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_vjpConv2DBackpropInput using shape instead of using filter size for … #331
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rxwei
reviewed
Jul 3, 2019
| @@ -184,8 +184,8 @@ func _vjpConv2DBackpropInput<Scalar: TensorFlowFloatingPoint>( | |||
| ) -> (Tensor<Scalar>, (Tensor<Scalar>) -> (Tensor<Scalar>, Tensor<Scalar>)) { | |||
| let value = conv2DBackpropInput(x, shape: shape, filter: filter, | |||
| strides: strides, padding: padding, dilations: dilations) | |||
| return (value, { v in | |||
| (conv2DBackpropFilter(x, input: v, filterSizes: shape, strides: strides, | |||
| return (value, { [filterShape = filter.shapeTensor] v in | |||
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Actually this pullback is already capturing filter (on line 190), so I think your original implementation is better! I reverted it for you.
|
As I mentioned in my comment on #288, I'm not having issues with the original code as it was before your change. Your test passes for me. The issue I had was with the calculation of the gradients during backprop which this PR fixed. I'm wondering if your issue was with your original test. It looks like you might have had Conv2D instead of TransposeConv2D. Have you tried running your updated test against the original code? |
sjaz24
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Jul 7, 2019
Fix 'conv2D' derivative (tensorflow#331)
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…filterSizes argument.