liquidnn 1.1.1

A PyTorch based module for Liquid Networks

#🧠 liquidnn

A PyTorch implementation of Liquid Neural Networks (LNNs) and their hybrids (LTC, CNN, RNN, LSTM variants). This library brings continuous-time dynamics into deep learning architectures using learnable time constants.

##✨ Features

🔹 LiquidNeuralNetwork – stacked liquid neurons

🔹 LTCLayer – Liquid Time-Constant layer

🔹 LiquidCNN – CNN + Liquid update

🔹 LiquidRNN – RNN + Liquid update

🔹 LiquidLSTM – LSTM + Liquid update (with bidirectional support)

🔹 Per-neuron learnable τ (time constants) with stability clamping

##📦 Installation From PyPI (after publishing):

pip install liquidnn

##🚀 Quick Start (all models in one script)

Copy-paste this script to test every model in liquidnn:

import torch
from liquidnn import (
    LiquidNeuralNetwork,
    LTCLayer,
    LiquidCNN,
    LiquidRNN,
    LiquidLSTM
)

# 1. LiquidNeuralNetwork
x = torch.randn(4, 10, 8)  # batch=4, seq_len=10, input_size=8  

model = LiquidNeuralNetwork(input_size=8, hidden_size=16,num_layers=2)  
out = model(x)   
print("LiquidNeuralNetwork output:", out.shape)  # [4, 16]   

# 2. LTCLayer    
x = torch.randn(5, 20, 10)  # batch=5, seq_len=20, input_size=10    
ltc = LTCLayer(input_size=10, hidden_size=32, num_layers=2)    
out = ltc(x)    
print("LTCLayer output:", out.shape)  # [5, 32]    

# 3. LiquidCNN    
x = torch.randn(8, 1, 28, 28)  # MNIST-like input    
model = LiquidCNN(input_channels=1, hidden_size=64, num_layers_liq=2, num_layers_conv=2)    
out = model(x)    
print("LiquidCNN output:", out.shape)  # [8, 64]    

# 4. LiquidRNN    
x = torch.randn(15, 4, 12)  # seq_len=15, batch=4, input_size=12    
model = LiquidRNN(input_size=12, hidden_size=32, num_layers_liq=2, num_layers_rnn=1)    
out = model(x)    
print("LiquidRNN output:", out.shape)  # [4, 32]    

# 5. LiquidLSTM    
x = torch.randn(3, 12, 10)  # batch=3, seq_len=12, input_size=10    
model = LiquidLSTM(input_size=10, hidden_size=32,    
                   num_layers_liq=2, num_layers_lstm=1,    
                   batch_first=True, bidirectional=False)    
out = model(x)    
print("LiquidLSTM output:", out.shape)  # [3, 32]    

##📚 Architectures

LiquidNeuralNetwork: Stacked liquid neurons

LTCLayer: Continuous-time RNN update rule

LiquidCNN: Convolutional backbone + LTC dynamics

LiquidRNN: RNN + Liquid refinement

LiquidLSTM: LSTM + Liquid refinement

Install dependencies:

pip install torch

##🌟 Contribute

PRs and issues are welcome! If you try new liquidized architectures, feel free to share 🚀