Calculation Results
| Layer | Operation | Input | Effective kernel | Padding | Stride | Dilation | Output | Status |
|---|
Padding Visualization
Framework Configuration
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Calculation Steps
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CNN Layer Configuration
Formula Used
The calculator first finds each effective kernel dimension. Dilation expands spacing without adding learned kernel values. The effective size equals D(K − 1) + 1.
Standard convolution uses padded input, kernel, stride, and dilation. Output height uses floor((H + T + B − Keff) / S) + 1. Width follows the same calculation with horizontal values.
Transposed convolution expands spatial dimensions using stride and output padding. Its output equals (H − 1)S − T − B + Keff + O. Width follows the identical transposed formula for horizontal dimensions.
How to Use
Enter the first tensor height, width, and channels. Choose standard or transposed convolution for every layer. Later layers inherit the previous output automatically.
Select valid, same, full, custom, or target padding. Add kernel, stride, dilation, and output channel values. Transposed layers also accept output padding values.
Press calculate or keep real-time calculations enabled. Review warnings, framework code, and visual padding details. Export results after validating every configured convolution layer.
Example Data
| Input | Kernel | Stride | Dilation | Padding | Expected output |
|---|---|---|---|---|---|
| 28×28 | 3×3 | 1×1 | 1×1 | SAME | 28×28 |
| 32×32 | 3×3 | 1×1 | 1×1 | VALID | 30×30 |
| 224×224 | 7×7 | 2×2 | 1×1 | SAME | 112×112 |
| 64×64 | 3×3 | 1×1 | 2×2 | SAME | 64×64 |
Frequently Asked Questions
What does CNN padding do?
Padding adds border values around an input feature map. It controls output dimensions and preserves edge information. Zero padding remains the most common implementation.
What is valid padding?
Valid padding adds no border values around inputs. Output dimensions usually shrink after applying the kernel. Larger kernels reduce dimensions more strongly.
What is same padding?
Same padding targets an output based on input and stride. Stride one usually preserves spatial input dimensions. Uneven totals require asymmetric padding distribution.
Why are SAME_UPPER and SAME_LOWER different?
Both modes use the same total padding amount. SAME_UPPER places extra padding after the input. SAME_LOWER places extra padding before the input.
How does dilation affect padding?
Dilation increases the kernel's effective receptive field. It may require more padding for preserved dimensions. Learned parameter counts remain unchanged.
Can stride create fractional output dimensions?
The raw convolution ratio may not divide evenly. Most frameworks apply floor for standard convolutions. This calculator clearly warns about truncated remainders.
What is output padding?
Output padding applies only to transposed convolutions. It resolves shape ambiguity created by larger strides. It does not add ordinary input borders.
Can custom padding be asymmetric?
Yes, each side can use a different value. Asymmetric padding helps match exact target dimensions. Some frameworks need an explicit padding operation.
Why do framework outputs sometimes differ?
Frameworks may distribute odd padding totals differently. Tensor layouts and rounding rules can also differ. Always compare generated settings with framework documentation.