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ARNN.py
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ARNN.py
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import torch
import torch.nn as nn
class FBRNN(nn.Module):
#Simple A-RNN
def __init__(self):
super(FBRNN, self).__init__()
drop=0.4
self.conv1= nn.Conv1d(1, 8, kernel_size=(32))
self.bn1=nn.BatchNorm1d(8)
self.relu1= nn.ReLU()
self.pool1= nn.MaxPool1d(kernel_size=(2), stride=(2))
self.drop1= nn.Dropout(p=drop)
self.conv2= nn.Conv1d(8, 10, kernel_size=(32))
self.bn2=nn.BatchNorm1d(10)
self.relu2= nn.ReLU()
self.pool2= nn.MaxPool1d(kernel_size=(2), stride=(2))
self.drop2= nn.Dropout(p=drop)
self.LSTM1= nn.LSTM(input_size=10,hidden_size=100,num_layers=1,batch_first=True,bidirectional=False)
self.drop3= nn.Dropout(p=drop)
self.LSTM2= nn.LSTM(input_size=100,hidden_size=50,num_layers=1,batch_first=True,bidirectional=False)
self.drop4= nn.Dropout(p=drop)
self.LSTM3= nn.LSTM(input_size=50,hidden_size=20,num_layers=1,batch_first=True,bidirectional=False)
self.drop5= nn.Dropout(p=drop)
self.LSTM4= nn.LSTM(input_size=20,hidden_size=10,num_layers=1,batch_first=True,bidirectional=False)
self.drop6= nn.Dropout(p=drop)
self.LSTM5= nn.LSTM(input_size=10,hidden_size=5,num_layers=1,batch_first=True,bidirectional=False)
self.drop7= nn.Dropout(p=drop)
self.outL= nn.Linear(40,2)
def forward(self,x):
x=self.conv1(x)
#print(x.shape)
x=self.bn1(x)
x=self.relu1(x)
x=self.pool1(x)
x=self.drop1(x)
x=self.conv2(x)
x=self.bn2(x)
x=self.relu2(x)
x=self.pool2(x)
x=self.drop2(x)
x=x.permute(0,2,1)#batch,sequence,channels
#print(x.shape)
x,_=self.LSTM1(x)
x=self.drop3(x)
x,_=self.LSTM2(x)
x=self.drop4(x)
x,_=self.LSTM3(x)
x=self.drop5(x)
x,_=self.LSTM4(x)
x=self.drop6(x)
x,_=self.LSTM5(x)
x=self.drop7(x)
#print(x.shape)
x=x.reshape(x.size(0), -1)
#print(x.shape)
x=self.outL(x)
return(x)