I would like to connect two Fully Connected layers. Then, after connecting them, I want to build another neural net with the Fully Connected layer.
I can see that the error is caused by not setting cat_x = torch.cat([x, x1]) properly. However, I do not know how to solve this problem.

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)

error message

/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)

CodePudding user response：

since both x and x1 have dimensions of (100,16), the torch.cat operator concatenates in the 1st dimension (since they are of similar size in that direction). For your code to work change the cat_x = torch.cat([x, x1]) to cat_x = torch.cat([x, x1], dim=1)

CodePudding user response：

Try:

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