Question

使用此模型，我尝试使用预定义的权重和偏差初始化网络：

dimensions_input = 10
hidden_layer_nodes = 5
output_dimension = 10

class Model(torch.nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.linear = torch.nn.Linear(dimensions_input,hidden_layer_nodes)
        self.linear2 = torch.nn.Linear(hidden_layer_nodes,output_dimension)

        self.linear.weight = torch.nn.Parameter(torch.zeros(dimensions_input,hidden_layer_nodes))
        self.linear.bias = torch.nn.Parameter(torch.ones(hidden_layer_nodes))

        self.linear2.weight = torch.nn.Parameter(torch.zeros(dimensions_input,hidden_layer_nodes))
        self.linear2.bias = torch.nn.Parameter(torch.ones(hidden_layer_nodes))

    def forward(self, x):
        l_out1 = self.linear(x)
        y_pred = self.linear2(l_out1)
        return y_pred

model = Model()

criterion = torch.nn.MSELoss(size_average = False)
optim = torch.optim.SGD(model.parameters(), lr = 0.00001)

def train_model():
    y_data = x_data.clone()
    for i in range(10000):
        y_pred = model(x_data)
        loss = criterion(y_pred, y_data)

        if i % 5000 == 0:
            print(loss)
        optim.zero_grad()

        loss.backward()
        optim.step()

RuntimeError：

张量的扩展大小（10）必须与现有大小（5）相匹配在非单维度1上

我的尺寸看起来正确，因为它们与相应的线性层匹配？

Answer 1

由于未定义x_data，因此提供的代码无法运行，因此我不确定这是问题所在，但是令我印象深刻的是，您应该替换< / p>

self.linear2.weight = torch.nn.Parameter(torch.zeros(dimensions_input,hidden_layer_nodes))
self.linear2.bias = torch.nn.Parameter(torch.ones(hidden_layer_nodes))

使用

self.linear2.weight = torch.nn.Parameter(torch.zeros(hidden_layer_nodes, output_dimension))
self.linear2.bias = torch.nn.Parameter(torch.ones(output_dimension))

使用PyTorch初始化权重和偏差-如何校正尺寸？

1 个答案: