I am trying to implemnt this model. I have the following code:
class MyModelA(nn.Module):
def __init__(self):
super(MyModelA, self).__init__()
self.conv1 = nn.Sequential(
nn.Conv1d(in_channels=256, out_channels=252, kernel_size=5),
nn.softmax(),
nn.Avgpool1d(kernel_size= 2, stride=2),
nn.Dropout(p=0.25))
self.conv2 = nn.Conv1d(in_channels=126, out_channels=122, kernel_size= 5),
nn.softmax(),
nn.Avgpool1d(kernel_size= 2, stride=2),
nn.Dropout(p=0.25)
def forward(self, x):
x = self.fc1(x)
return x
class MyModelB(nn.Module):
def __init__(self):
super(MyModelB, self).__init__()
self.conv1 = nn.Sequential(
nn.Conv1d(in_channels=256, out_channels=252, kernel_size=5),
nn.softmax(),
nn.Avgpool1d(kernel_size= 2, stride=2),
nn.Dropout(p=0.25))
self.conv2 = nn.Conv1d(in_channels=126, out_channels=122, kernel_size= 5),
nn.softmax(),
nn.Avgpool1d(kernel_size= 2, stride=2),
nn.Dropout(p=0.25)
def forward(self, x):
x = self.fc1(x)
return x
class MyModelC(nn.Module):
def __init__(self):
super(MyModelC, self).__init__()
self.conv1 = nn.Sequential(
nn.Conv1d(in_channels=256, out_channels=252, kernel_size=5),
nn.softmax(),
nn.Avgpool1d(kernel_size= 2, stride=2),
nn.Dropout(p=0.25))
self.conv2 = nn.Conv1d(in_channels=126, out_channels=122, kernel_size= 5),
nn.softmax(),
nn.Avgpool1d(kernel_size= 2, stride=2),
nn.Dropout(p=0.25)
class MyEnsemble(nn.Module):
def __init__(self, modelA, modelB):
super(MyEnsemble, self).__init__()
self.modelA = modelA
Now i have to merge this three classes (MyModelA, MyModelB and MyModelC) into one. Do i use
tensor.cat(MyModelA, MyModelB and MyModelC)
or is there something else that i should do to merge this 3 models ?
Please note the code is not complete, i am editing still. Thanks to @ptrblck for the model converging cocept code.