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TensorFlow 2.0 version View source on GitHub

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


  • tf.compat.v1.image.extract_patches
  • tf.compat.v2.image.extract_patches


  • images: A 4-D Tensor with shape `[batch, in_rows, in_cols, depth]
  • sizes: The size of the sliding window for each dimension of images.
  • 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 rate in 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 [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. Note out_rows and out_cols are the dimensions of the output patches.