public final class
Conv2dBackpropInput
Computes the gradients of convolution with respect to the input.
Nested Classes
| class | Conv2dBackpropInput.Options | Optional attributes for Conv2dBackpropInput
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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.
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| static <T extends TNumber> Conv2dBackpropInput<T> | |
| static Conv2dBackpropInput.Options |
dataFormat(String dataFormat)
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| static Conv2dBackpropInput.Options |
dilations(List<Long> dilations)
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| static Conv2dBackpropInput.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 Conv2dBackpropInput.Options |
useCudnnOnGpu(Boolean useCudnnOnGpu)
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Inherited Methods
| boolean |
equals(Object arg0)
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| final Class<?> |
getClass()
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| int |
hashCode()
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| final void |
notify()
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| final void |
notifyAll()
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| String |
toString()
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| final void |
wait(long arg0, int arg1)
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| final void |
wait(long arg0)
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| final void |
wait()
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| abstract ExecutionEnvironment |
env()
Return the execution environment this op was created in.
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| abstract Operation |
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]. |
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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. |
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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. |
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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.