor. Has the same type as input.