Pytorch: why print(model) does not show the activation functions?
I need to extract weights, bias and at least the type of activation function from a trained NN in pytorch. 

I know that to extract the weights and biases the command is:

model.parameters()

but I can't figure out how to extract also the activation function used on the layers.Here is my network

class NetWithODE(torch.nn.Module):
    def __init__(self, n_feature, n_hidden, n_output, sampling_interval, scaler_features):
        super(NetWithODE, self).__init__()
        self.hidden = torch.nn.Linear(n_feature, n_hidden)  # hidden layer
        self.predict = torch.nn.Linear(n_hidden, n_output)  # output layer
        self.sampling_interval = sampling_interval
        self.device = torch.device("cpu")
        self.dtype = torch.float
        self.scaler_features = scaler_features

    def forward(self, x):
        x0 = x.clone().requires_grad_(True)
        # activation function for hidden layer
        x = F.relu(self.hidden(x))
        # linear output, here r should be the output
        r = self.predict(x)
        # Now the r enters the integrator
        x = self.integrate(r, x0)

        return x

    def integrate(self, r, x0):
        # RK4 steps per interval
        M = 4
        DT = self.sampling_interval / M
        X = x0

        for j in range(M):
            k1 = self.ode(X, r)
            k2 = self.ode(X + DT / 2 * k1, r)
            k3 = self.ode(X + DT / 2 * k2, r)
            k4 = self.ode(X + DT * k3, r)
            X = X + DT / 6 * (k1 + 2 * k2 + 2 * k3 + k4)

        return X

    def ode(self, x0, r):
        qF = r[0, 0]
        qA = r[0, 1]
        qP = r[0, 2]
        mu = r[0, 3]

        FRU = x0[0, 0]
        AMC = x0[0, 1]
        PHB = x0[0, 2]
        TBM = x0[0, 3]

        fFRU = qF * TBM  
        fAMC = qA * TBM  
        fPHB = qP - mu * PHB
        fTBM = mu * TBM

        return torch.stack((fFRU, fAMC, fPHB, fTBM), 0)


if I run the command 

print(model)



I get

NetWithODE(
  (hidden): Linear(in_features=4, out_features=10, bias=True)
  (predict): Linear(in_features=10, out_features=4, bias=True)
)


But where can I get the activation function (in this case Relu)?

I have pytorch 1.4.

There are two ways of adding operations to the network graph: lowlevel functional way and more advanced object way. You need latter to make your structure observable, In first case is just calling (not exactly, but...) a function without storing info about it. So, instead of 

    def forward(self, x):
    ...
        x = F.relu(self.hidden(x))


it must be something like

def __init__(...):
    ...
    self.myFirstRelu= torch.nn.ReLU()

def forward(self, x):
    ...
    x1 = self.hidden(x)
    x2 = self.myFirstRelu(x1)


Anyway a mix of two theese ways is generally bad idea, although even torchvision models have such inconsistiencies: models.inception_v3 not register the poolings for example >:-( (EDIT: it is fixed in june 2020, thanks, mitmul!).  



UPD:
- Thanks, that works, now if I print I see ReLU(). But this seems to only print the function in the same order they are defined in the init. Is there a way to get the associations between layers and activation functions? For example I want to know which activation was applyed to layer 1, which to layer 2 end so on...

There is no uniform way, but here is some tricks:
object way:

-just init them in order

-use torch.nn.Sequential 

-hook callbacks on nodes like that -

def hook( m, i, o):
    print( m._get_name() )

for ( mo ) in model.modules():
    mo.register_forward_hook(hook)


functional and object way:

-make use of internal model graph, builded on forward pass, as torchviz do (https://github.com/szagoruyko/pytorchviz/blob/master/torchviz/dot.py), or just use plot generated by said torchviz.
