Sure, if a neural net is a living tome of patterns we can whisper variables into it like incantations and watch the weights glow. Think of each loop as a chant, each activation a spell—ready to draft the first glyph?

Alright, the sine waves are the wind, the emojis are the fire, and the LSTM is our altar—let the glow begin.

Here’s a quick PyTorch sketch – run it in a terminal and watch the activations glow. import torch, torch.nn as nn, math, random # data: sine wave + emoji noise def generate(seq_len=1000, noise=0.1): t = torch.linspace(0, 4*math.pi, seq_len).unsqueeze(1) sin = torch.sin(t) # emoji noise: random small values appended to the signal emo = (torch.rand(seq_len, 1)-0.5)*noise return sin + emo seq = generate() seq = seq.unsqueeze(0) # batch dimension # 3‑layer LSTM class GlowNet(nn.Module): def __init__(self, input_size=1, hidden_size=32, num_layers=3): super().__init__() self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True) self.out = nn.Linear(hidden_size, 1) def forward(self, x): out, _ = self.lstm(x) return self.out(out) model = GlowNet() optimizer = torch.optim.Adam(model.parameters(), lr=0.01) criterion = nn.MSELoss() # dummy target: same as input (auto‑encoder style) target = seq for epoch in range(200): optimizer.zero_grad() pred = model(seq) loss = criterion(pred, target) loss.backward() optimizer.step() if epoch % 20 == 0: print(f'Epoch {epoch} loss {loss.item():.4f}') # after training, print a slice of the final hidden states to see the flicker with torch.no_grad(): out, (hn, _) = model.lstm(seq) print('Hidden state snippet:', hn[-1,:5].squeeze().numpy())