Conv

public final class Conv

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

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

Nested Classes

class Conv.Options Optional attributes for Conv  

Public Methods

Output<T>
asOutput()
Returns the symbolic handle of a tensor.
static Conv.Options
batchDims(Long batchDims)
static <T extends Number> Conv<T>
create(Scope scope, Operand<T> input, Operand<T> filter, List<Long> strides, String padding, Options... options)
Factory method to create a class wrapping a new Conv operation.
static Conv.Options
dataFormat(String dataFormat)
static Conv.Options
dilations(List<Long> dilations)
static Conv.Options
explicitPaddings(List<Long> explicitPaddings)
static Conv.Options
groups(Long groups)
Output<T>
output()
A (N+1+batch_dims)-D tensor.

Inherited Methods

Public Methods

public Output<T> asOutput ()

Returns the symbolic handle of a tensor.

Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is used to obtain a symbolic handle that represents the computation of the input.

public static Conv.Options batchDims (Long batchDims)

Parameters
batchDims A positive integer specifying the number of batch dimensions for the input tensor. Should be less than the rank of the input tensor.

public static Conv<T> create (Scope scope, Operand<T> input, Operand<T> filter, List<Long> strides, String padding, Options... options)

Factory method to create a class wrapping a new Conv operation.

Parameters
scope current scope
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)-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 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.
options carries optional attributes values
Returns
  • a new instance of Conv

public static Conv.Options dataFormat (String dataFormat)

Parameters
dataFormat Used to set the data format. By default `CHANNELS_FIRST`, uses `NHWC (2D) / NDHWC (3D)` or if `CHANNELS_LAST`, uses `NCHW (2D) / NCDHW (3D)`.

public static Conv.Options dilations (List<Long> dilations)

Parameters
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.

public static Conv.Options explicitPaddings (List<Long> explicitPaddings)

Parameters
explicitPaddings 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.

public static Conv.Options groups (Long groups)

Parameters
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.

public Output<T> output ()

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