pool – Down-Sampling¶
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theano.tensor.signal.pool.pool_2d(input, ws=None, ignore_border=None, stride=None, pad=(0, 0), mode='max', ds=None, st=None, padding=None)[source]¶ Downscale the input by a specified factor
Takes as input a N-D tensor, where N >= 2. It downscales the input image by the specified factor, by keeping only the maximum value of non-overlapping patches of size (ws[0],ws[1])
- Parameters
input (N-D theano tensor of input images) – Input images. Max pooling will be done over the 2 last dimensions.
ws (tuple of length 2 or theano vector of ints of size 2.) – Factor by which to downscale (vertical ws, horizontal ws). (2,2) will halve the image in each dimension.
ignore_border (bool (default None, will print a warning and set to False)) – When True, (5,5) input with ws=(2,2) will generate a (2,2) output. (3,3) otherwise.
stride (tuple of two ints or theano vector of ints of size 2.) – Stride size, which is the number of shifts over rows/cols to get the next pool region. If stride is None, it is considered equal to ws (no overlap on pooling regions), eg: stride=(1,1) will shifts over one row and one col for every iteration.
pad (tuple of two ints or theano vector of ints of size 2.) – (pad_h, pad_w), pad zeros to extend beyond four borders of the images, pad_h is the size of the top and bottom margins, and pad_w is the size of the left and right margins.
mode ({'max', 'sum', 'average_inc_pad', 'average_exc_pad'}) – Operation executed on each window. max and sum always exclude the padding in the computation. average gives you the choice to include or exclude it.
ds – deprecated, use parameter ws instead.
st – deprecated, use parameter stride instead.
padding – deprecated, use parameter pad instead.
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theano.tensor.signal.pool.max_pool_2d_same_size(input, patch_size)[source]¶ Takes as input a 4-D tensor. It sets all non maximum values of non-overlapping patches of size (patch_size[0],patch_size[1]) to zero, keeping only the maximum values. The output has the same dimensions as the input.
- Parameters
input (4-D theano tensor of input images) – Input images. Max pooling will be done over the 2 last dimensions.
patch_size (tuple of length 2 or theano vector of ints of size 2.) – Size of the patch (patch height, patch width). (2,2) will retain only one non-zero value per patch of 4 values.
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theano.tensor.signal.pool.pool_3d(input, ws=None, ignore_border=None, stride=None, pad=(0, 0, 0), mode='max', ds=None, st=None, padding=None)[source]¶ Downscale the input by a specified factor
Takes as input a N-D tensor, where N >= 3. It downscales the input image by the specified factor, by keeping only the maximum value of non-overlapping patches of size (ws[0],ws[1],ws[2])
- Parameters
input (N-D theano tensor of input images) – Input images. Max pooling will be done over the 3 last dimensions.
ws (tuple of length 3 or theano vector of ints of size 3) – Factor by which to downscale (vertical ws, horizontal ws, depth ws). (2,2,2) will halve the image in each dimension.
ignore_border (bool (default None, will print a warning and set to False)) – When True, (5,5,5) input with ws=(2,2,2) will generate a (2,2,2) output. (3,3,3) otherwise.
st (tuple of three ints or theano vector of ints of size 3) – Stride size, which is the number of shifts over rows/cols/slices to get the next pool region. If st is None, it is considered equal to ws (no overlap on pooling regions).
pad (tuple of two ints or theano vector of ints of size 3) – (pad_h, pad_w, pad_d), pad zeros to extend beyond six borders of the images, pad_h is the size of the top and bottom margins, pad_w is the size of the left and right margins, and pad_d is the size of the front and back margins
mode ({'max', 'sum', 'average_inc_pad', 'average_exc_pad'}) – Operation executed on each window. max and sum always exclude the padding in the computation. average gives you the choice to include or exclude it.
ds – deprecated, use parameter ws instead.
st – deprecated, use parameter st instead.
padding – deprecated, use parameter pad instead.