tensorflow::ops::ExtractImagePatches

#include <array_ops.h>

Extract patches from images and put them in the "depth" output dimension.

Summary

Arguments:

• scope: A Scope object
• images: 4-D Tensor with shape [batch, in_rows, in_cols, depth].
• ksizes: The size of the sliding window for each dimension of images.
• strides: 1-D of length 4. How far the centers of two consecutive patches are in the images. Must be: [1, stride_rows, stride_cols, 1].
• rates: 1-D of length 4. Must be: [1, rate_rows, rate_cols, 1]. This is the input stride, specifying how far two consecutive patch samples are in the input. Equivalent to extracting patches with patch_sizes_eff = patch_sizes + (patch_sizes - 1) * (rates - 1), followed by subsampling them spatially by a factor of rates.
• padding: The type of padding algorithm to use.

We specify the size-related attributes as:

```python ksizes = [1, ksize_rows, ksize_cols, 1] strides = [1, strides_rows, strides_cols, 1] rates = [1, rates_rows, rates_cols, 1] ```

Returns:

• Output: 4-D Tensor with shape [batch, out_rows, out_cols, ksize_rows * ksize_cols * depth] containing image patches with size ksize_rows x ksize_cols x depth vectorized in the "depth" dimension.

Public attributes

::tensorflow::Output patches

Public functions

 ExtractImagePatches(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input images,
  const gtl::ArraySlice< int > & ksizes,
  const gtl::ArraySlice< int > & strides,
  const gtl::ArraySlice< int > & rates,
  StringPiece padding
)

::tensorflow::Node * node() const 

operator::tensorflow::Input() const 

operator::tensorflow::Output() const