CODEWITHSUNDEEP
keraspytorch
Milind Dalvi
Milind Dalvi

Reputation: 846

Pytorch to Keras code equivalence

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

Upvotes: 11

Views: 13363

Answers (3)

sarjit07
sarjit07

Reputation: 10789

  model = Sequential()
  model.add(Dense(30, input_dim=5, activation='relu'))
  model.add(Dense(3, activation=None))

Upvotes: -1

nerox8664
nerox8664

Reputation: 81

Looks like a

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

If you are trying to convert Pytorch model to Keras model, you can also try a Pytorch2Keras converter.

It supports base layers like Conv2d, Linear, Activations, Element-wise operations. So, I've converted ResNet50 with error 1e-6.

Upvotes: 2

Wasi Ahmad
Wasi Ahmad

Reputation: 37771

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!

Upvotes: 13

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