网站群建设 会议 主持,小程序api密钥,网上的推广公司,北京做网站软件1.从零开始实现LSTM
#从零开始实现长短期记忆网络
import torch
from torch import nn
from d2l import torch as d2l#加载时光机器数据集
batch_size,num_steps 32,35
train_iter,vocab d2l.load_data_time_machine(batch_size,num_steps)#1.定义和初始化模型参数#xff…1.从零开始实现LSTM
#从零开始实现长短期记忆网络
import torch
from torch import nn
from d2l import torch as d2l#加载时光机器数据集
batch_size,num_steps 32,35
train_iter,vocab d2l.load_data_time_machine(batch_size,num_steps)#1.定义和初始化模型参数
#超参数num_hiddens定义隐藏单元的数量。按照标准差0.01的高斯分布初始化权重并将偏置项设为0。
def get_lstm_params(vocab_size,num_hiddens,device):num_inputs num_outputs vocab_sizedef normal(shape):return torch.randn(sizeshape,devicedevice)*0.01def three():return (normal((num_inputs,num_hiddens)),normal((num_hiddens,num_hiddens)),torch.zeros(num_hiddens,devicedevice))W_xi,W_hi,b_i three() #输入门参数W_xf,W_hf,b_f three() #遗忘门参数W_xo,W_ho,b_o three() #输出门参数W_xc,W_hc,b_c three() #候选记忆元参数#输出层参数W_hq normal((num_hiddens,num_outputs))b_q torch.zeros(num_outputs,devicedevice)#附加梯度params [W_xi,W_hi,b_i,W_xf,W_hf,b_f,W_xo,W_ho,b_o,W_xc,W_hc,b_c,W_hq,b_q]for param in params:param.requires_grad_(True)return params
#2.定义模型
#在初始化函数中长短期记忆网络的隐状态需要返回一个额外的记忆元单元的值为0形状为批量大小隐藏单元数
def init_lstm_state(batch_size,num_hiddens,device):return (torch.zeros((batch_size,num_hiddens),devicedevice),torch.zeros((batch_size,num_hiddens),devicedevice))
#实际模型的定义与前面讨论的一样提供三个门和一个额外的记忆元。
#只有隐状态才会传递到输出层而记忆元mathbf{C}_t不直接参与输出计算。
def lstm(inputs,state,params):[W_xi,W_hi,b_i,W_xf,W_hf,b_f,W_xo,W_ho,b_o,W_xc,W_hc,b_c,W_hq,b_q] params(H,C) stateoutputs []for X in inputs:I torch.sigmoid((X W_xi) (H W_hi) b_i)F torch.sigmoid((X W_xf) (H W_hf) b_f)O torch.sigmoid((X W_xo) (H W_ho) b_o)C_tilda torch.tanh((X W_xc) (H W_hc) b_c)C F * C I * C_tildaH O * torch.tanh(C)Y (H W_hq) b_qoutputs.append(Y)return torch.cat(outputs,dim0),(H,C)
#3.训练和预测
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_lstm_params,init_lstm_state,lstm)
print(d2l.train_ch8(model,train_iter,vocab,lr,num_epochs,device))
d2l.plt.show() 2.简洁实现LSTM
#简洁实现长短期记忆网络
import torch
from torch import nn
from d2l import torch as d2l#加载时光机器数据集
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 500,1num_inputs vocab_size
lstm_layer nn.LSTM(num_inputs,num_hiddens)
model d2l.RNNModel(lstm_layer,len(vocab))
model model.to(device)
print(d2l.train_ch8(model,train_iter,vocab,lr,num_epochs,device))
d2l.plt.show()
