custom-rnn
A lightweight, NumPy-only neural network library implementing recurrent architectures and CTC (Connectionist Temporal Classification) from scratch.
Features
- RNN Cell — vanilla recurrent neural network cell with forward and backward (BPTT) support
- GRU Cell — gated recurrent unit with reset, update, and new gates
- RNN Phoneme Classifier — multi-layer RNN for sequence classification
- Character Predictor — GRU-based character-level prediction with inference utilities
- CTC Loss — full CTC forward-backward algorithm with posterior probability computation
- CTC Decoding — greedy search and beam search decoders
- Neural Network Primitives — Linear layer, Sigmoid, Tanh, Softmax Cross-Entropy loss
Installation
pip install custom-rnn
Quick Start
import numpy as np
from mytorch import RNNCell, GRUCell
from CTC import CTC, CTCLoss, GreedySearchDecoder, BeamSearchDecoder
# Create an RNN cell
rnn = RNNCell(input_size=10, hidden_size=20)
x = np.random.randn(1, 10)
h = np.zeros((1, 20))
h_next = rnn(x, h)
# Create a GRU cell
gru = GRUCell(input_size=10, hidden_size=20)
x = np.random.randn(10)
h = np.zeros(20)
h_next = gru(x, h)
# CTC greedy decoding
symbols = ["a", "b", "c"]
decoder = GreedySearchDecoder(symbols)
# CTC beam search decoding
beam_decoder = BeamSearchDecoder(symbols, beam_width=3)
Requirements
- Python >= 3.8
- NumPy
1 maintainer
Metadata
1 maintainer
Release files for custom-rnn 0.1.0
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