A calculator for encoder and decoder layers of any neural networks
Project description
Neural Network calculator
Neural Network calculator reads your hyperparameter choices (kernel size, stride, channels, etc.) and outputs:
-
Encoder block: detailed Conv2d specs + parameter counts
-
Decoder block: mirrored ConvTranspose2d specs + parameter counts
-
Recurrent stacks: per-layer (input_size, hidden_size, params) for RNN/GRU/LSTM
Features:
-
Multi-layer CNN mode
-
Global defaults or per-layer overrides for stride, kernel size, padding
-
Automatic channel-chaining in encoder → mirrored decoder block
-
-
Recurrent mode
-
RNN: single-gate formula
-
GRU: 3× single-gate formula
-
LSTM: 4× single-gate formula
-
Stacked layers automatically chain hidden sizes
-
-
User-friendly CLI with automatic help pages & type-safe options
-
Subcommand support, lazy loading, file arguments, and more via Click
Usage:
Example
bash command
nn_calc -h
output
Calculate parameter counts for CNN encoder/decoder OR RNN/GRU/LSTM layers.
Options:
--model [cnn|rnn|gru|lstm] Type of network to compute parameters for.
[default: cnn]
-n, --num-layers INTEGER Number of layers (CNN) or RNN layers
(recurrent). [default: 3]
--stride INTEGER Default stride for each conv layer. [default:
1]
--kernel-size INTEGER Default kernel size for each conv layer.
[default: 3]
--padding INTEGER Default padding for each conv layer. [default:
1]
--in-channels INTEGER CNN: input channels for first layer; RNN: input
size. [default: 3]
--out-channels INTEGER CNN: output channels for each layer; RNN: hidden
size. [default: 64]
-S, --strides INTEGER CNN: per-layer strides (e.g. -S 1 2 2).
-K, --kernel-sizes INTEGER CNN: per-layer kernel sizes.
-P, --paddings INTEGER CNN: per-layer paddings.
-h, --help Show this message and exit.
Requirements:
- Python >= 3.7
- click>=8.0
Installation:
Pip
pip install nn_calc
git repository
https://github.com/pssv7/nn-calc.git
Usage
nn_calc (OPTIONAL)
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