BATCH_SIZE = 100 SEQ_SIZE = 16 learning_rate = 0.0001
时间: 2023-06-11 19:05:32 浏览: 47
这似乎是一些神经网络训练的参数设置。BATCH_SIZE 表示每个训练批次的样本数量,SEQ_SIZE 表示输入序列的长度,learning_rate 表示学习率,即每次更新权重的步长大小。这些参数的具体值需要根据具体应用场景进行调整。
相关问题
解释一下BATCH_SIZE = 100 SEQ_SIZE = 1 learning_rate = 0.0001 PATH_SAVE = './model/convGRU_model.t7' os.environ["CUDA_VISIBLE_DEVICES"] = "0,1"
BATCH_SIZE = 100表示每次训练时输入的数据集大小为100,SEQ_SIZE = 1表示输入数据序列的长度为1,learning_rate = 0.0001表示学习率,即控制参数更新的速率,在每次迭代中会对参数进行微调,PATH_SAVE = './model/convGRU_model.t7'表示模型训练完后保存的路径,os.environ["CUDA_VISIBLE_DEVICES"] = "0,1"表示使用GPU进行训练,其中"0,1"表示使用多个GPU进行并行计算。只有遵守隐私规则并妥善保管您的隐私才能确保您的数据安全。
class Net(nn.Module): def __init__(self,input_size,hidden_size,num_layers,output_size,batch_size,seq_length) -> None: super(Net,self).__init__() self.input_size=input_size self.hidden_size=hidden_size self.num_layers=num_layers self.output_size=output_size self.batch_size=batch_size self.seq_length=seq_length self.num_directions=1 # 单向LSTM self.lstm=nn.LSTM(input_size=input_size,hidden_size=hidden_size,num_layers=num_layers,batch_first=True) # LSTM层 self.fc=nn.Linear(hidden_size,output_size) # 全连接层 def forward(self,x): # e.g. x(10,3,100) 三个句子,十个单词,一百维的向量,nn.LSTM(input_size=100,hidden_size=20,num_layers=4) # out.shape=(10,3,20) h/c.shape=(4,b,20) batch_size, seq_len = x.size()[0], x.size()[1] # x.shape=(604,3,3) h_0 = torch.randn(self.num_directions * self.num_layers, x.size(0), self.hidden_size) c_0 = torch.randn(self.num_directions * self.num_layers, x.size(0), self.hidden_size) # output(batch_size, seq_len, num_directions * hidden_size) output, _ = self.lstm(x, (h_0, c_0)) # output(5, 30, 64) pred = self.fc(output) # (5, 30, 1) pred = pred[:, -1, :] # (5, 1) return pred改成python代码
这段代码是一个简单的使用 PyTorch 实现的 LSTM 模型。下面是将其转化为 Python 代码的版本:
```python
import torch
import torch.nn as nn
class Net(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, output_size, batch_size, seq_length):
super(Net, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.num_layers = num_layers
self.output_size = output_size
self.batch_size = batch_size
self.seq_length = seq_length
self.num_directions = 1 # 单向LSTM
# LSTM层
self.lstm = nn.LSTM(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, batch_first=True)
# 全连接层
self.fc = nn.Linear(hidden_size, output_size)
def forward(self, x):
# x.shape=(10,3,100),三个句子,十个单词,一百维的向量
# out.shape=(10,3,20),h/c.shape=(4,b,20)
batch_size, seq_len = x.size()[0], x.size()[1]
h_0 = torch.randn(self.num_directions * self.num_layers, x.size(0), self.hidden_size)
c_0 = torch.randn(self.num_directions * self.num_layers, x.size(0), self.hidden_size)
output, _ = self.lstm(x, (h_0, c_0)) # output(batch_size, seq_len, num_directions * hidden_size)
pred = self.fc(output) # (5, 30, 1)
pred = pred[:, -1, :] # (5, 1)
return pred
```