我正在尝试使用 pytorch 库构建一个带有一个神经元的神经网络。这是我的代码(错误在底部)import numpy as npimport randomimport matplotlib.pyplot as pltx_train = np.array([random.randint(1,1000) for x in range(1000)], dtype = np.float32)y_train = np.array([int(num*3+1) for num in x_train], dtype = np.float32)x_test = np.array([random.randint(1,1000) for x in range(1000)], dtype = np.float32)y_test = np.array([int(num*3+1) for num in x_train], dtype = np.float32)X_train = torch.from_numpy(x_train)Y_train = torch.from_numpy(y_train)plt.figure(figsize = (8,8))plt.scatter(X_train, Y_train)plt.show()X_test = torch.from_numpy(x_test)Y_test = torch.from_numpy(y_test)input_size = 1hidden_size = 1output_size = 1learning_rate = 0.1w1 = torch.rand(input_size, hidden_size, requires_grad = True)b1 = torch.rand(hidden_size, output_size, requires_grad = True)for i in range(100): y_pred = X_train.mm(w1).clamp(min = 0).add(b1) loss = (Y_train-y_pred).pow(2).sum() loss.backward() with torch.no_grad(): w1-=w1.grad*learning_rate b1 -= b1.grad*learning_rate w1.grad.zero_() b1.grad.zero_()当我运行此代码时,它给了我一个运行时错误:RuntimeError Traceback (most recent call last)<ipython-input-84-5142b17ecfff> in <module> 32 33 for i in range(100):---> 34 y_pred = X_train.mm(w1).clamp(min = 0).add(b1) 35 loss = (Y_train-y_pred).pow(2).sum() 36 RuntimeError: matrices expected, got 1D, 2D tensors at C:\w\1\s\windows\pytorch\aten\src\TH/generic/THTensorMath.cpp:192那行代码有什么问题以及如何使其按计划工作。
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烙印99
TA贡献1829条经验 获得超13个赞
torch
需要 2D 输入,因此您需要为输入张量添加一个新维度。
X_train = torch.from_numpy(x_train[..., np.newaxis]) X_test = torch.from_numpy(x_test[..., np.newaxis])
正如上面有人评论的那样,您还可以使用torch.unsqueeze
:
for i in range(100): y_pred = torch.unsqueeze(X_train, 1).mm(w1).clamp(min = 0).add(b1) loss = (Y_train-y_pred).pow(2).sum()
两者都做同样的事情。前者将其应用于numpy
数组,后者将其应用于torch
张量。两者都会产生这种形状,这是正确的格式torch
:
Out[13]: torch.Size([1000, 1])
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