images and put them in the "depth" output dimension.
tf.image.extract_patches( images, sizes, strides, rates, padding, name=None )
images: A 4-D Tensor with shape `[batch, in_rows, in_cols, depth]
sizes: The size of the sliding window for each dimension of
strides: A 1-D Tensor of length 4. How far the centers of two consecutive patches are in the images. Must be:
[1, stride_rows, stride_cols, 1].
rates: A 1-D Tensor 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. This is equivalent to
ratein dilated (a.k.a. Atrous) convolutions.
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]
name: A name for the operation (optional).
A 4-D Tensor. Has the same type as
images, and with shape
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. Note
out_cols are the dimensions of
the output patches.