Pytorch to Keras code equivalence

Question

Given a below code in PyTorch what would be the Keras equivalent?

class Network(nn.Module):

    def __init__(self, state_size, action_size):
        super(Network, self).__init__()

        # Inputs = 5, Outputs = 3, Hidden = 30
        self.fc1 = nn.Linear(5, 30)
        self.fc2 = nn.Linear(30, 3)

    def forward(self, state):
        x = F.relu(self.fc1(state))
        outputs = self.fc2(x)
        return outputs

Is it this?

model = Sequential()
model.add(Dense(units=30, input_dim=5, activation='relu'))
model.add(Dense(units=30, activation='relu'))
model.add(Dense(units=3, activation='linear'))

or is it this?

model = Sequential()
model.add(Dense(units=30, input_dim=5, activation='linear'))
model.add(Dense(units=30, activation='relu'))
model.add(Dense(units=3, activation='linear'))

or is it?

model = Sequential()
model.add(Dense(units=30, input_dim=5, activation='relu'))
model.add(Dense(units=30, activation='linear'))
model.add(Dense(units=3, activation='linear'))

Thanks

Answer 1

None of them looks correct according to my knowledge. A correct Keras equivalent code would be:

model = Sequential()
model.add(Dense(30, input_shape=(5,), activation='relu')) 
model.add(Dense(3)) 

model.add(Dense(30, input_shape=(5,), activation='relu'))

Model will take as input arrays of shape (*, 5) and output arrays of shape (*, 30). Instead of input_shape, you can use input_dim also. input_dim=5 is equivalent to input_shape=(5,).

model.add(Dense(3))

After the first layer, you don't need to specify the size of the input anymore. Moreover, if you don't specify anything for activation, no activation will be applied (equivalent to linear activation).

---

Another alternative would be:

model = Sequential()
model.add(Dense(30, input_dim=5)) 
model.add(Activation('relu'))
model.add(Dense(3)) 

Hopefully this makes sense!