深度学习 机器学习 BUG修复：

import torch

class AE_LSTM(nn.Module):

def init(self, in_channel=1):

super(AE_LSTM, self).init()

self.encoder = nn.Sequential( nn.LSTM(in_channel, 16, batch_first=True, bidirectional=True), nn.InstanceNorm1d(16),

nn.ReLU(),

nn.LSTM(16, 32, batch_first=True, bidirectional=True),

nn.InstanceNorm1d(32),

nn.ReLU(),

nn.LSTM(32, 32, batch_first=True, bidirectional=True),

nn.InstanceNorm1d(32),

nn.ReLU(),

# nn.MaxPool1d(kernel_size=8),

nn.AvgPool1d(kernel_size=8),

nn.Conv1d(32, 32, kernel_size=1, stride=1, padding=0) )

然后运行就会报错：

init() got an unexpected keyword argument ‘in_channel’

修改BUG： 这里是犯了个低级错误，没有注意到__init__()

import torch

class AE_LSTM(nn.Module):

def __init__(self, in_channel=1):

super(AE_LSTM, self).__init__()

self.encoder = nn.Sequential( nn.LSTM(in_channel, 16, batch_first=True, bidirectional=True), nn.InstanceNorm1d(16),

nn.ReLU(),

nn.LSTM(16, 32, batch_first=True, bidirectional=True),

nn.InstanceNorm1d(32),

nn.ReLU(),

nn.LSTM(32, 32, batch_first=True, bidirectional=True),

nn.InstanceNorm1d(32),

nn.ReLU(),

# nn.MaxPool1d(kernel_size=8),

nn.AvgPool1d(kernel_size=8),

nn.Conv1d(32, 32, kernel_size=1, stride=1, padding=0) )

就不会报这个错了

参考文章