I have built a DNN with only one hidden layer, the following are the parameters:

input_size = 100

hidden_size = 20

output_size = 2

def **init**():

self.linear1 = nn.Linear()

self.linear2 = nn.Linear()

def forward():

x1 = F.leaky_relu()

return F.leaky_relu()

#unimportant codes omitted

loss_function = nn.CrossEntropyLoss()

optimizer = optim.SGD(model.parameters(), lr=0.02)

normalized word vectors of size 100 from authoritative github are used as input

My purpose is to identify whether a word is an event. For example, âdoughtâ is an event but âdogâ is not.

After training, the 2-dimensional output tensors are almost the same (say,(-0.8,-1.20) and (-0.8,-1.21), (-0.2,-1.01) and (-0.2,-1.02)) even if the activation function and loss function are changed.

Could someone tell me the reason? I tried my best but failed to solve it.