seq_import = function("prodigal_outdir.fna"){ Error: unexpected string constant in "seq_import = function("prodigal_outdir.fna""报错解决办法
时间: 2023-07-19 22:32:38 浏览: 21
这个错误提示是因为在定义函数时,函数名后面的括号中应该列出函数的参数,而不是直接写入参数的值。可以将函数名后面的括号中的参数名改为 `file_name`,然后在函数内部使用这个参数名来读取文件,例如:
```
seq_import = function(file_name){
seq_data = read.fasta(file_name)
# 这里可以对读取到的序列数据进行处理
return(seq_data)
}
# 调用函数
seq_data = seq_import("prodigal_outdir.fna")
```
在调用函数时,需要将要读取的文件名作为参数传递给函数。
相关问题
seq_import = function("prodigal_outdir.fna"){ Error: unexpected string constant in "seq_import = function("prodigal_outdir.fna""
这个错误提示是因为在定义函数时,函数名后面的括号中应该列出函数的参数,而不是直接写入参数的值。可以将函数名后面的括号中的参数名改为 `file_name`,然后在函数内部使用这个参数名来读取文件,例如:
```
seq_import = function(file_name){
seq_data = read.fasta(file_name)
# 这里可以对读取到的序列数据进行处理
return(seq_data)
}
# 调用函数
seq_data = seq_import("prodigal_outdir.fna")
```
在调用函数时,需要将要读取的文件名作为参数传递给函数。
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
```