深度循环神经网络
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import torch
from torch import nn
from d2l import torch as d2l
batch_size,num_steps = 32,35
train_iter,vocab = d2l.load_data_time_machine(batch_size,num_steps)
vocab_size,num_hiddens,num_layers = len(vocab),256,2
num_inputs = vocab_size
device = torch.device('cuda')
lstm_layer = nn.LSTM(num_inputs,num_hiddens,num_layers)
model = d2l.RNNModel(lstm_layer,len(vocab))
model = model.to(device)
num_epochs,lr = 500,2
d2l.train_ch8(model,train_iter,vocab,lr,num_epochs,device)
d2l.plt.show() perplexity 1.0, 159965.3 tokens/sec on cuda
time travelleryou can show black is white by argument said filby
travelleryou can show black is white by argument said filby
总结：
在深度循环神经网络中，内部状态的信息会被传输至当前层的下一个时间步以及后续层的当前时间步。
深度神经网络通过多层隐藏结构能够有效学习并表达更为复杂的非线性关系。
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