Multilayer Perceptron

class MLP(nn.Module):
    def __init__(self, hidden_layers=[800, 800], droprates=[0, 0]):
        super(MLP, self).__init__()
        self.model = nn.Sequential()
        self.model.add_module("dropout0",MyDropout(p=droprates[0]))
        self.model.add_module("input", nn.Linear(28*28, hidden_layers[0]))
        self.model.add_module("tanh", nn.Tanh())
        
        # Add hidden layers
        for i,d in enumerate(hidden_layers[:-1]):
            self.model.add_module("dropout_hidden"+str(i+1), MyDropout(p=droprates[1]))
            self.model.add_module("hidden"+str(i+1), nn.Linear(hidden_layers[i], hidden_layers[i+1]))
            self.model.add_module("tanh_hidden"+str(i+1), nn.Tanh())
        self.model.add_module("final",nn.Linear(hidden_layers[-1], 10))        
        
    def forward(self, x):
        # Turn to 1D
        x = x.view(x.shape[0], 28*28)
        x = self.model(x)
        return x