Computes a 2-D convolution given 4-D `input` and `filter` tensors.
Given an input tensor of shape `[batch, in_height, in_width, in_channels]` and a filter / kernel tensor of shape `[filter_height, filter_width, in_channels, out_channels]`, this op performs the following:
1. Flattens the filter to a 2-D matrix with shape `[filter_height * filter_width * in_channels, output_channels]`. 2. Extracts image patches from the input tensor to form a virtual tensor of shape `[batch, out_height, out_width, filter_height * filter_width * in_channels]`. 3. For each patch, right-multiplies the filter matrix and the image patch vector.
In detail, with the default NHWC format,
output[b, i, j, k] = sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * filter[di, dj, q, k]
Must have `strides[0] = strides[3] = 1`. For the most common case of the same horizontal and vertices strides, `strides = [1, stride, stride, 1]`.
Nested Classes
| class | Conv2d.Options | Optional attributes for Conv2d
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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> Conv2d<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 Conv2d operation.
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| static Conv2d.Options |
dataFormat(String dataFormat)
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| static Conv2d.Options |
dilations(List<Long> dilations)
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| static Conv2d.Options |
explicitPaddings(List<Long> explicitPaddings)
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| Output<T> |
output()
A 4-D tensor.
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| static Conv2d.Options |
useCudnnOnGpu(Boolean useCudnnOnGpu)
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Inherited Methods
Constants
public static final String OP_NAME
The name of this op, as known by TensorFlow core engine
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 Conv2d<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 Conv2d operation.
Parameters
| scope | current scope |
|---|---|
| input | A 4-D tensor. The dimension order is interpreted according to the value of `data_format`, see below for details. |
| filter | A 4-D tensor of shape `[filter_height, filter_width, in_channels, out_channels]` |
| strides | 1-D tensor of length 4. The stride of the sliding window for each dimension of `input`. The dimension order is determined by the value of `data_format`, see below for details. |
| padding | The type of padding algorithm to use. |
| options | carries optional attributes values |
Returns
- a new instance of Conv2d
public static Conv2d.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, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width]. |
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public static Conv2d.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 Conv2d.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 ()
A 4-D tensor. The dimension order is determined by the value of `data_format`, see below for details.