Step-by-Step Guide
Let's build a simple RNN from scratch! We'll create a character-level language model that learns to generate text.
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Step 1
Prepare Your Data
Load text, create vocabulary, encode sequences
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Step 2
Define RNN Architecture
Choose layers, hidden size, activation functions
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Step 3
Implement Forward Pass
Process sequences through the network
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Step 4
Train Your RNN
Loss function, optimizer, training loop
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Step 5
Generate Text
Use trained model to create new sequences
Quick Start Code
Here's a minimal RNN implementation to get you started. Click for the full code!
import torch
import torch.nn as nn
class SimpleRNN(nn.Module):
def __init__(self, vocab_size, hidden_size):
super().__init__()
self.embedding = nn.Embedding(vocab_size, hidden_size)
self.rnn = nn.RNN(hidden_size, hidden_size, batch_first=True)
self.fc = nn.Linear(hidden_size, vocab_size)
def forward(self, x, hidden=None):
embedded = self.embedding(x)
output, hidden = self.rnn(embedded, hidden)
output = self.fc(output)
return output, hidden
# Click for complete implementation...
Pro Tip:
Start simple! Get a basic RNN working before adding complexity like LSTM/GRU cells or attention mechanisms.