public final class
Conv2DBackpropInputV2
Computes the gradients of convolution with respect to the input.
Nested Classes
| class | Conv2DBackpropInputV2.Options | Optional attributes for Conv2DBackpropInputV2
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Public Methods
| Output<T> |
asOutput()
Returns the symbolic handle of a tensor.
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| static <T extends Number> Conv2DBackpropInputV2<T> | |
| static Conv2DBackpropInputV2.Options |
dataFormat(String dataFormat)
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| static Conv2DBackpropInputV2.Options |
dilations(List<Long> dilations)
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| static Conv2DBackpropInputV2.Options |
explicitPaddings(List<Long> explicitPaddings)
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| Output<T> |
output()
4-D with shape `[batch, in_height, in_width, in_channels]`.
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| static Conv2DBackpropInputV2.Options |
useCudnnOnGpu(Boolean useCudnnOnGpu)
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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 Conv2DBackpropInputV2<T> create (Scope scope, Operand<T> input, Operand<T> filter, Operand<T> outBackprop, List<Long> strides, String padding, Options... options)
Factory method to create a class wrapping a new Conv2DBackpropInputV2 operation.
Parameters
| scope | current scope |
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| input | 4-D with shape `[batch, in_height, in_width, in_channels]`. Only shape of tensor is used. |
| 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 Conv2DBackpropInputV2
public static Conv2DBackpropInputV2.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]. |
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public static Conv2DBackpropInputV2.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. |
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public static Conv2DBackpropInputV2.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. |
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public Output<T> output ()
4-D with shape `[batch, in_height, in_width, in_channels]`. Gradient w.r.t. the input of the convolution.