Conv2dBackpropInput

public final class Conv2dBackpropInput

Computes the gradients of convolution with respect to the input.

Nested Classes

class Conv2dBackpropInput.Options Optional attributes for Conv2dBackpropInput

Constants

String OP_NAME The name of this op, as known by TensorFlow core engine

Public Methods

Output <T>
asOutput ()
Returns the symbolic handle of the tensor.
static <T extends TNumber > Conv2dBackpropInput <T>
create ( Scope scope, Operand < TInt32 > inputSizes, Operand <T> filter, Operand <T> outBackprop, List<Long> strides, String padding, Options... options)
Factory method to create a class wrapping a new Conv2dBackpropInput operation.
static Conv2dBackpropInput.Options
dataFormat (String dataFormat)
static Conv2dBackpropInput.Options
dilations (List<Long> dilations)
static Conv2dBackpropInput.Options
explicitPaddings (List<Long> explicitPaddings)
Output <T>
output ()
4-D with shape `[batch, in_height, in_width, in_channels]`.
static Conv2dBackpropInput.Options
useCudnnOnGpu (Boolean useCudnnOnGpu)

Inherited Methods

Constants

public static final String OP_NAME

The name of this op, as known by TensorFlow core engine

Constant Value: "Conv2DBackpropInput"

Public Methods

public Output <T> asOutput ()

Returns the symbolic handle of the 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 Conv2dBackpropInput <T> create ( Scope scope, Operand < TInt32 > inputSizes, Operand <T> filter, Operand <T> outBackprop, List<Long> strides, String padding, Options... options)

Factory method to create a class wrapping a new Conv2dBackpropInput operation.

Parameters
scope current scope
inputSizes An integer vector representing the shape of `input`, where `input` is a 4-D `[batch, height, width, channels]` tensor.
filter 4-D with shape `[filter_height, filter_width, in_channels, out_channels]`.
outBackprop 4-D with shape `[batch, out_height, out_width, out_channels]`. Gradients w.r.t. the output of the convolution.
strides The stride of the sliding window for each dimension of the input of the convolution. Must be in the same order as the dimension specified with format.
padding The type of padding algorithm to use.
options carries optional attributes values
Returns
  • a new instance of Conv2dBackpropInput

public static Conv2dBackpropInput.Options dataFormat (String dataFormat)

Parameters
dataFormat Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width].

public static Conv2dBackpropInput.Options dilations (List<Long> dilations)

Parameters
dilations 1-D tensor of length 4. 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 `data_format`, see above for details. Dilations in the batch and depth dimensions must be 1.

public static Conv2dBackpropInput.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 Output <T> output ()

4-D with shape `[batch, in_height, in_width, in_channels]`. Gradient w.r.t. the input of the convolution.

public static Conv2dBackpropInput.Options useCudnnOnGpu (Boolean useCudnnOnGpu)