I am doing regression problem for images using CNN.My training decreases for certain epoch but after 10 epochs, it stopped decreasing.It starts fluctuating around the loss at epoch 10.I tried decreasing lr too, but it is also not helpful.I increased the network complexity but it is not helping too.Anyone having any ideas?
My network is given below -
class Purity(nn.Module):
def init(self, dropout=True):
super(Purity, self).init()
print(“Purity predictor model”)
self.reg_features = nn.Sequential(OrderedDict([
(“conv1”, nn.Conv2d(6, 96, kernel_size=11, stride=4)),
(“relu1”, nn.ReLU(inplace=True)),
(“pool1”, nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True)),
(“norm1”, nn.LocalResponseNorm(5, 1.e-4, 0.75)),
(“conv2”, nn.Conv2d(96, 256, kernel_size=5, padding=2, groups=2)),
(“relu2”, nn.ReLU(inplace=True)),
(“pool2”, nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True)),
(“norm2”, nn.LocalResponseNorm(5, 1.e-4, 0.75)),
(“conv3”, nn.Conv2d(256, 384, kernel_size=3, stride=2)),
(“relu3”, nn.ReLU(inplace=True)),
(“conv4”, nn.Conv2d(384, 384, kernel_size=3, padding=1, groups=2)),
(“relu4”, nn.ReLU(inplace=True)),
(“conv5”, nn.Conv2d(384, 256, kernel_size=3, padding=1, groups=2)),
(“relu5”, nn.ReLU(inplace=True)),
(“pool5”, nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True)),
]))
self.regressor = nn.Sequential(OrderedDict([
(“fc8”, nn.Linear(33256, 4096)),
(“relu8”, nn.ReLU(inplace=True)),
(“drop6”, nn.Dropout() if dropout else Id()),
(“fc9”, nn.Linear(4096, 1024)),
(“relu9”, nn.ReLU(inplace=True)),
(“drop9”, nn.Dropout() if dropout else Id()),
(“fc10”, nn.Linear(1024, 1)),
(“sigmoid”, nn.Sigmoid())
]))