This code runs fine to create a simple feed-forward neural Network. The layer (torch.nn.Linear) is assigned to the class variable by using self.
class MultipleRegression3L(torch.nn.Module):
def __init__(self, num_features):
super(MultipleRegression3L, self).__init__()
self.layer_1 = torch.nn.Linear(num_features, 16)
## more layers
self.relu = torch.nn.ReLU()
def forward(self, inputs):
x = self.relu(self.layer_1(inputs))
x = self.relu(self.layer_2(x))
x = self.relu(self.layer_3(x))
x = self.layer_out(x)
return (x)
def predict(self, test_inputs):
return self.forward(test_inputs)
However, when I tried to store the layer using the list:
class MultipleRegression(torch.nn.Module):
def __init__(self, num_features, params):
super(MultipleRegression, self).__init__()
number_of_layers = 3 if not 'number_of_layers' in params else params['number_of_layers']
number_of_neurons_in_each_layer = [16, 32, 16] if not 'number_of_neurons_in_each_layer' in params else params['number_of_neurons_in_each_layer']
activation_function = "relu" if not 'activation_function' in params else params['activation_function']
self.layers = []
v1 = num_features
for i in range(0, number_of_layers):
v2 = number_of_neurons_in_each_layer[i]
self.layers.append(torch.nn.Linear(v1, v2))
v1 = v2
self.layer_out = torch.nn.Linear(v2, 1)
if activation_function == "relu":
self.act_func = torch.nn.ReLU()
else:
raise Exception("Activation function %s is not supported" % (activation_function))
def forward(self, inputs):
x = self.act_func(self.layers[0](inputs))
for i in range(1, len(self.layers)):
x = self.act_func(self.layers[i](x))
x = self.layer_out(x)
return (x)
The two models do not behave the same way. What can be wrong here?
