RuntimeError: mat1 and mat2 shapes cannot be multiplied (200x16 and 32x1)

Question

我想连接两个全连接层。然后，在连接它们之后，我想用 Fully Connected 层构建另一个神经网络。
我可以看到错误是由于未正确设置 cat_x = torch.cat([x, x1]) 引起的。但是，我不知道如何解决这个问题。

import torch
from torch import nn, optim
import numpy as np
from matplotlib import pyplot as plt
 
class Regression(nn.Module):
    def __init__(self):
        super().__init__()
        self.linear1 = nn.Linear(2, 32)
        self.linear2 = nn.Linear(32, 16)
        self.linear3 = nn.Linear(32, 1)
 
    def forward(self, input):
        x = nn.functional.relu(self.linear1(input))
        x = nn.functional.relu(self.linear2(x))

        x1 = nn.functional.elu(self.linear1(input))
        x1 = nn.functional.elu(self.linear2(x1))

        cat_x = torch.cat([x, x1])

        cat_x = self.linear3(cat_x)

        return cat_x
 
def train(model, optimizer, E, iteration, x, y):
    losses = []
    for i in range(iteration):
        optimizer.zero_grad()                   # 勾配情報を0に初期化
        y_pred = model(x)                       # 予測
        loss = E(y_pred.reshape(y.shape), y)    # 損失を計算(shapeを揃える)
        loss.backward()                         # 勾配の計算
        optimizer.step()                        # 勾配の更新
        losses.append(loss.item())              # 損失値の蓄積
        print('epoch=', i+1, 'loss=', loss)
    return model, losses
 
def test(model, x):
    y_pred = model(x).data.numpy().T[0]  # 予測
    return y_pred

x = np.random.uniform(0, 10, 100)                                   # x軸をランダムで作成
y = np.random.uniform(0.9, 1.1, 100) * np.sin(2 * np.pi * 0.1 * x)  # 正弦波を作成
x = torch.from_numpy(x.astype(np.float32)).float()                  # xをテンソルに変換
y = torch.from_numpy(y.astype(np.float32)).float()                  # yをテンソルに変換
X = torch.stack([torch.ones(100), x], 1)                            # xに切片用の定数1配列を結合
 
net = Regression()
 
 
optimizer = optim.RMSprop(net.parameters(), lr=0.01)                # 最適化にRMSpropを設定
E = nn.MSELoss()   
net, losses = train(model=net, optimizer=optimizer, E=E, iteration=5000, x=X, y=y)

错误信息

/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py in linear(input, weight, bias)
   1846     if has_torch_function_variadic(input, weight, bias):
   1847         return handle_torch_function(linear, (input, weight, bias), input, weight, bias=bias)
-> 1848     return torch._C._nn.linear(input, weight, bias)
   1849 
   1850 

RuntimeError: mat1 and mat2 shapes cannot be multiplied (200x16 and 32x1)

Answer 1

因为 x 和 x1 的维度都是 (100,16)，所以 torch.cat 运算符在第一个维度上连接（因为它们在那个方向上的大小相似） .为了使您的代码正常工作，请将 cat_x = torch.cat([x, x1]) 更改为 cat_x = torch.cat([x, x1], dim=1)

Answer 2

尝试：

cat_x = torch.cat([x, x1], dim=1)

RuntimeError: mat1 and mat2 shapes cannot be multiplied (200x16 and 32x1)

RuntimeError: mat1 and mat2 shapes cannot be multiplied (200x16 and 32x1)

python

machine-learning

deep-learning

pytorch