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我正在用Pytorch試驗Autoencoder。當我使用比較大的神經網絡例如nn.Linear(250 * 250,40 * 40)作爲第一層時,Jupyter內核不斷崩潰。當我使用較小的圖層大小時線性(250 * 250,20 * 20)。 Jupyter內核是可以的。任何想法如何解決這個問題?所以我可以運行更大的網絡。謝謝。整個網絡如下。Jupyter內核崩潰/死亡時使用大型神經網絡層,任何想法?
# model:
class AutoEncoder(nn.Module):
def __init__(self):
super().__init__()
self.encoder = nn.Sequential(
nn.Linear(250*250, 20*20),
nn.BatchNorm1d(20*20,momentum=0.5),
nn.Dropout(0.5),
nn.LeakyReLU(),
nn.Linear(20*20, 20*20),
nn.BatchNorm1d(20*20,momentum=0.5),
nn.Dropout(0.5),
nn.LeakyReLU(),
nn.Linear(20*20, 20*20),
nn.BatchNorm1d(20*20,momentum=0.5),
nn.Dropout(0.5),
nn.LeakyReLU(),
nn.Linear(20*20, 15*15),
nn.BatchNorm1d(15*15,momentum=0.5),
nn.Dropout(0.5),
nn.LeakyReLU(),
nn.Linear(15*15, 3),
nn.BatchNorm1d(3,momentum=0.5),
#nn.Dropout(0.5),
#nn.Tanh(),
#nn.Linear(5*5,5),
)
self.decoder = nn.Sequential(
#nn.Linear(5, 5*5),
#nn.BatchNorm1d(5*5,momentum=0.5),
#nn.Dropout(0.5),
#nn.Tanh(),
nn.Linear(3, 15*15),
nn.BatchNorm1d(15*15,momentum=0.5),
nn.Dropout(0.5),
nn.LeakyReLU(),
nn.Linear(15*15, 20*20),
nn.BatchNorm1d(20*20,momentum=0.5),
nn.Dropout(0.5),
nn.LeakyReLU(),
nn.Linear(20*20, 20*20),
nn.BatchNorm1d(20*20,momentum=0.5),
nn.Dropout(0.5),
nn.LeakyReLU(),
nn.Linear(20*20, 250*250),
nn.BatchNorm1d(250*250,momentum=0.5),
nn.Dropout(0.5),
nn.Sigmoid(),
)
def forward(self, x):
encoded = self.encoder(x)
decoded = self.decoder(encoded)
return encoded, decoded