tensorflow::ops::Conv

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

Summary

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

Args:

• scope: A Scope object
• input: 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: An (N+2)-DTensor 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: 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: The type of padding algorithm to use.

Optional attributes (see Attrs):

• explicit_paddings: 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: 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: 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: A positive integer specifying the number of batch dimensions for the input tensor. Should be less than the rank of the input tensor.
• groups: 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.

Returns:

• Output: A (N+1+batch_dims)-D tensor. The dimension order is determined by the value of channels_last_format, see below for details.

Public attributes

Operation operation

::tensorflow::Output output

Public functions

 Conv(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input input,
  ::tensorflow::Input filter,
  const gtl::ArraySlice< int > & strides,
  StringPiece padding
)

 Conv(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input input,
  ::tensorflow::Input filter,
  const gtl::ArraySlice< int > & strides,
  StringPiece padding,
  const Conv::Attrs & attrs
)

::tensorflow::Node * node() const 

 operator::tensorflow::Input() const 

 operator::tensorflow::Output() const 

Public static functions

Attrs BatchDims(
  int64 x
)

Attrs DataFormat(
  StringPiece x
)

Attrs Dilations(
  const gtl::ArraySlice< int > & x
)

Attrs ExplicitPaddings(
  const gtl::ArraySlice< int > & x
)

Attrs Groups(
  int64 x
)