@KFrank
I have done that as well.Here is my model code
model_1 = CNN(num_classes=5, hid_size=128).to(device)
The model code is:
class ConvNormPool(nn.Module):
“”“Conv Skip-connection module”""
def init(
self,
input_size,
hidden_size,
kernel_size,
norm_type=‘bachnorm’
):
super().init()
self.kernel_size = kernel_size
self.conv_1 = nn.Conv1d(
in_channels=input_size,
out_channels=hidden_size,
kernel_size=kernel_size
)
self.conv_2 = nn.Conv1d(
in_channels=hidden_size,
out_channels=hidden_size,
kernel_size=kernel_size
)
self.conv_3 = nn.Conv1d(
in_channels=hidden_size,
out_channels=hidden_size,
kernel_size=kernel_size
)
self.swish_1 = Swish()
self.swish_2 = Swish()
self.swish_3 = Swish()
if norm_type == 'group':
self.normalization_1 = nn.GroupNorm(
num_groups=8,
num_channels=hidden_size
)
self.normalization_2 = nn.GroupNorm(
num_groups=8,
num_channels=hidden_size
)
self.normalization_3 = nn.GroupNorm(
num_groups=8,
num_channels=hidden_size
)
else:
self.normalization_1 = nn.BatchNorm1d(num_features=hidden_size)
self.normalization_2 = nn.BatchNorm1d(num_features=hidden_size)
self.normalization_3 = nn.BatchNorm1d(num_features=hidden_size)
self.pool = nn.MaxPool1d(kernel_size=2)
def forward(self, input):
#print("INPUT SHAPE SKIP")
#print(input.shape)
conv1 = self.conv_1(input)
x = self.normalization_1(conv1)
x = self.swish_1(x)
x = F.pad(x, pad=(self.kernel_size - 1, 0))
x = self.conv_2(x)
x = self.normalization_2(x)
x = self.swish_2(x)
x = F.pad(x, pad=(self.kernel_size - 1, 0))
conv3 = self.conv_3(x)
x = self.normalization_3(conv1+conv3)
x = self.swish_3(x)
x = F.pad(x, pad=(self.kernel_size - 1, 0))
x = self.pool(x)
return x
class CNN(nn.Module):
def init(
self,
input_size = 1,
hid_size = 256,
kernel_size = 5,
num_classes = 5,
):
super().__init__()
self.conv1 = ConvNormPool(
input_size=input_size,
hidden_size=hid_size,
kernel_size=kernel_size,
)
self.conv2 = ConvNormPool(
input_size=hid_size,
hidden_size=hid_size//2,
kernel_size=kernel_size,
)
self.conv3 = ConvNormPool(
input_size=hid_size//2,
hidden_size=hid_size//4,
kernel_size=kernel_size,
)
self.avgpool = nn.AdaptiveAvgPool1d((1))
self.fc = nn.Linear(in_features=hid_size//4, out_features=num_classes)
def forward(self, input):
#print("INPUT SHAPE is ")
#print(input.shape)
#print("INPUT SHAPE is ")
#print(input.shape)
#input = input.unsqueeze(1)
#input = input.unsqueeze(0)
x = self.conv1(input)
x = self.conv2(x)
x = self.conv3(x)
x = self.avgpool(x)
# print(x.shape) # num_features * num_channels
x = x.view(-1, x.size(1) * x.size(2))
**x = F.softmax(self.fc(x), dim=1)**
return x
According to your suggestion, I did thechange in the last part of CNN class …I changed x = F.softmax(self.fc(x), dim=1) to
x = F.log_softmax(self.fc(x), dim=1)
Now I get the NLLLloss Values as:
Will this Log_softmax layer also work proper for CrossEntropyLoss or I need to change it to simple softmax only??
Kindly pls.tell me if I am wrong…And also where and what code to insert for correction…I am pretty new to pytorch So pls help!!!
I would be very grateful 
