def init_run_gru( num_epochs, batch_size, num_steps, num_hiddens, lr): train_iter, vocab = d2l.load_data_time_machine(batch_size, num_steps) vocab_size, device = len(vocab), d2l.try_gpu() num_inputs = vocab_size gru_layer = nn.GRU(num_inputs, num_hiddens) model = d2l.RNNModel(gru_layer, len(vocab)) model = model.to(device) return train_ch8_slim(model, train_iter, vocab, lr, num_epochs, device)
W_xz, W_hz, b_z, W_xr, W_hr, b_r, W_xh, W_hh, b_h, W_hq, b_q = params H, = state outputs = [] for X in inputs: Z = torch.sigmoid((X @ W_xz) + (H @ W_hz) + b_z) R = torch.sigmoid((X @ W_xr) + (H @ W_hr) + b_r) H_tilda = torch.tanh((X @ W_xh) + ((R * H) @ W_hh) + b_h) H = Z * H + (1 - Z) * H_tilda Y = H @ W_hq + b_q outputs.append(Y) return torch.cat(outputs, dim=0), (H, ) # Hyperparameters batch_size, num_steps = 32, 35 train_iter, vocab = d2l.load_data_time_machine(batch_size, num_steps) vocab_size, num_hiddens, device = len(vocab), 256, d2l.try_gpu() num_epochs, lr = 500, 1 model = d2l.RNNModelScratch(len(vocab), num_hiddens, device, get_params, init_gru_state, gru) print('scratch model') d2l.train_ch8(model, train_iter, vocab, lr, num_epochs, device) num_inputs = vocab_size gru_layer = nn.GRU(num_inputs, num_hiddens) model = d2l.RNNModel(gru_layer, len(vocab)) model = model.to(device) print('concise model') # d2l.train_ch8(model, train_iter, vocab, lr, num_epochs, device) """