Jupyter Kernel crash/dies when use large Neural Network layer, any idea pls?
I am experimenting Autoencoder with Pytorch. It seems when I use relatively larger neural network for instance nn.Linear(250*250, 40*40) as the first layer, the Jupyter kernel keep crashing. when I use smaller layer size e.g. nn.Linear(250*250, 20*20). the Jupyter kernel is ok. Any idea how to fix this? So I can run larger network.Thank you. The entire network is as below.

# 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


I have found the root cause. I am running a docker ubuntu image/package on windows. the memory setting is set too low, when I increase the memory setting on docker. my ubuntu environment got more memory, then I can larger matrix operations.
