ConvTranspose Output Calculator
Calculate the output shape produced by a transposed (deconvolution) layer.
About ConvTranspose Output Calculator
Transposed convolution (deconvolution) upsamples feature maps. Its output size is (input-1)×stride - 2×padding + kernel_size + output_padding. It is used in decoder networks, GANs for image generation, and semantic segmentation for upsampling to full resolution.
Formula
ConvTranspose Output
output = (input - 1) × stride - 2 × padding + kernel_size + output_padding
- output_padding
- Extra padding added to one side of output
Worked example
With the default values below, ConvTranspose Output Calculator returns Output Size of 16. Change any input above and every figure here is recalculated in your browser.
Inputs used
- Input Sizeminimum 1
- 8
- Kernel Sizeminimum 1
- 4
- Strideminimum 1
- 2
- Paddingminimum 0
- 1
- Output Paddingminimum 0
- 0
Results
- Output Size
- 16
Step by step
- 1Output = (input−1)×stride − 2×pad + kernel + output_pad= 16
Assumptions and limits
- The result is derived only from the 5 inputs listed above. Anything not among them is not part of the calculation.
- In particular, this calculator does not take inflation, tax, fees or charges or compounding frequency into account.
- Figures are rounded for display to whole numbers, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.
How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.
Frequently Asked Questions
Is transposed convolution the same as deconvolution?
Technically no — true deconvolution inverts a convolution. Transposed convolution is the gradient operation of convolution and is commonly (if imprecisely) called deconvolution in deep learning.
Why does transposed convolution cause checkerboard artifacts?
When stride doesn't evenly divide kernel size, overlapping regions receive unequal contributions, creating a checkerboard pattern. Using kernel_size divisible by stride (e.g., 4×4 with stride 2) helps.
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