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define_network.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
class AutoEncoder_1(nn.Module):
def __init__(self, input_size=48, layer1_size=20):
super(AutoEncoder_1, self).__init__()
self.input_size = input_size
self.layer1_size = layer1_size
self.encoder1 = nn.Linear(input_size, layer1_size)
self.decoder1 = nn.Linear(layer1_size, input_size)
def forward(self, x):
x = F.tanh(self.encoder1(x))
x = self.decoder1(x)
return x
class AutoEncoder_2(nn.Module):
def __init__(self, input_size=48, layer1_size=20, layer2_size=10):
super(AutoEncoder_2, self).__init__()
self.input_size = input_size
self.layer1_size = layer1_size
self.layer2_size = layer2_size
self.encoder1 = nn.Linear(input_size, layer1_size)
self.encoder2 = nn.Linear(layer1_size, layer2_size)
self.decoder2 = nn.Linear(layer2_size, layer1_size)
self.decoder1 = nn.Linear(layer1_size, input_size)
def forward(self, x):
x = F.tanh(self.encoder1(x))
x = F.tanh(self.encoder2(x))
x = F.tanh(self.decoder2(x))
x = self.decoder1(x)
return x
class AutoEncoder_3(nn.Module):
def __init__(self, input_size=48, layer1_size=20, layer2_size=10, layer3_size=5):
super(AutoEncoder_3, self).__init__()
self.input_size = input_size
self.layer1_size = layer1_size
self.layer2_size = layer2_size
self.layer3_size = layer3_size
self.encoder1 = nn.Linear(input_size, layer1_size)
self.encoder2 = nn.Linear(layer1_size, layer2_size)
self.encoder3 = nn.Linear(layer2_size, layer3_size)
self.decoder3 = nn.Linear(layer3_size, layer2_size)
self.decoder2 = nn.Linear(layer2_size, layer1_size)
self.decoder1 = nn.Linear(layer1_size, input_size)
def forward(self, x):
x = F.tanh(self.encoder1(x))
x = F.tanh(self.encoder2(x))
x = F.tanh(self.encoder3(x))
x = F.tanh(self.decoder3(x))
x = F.tanh(self.decoder2(x))
x = self.decoder1(x)
return x
class Compression_encoder(nn.Module):
def __init__(self, input_size=48, layer1_size=20, layer2_size=10, layer3_size=5):
super(Compression_encoder, self).__init__()
self.input_size = input_size
self.layer1_size = layer1_size
self.layer2_size = layer2_size
self.layer3_size = layer3_size
self.encoder1 = nn.Linear(input_size, layer1_size)
self.encoder2 = nn.Linear(layer1_size, layer2_size)
self.encoder3 = nn.Linear(layer2_size, layer3_size)
def forward(self, x):
x = F.tanh(self.encoder1(x))
x = F.tanh(self.encoder2(x))
x = F.tanh(self.encoder3(x))
return x