Thanks for your response! I really appreciate it.
If I’m understanding you correctly, it should be something like this:
self.mod = nn.Sequential(
            nn.Linear(input_dim,100),
            nn.ReLU(),
            nn.Dropout(0.5),
            nn.Linear(100, 50),
            nn.ReLU(),
            nn.Dropout(0.5),
            nn.Linear(50, 25),
            nn.ReLU(),
            nn.Linear(25, 1),
            nn.Linear(1, 1),
            nn.Sigmoid()
        )

So from this I am learning that the number of units in the Keras definition is the second number in the Torch layer definition. In essence:

Dense(units=100, activation=“relu”, kernel_initializer=he_uniform_initializer)
nn.Linear(input_dim, 100)
nn.ReLU()

I’m still not certain how to reat the Input() definition from Keras. Is it a linear layer like I have it?


input_tensor = Input(shape=(1, input_tensor_length))



Also, do you think the he_uniform_initialization matters?