Added Up Sampling (inverse Max Pooling) Layers #532
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| def get_output(self, train): | ||
| X = self.get_input(train) | ||
| Y = theano.tensor.extra_ops.repeat(X, self.upsample_size[0], axis = -2) |
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Why use negative axis indexes here, since the tensor shape is completely known?
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No particular reason, just my own style preference. Would you like me to change that?
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I think it would make the operation clearer.
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Negative axis indices have been removed from UpSample2D to make the operation clearer. |
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Merged. I think this would need tests, though (let's try not to add code that does not have unit tests). |
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Certainly. I've already tested both layers extensively. |
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Cool, I'm sure you have. I mean, specifically, adding unit tests for these layers in |
fchollet
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Co-authored-by: Haifeng Jin <haifeng-jin@users.noreply.github.com>
hubingallin
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Co-authored-by: Haifeng Jin <haifeng-jin@users.noreply.github.com>
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I've added two new layers to perform up sampling to convolutional.py, in response to: https://groups.google.com/forum/#!topic/theano-users/wy9Fuy5pWU0
The documentation for both layers is below:
UpSample1D:
Description: This layer up samples input across one dimension (e.g. inverse MaxPooling1D)
Input shape: (nb_samples, steps, dim)
Output shape: (nb_samples, upsampled_steps, dim)
Potential use case: For stacking after a MaxPooling1D Layer
UpSample2D:
Description: This layer upsamples input across two dimensions (e.g. inverse MaxPooling2D)
Input shape: (nb_samples, stack_size, nb_row, nb_col)
Output shape: (nb_samples, stack_size, new_nb_row, new_nb_col)
Potential use case: For stacking after a MaxPooling2D Layer