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[英]pytorch nn.Sequential(*list) TypeError: list is not a Module subclass
[英]using list in creating pytorch NN module
这段代码运行良好,可以创建一个简单的前馈神经网络。 使用self
将层 ( torch.nn.Linear
) 分配给 class 变量。
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)
但是,当我尝试使用列表存储图层时:
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)
这两个模型的行为方式不同。 这里有什么问题?
Pytorch 需要保留 model 中的模块图,所以使用list
不起作用。 使用self.layers = torch.nn.ModuleList()
解决了这个问题。
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