ミニバッチ学習の際、NNの入力の数が合わない

import torch.nn as nn

# Networkをclassとして作成する.
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__() 

        self.relu = nn.ReLU()
        self.fc1 = nn.Linear(3, 3)
        self.fc2 = nn.Linear(3, 1)

    def forward(self, x):
        x = x.reshape(1, -1)
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        return x

import torch.optim as optim

device = torch.device("cpu")
net = Net()
net = net.to(device)
criterion = nn.MSELoss()

teacher_X = (shape = (1036, 3), dtype= np.float32のnp.ndarray)
teacher_y = (shape = (1036, 1), dtype= np.float32のnp.ndarray)

dataset = torch.utils.data.TensorDataset(teacher_X, teacher_y)
#説明変数とラベルをくっつける

train, test = train_test_split(dataset, test_size=0.5)
#trainデータとtestデータに分ける

trainloader = torch.utils.data.DataLoader(train, batch_size = 2*37, shuffle = True)
testloader = torch.utils.data.DataLoader(test, batch_size = 2*37, shuffle = False)
#バッチサイズ74に分割する

for epoch in range(1):
    for batch in trainloader:
      inputs, labels = batch
      inputs, labels = inputs.to(device), labels.to(device) 
      optimizer.zero_grad() 
      outputs = net(inputs)
      output_list.append(outputs.item())
      loss = criterion(outputs, labels)
      loss.backward()
      optimizer.step()

---> 35         x = self.fc1(x)
---> 20       outputs = net(inputs)
RuntimeError: mat1 and mat2 shapes cannot be multiplied (1x222 and 3x3)

for (inputs, labels) in trainloader:
    break
print(inputs)
print(labels)
---出力---
tensor([[-2.4840e-01, -4.5650e-02, -1.0503e+00],
        [ 8.9746e-01,  1.1928e+00,  2.3086e+00],
        [ 3.2453e-01,  6.6884e-01, -1.0383e+00],
        [-8.2133e-01, -1.1412e+00, -7.7657e-01],
        [-2.4840e-01,  9.7248e-02, -7.0233e-01],
        [-8.2133e-01, -1.2841e+00, -7.0156e-01],
        [ 8.9746e-01,  1.1452e+00, -1.0240e+00],
        [ 3.2453e-01,  8.5937e-01, -6.6869e-01],
        [-8.2133e-01, -7.1251e-01, -7.0272e-01],
        [ 2.6163e+00,  1.8597e+00, -6.9924e-01],
        [-2.4840e-01, -4.5650e-02, -6.8068e-01],
        [ 1.4704e+00,  1.3357e+00,  3.1652e-01],
        [-2.4840e-01,  9.7248e-02, -1.0287e+00],
        [-2.4840e-01,  1.4488e-01, -1.0256e+00],
        [-2.4840e-01,  9.7248e-02, -1.0503e+00],
        [ 2.0433e+00,  1.3833e+00, -7.0233e-01],
        [ 3.2453e-01,  8.5937e-01, -6.8802e-01],
        [ 3.2453e-01,  8.5937e-01, -1.0503e+00],
        [-2.4840e-01,  1.9823e-03, -1.0252e+00],
        [-8.2133e-01, -7.1251e-01, -6.9924e-01],
        [ 8.9746e-01,  1.0975e+00, -1.0383e+00],
        [ 2.6163e+00,  1.8597e+00, -6.9924e-01],
        [ 3.2453e-01,  6.6884e-01, -9.6990e-01],
        [-2.4840e-01,  1.4488e-01, -1.0472e+00],
        [-8.2133e-01, -7.1251e-01,  1.7461e-01],
        [ 3.2453e-01,  8.5937e-01, -6.9924e-01],
        [ 3.2453e-01,  8.5937e-01, -1.0383e+00],
        [-8.2133e-01, -7.1251e-01, -1.0240e+00],
        [-8.2133e-01, -1.2841e+00, -1.0306e+00],
        [-2.4840e-01,  1.4488e-01, -1.0368e+00],
        [-2.4840e-01, -4.5650e-02, -6.9924e-01],
        [-8.2133e-01, -1.2841e+00,  1.2808e+00],
        [-8.2133e-01, -7.1251e-01,  1.2546e+00],
        [-8.2133e-01, -8.0777e-01, -6.9924e-01],
        [ 1.4704e+00,  1.3357e+00, -1.0472e+00],
        [-2.4840e-01,  1.9251e-01, -1.0391e+00],
        [ 8.9746e-01,  1.1452e+00, -1.0383e+00],
        [-8.2133e-01, -9.9830e-01,  2.2367e+00],
        [ 2.6163e+00,  1.8597e+00, -1.0472e+00],
        [ 8.9746e-01,  1.0499e+00, -6.6947e-01],
        [-2.4840e-01,  1.4488e-01, -1.0356e+00],
        [ 8.9746e-01,  1.1452e+00,  2.0787e-01],
        [ 8.9746e-01,  1.2404e+00, -1.0472e+00],
        [-8.2133e-01, -7.1251e-01, -1.0364e+00],
        [ 2.6163e+00,  1.8597e+00, -1.0480e+00],
        [-8.2133e-01, -9.0304e-01, -6.9924e-01],
        [ 8.9746e-01,  1.1452e+00, -1.0472e+00],
        [-8.2133e-01, -1.0459e+00, -1.0279e+00],
        [ 8.9746e-01,  1.1928e+00, -1.0472e+00],
        [-8.2133e-01, -1.0459e+00, -6.9924e-01],
        [-2.4840e-01,  1.4488e-01, -1.0267e+00],
        [-8.2133e-01, -7.1251e-01, -7.0272e-01],
        [-2.4840e-01,  9.7248e-02, -1.0472e+00],
        [-2.4840e-01, -2.8381e-01, -7.0233e-01],
        [-2.4840e-01,  9.7248e-02, -1.0472e+00],
        [-2.4840e-01,  9.7248e-02, -1.0472e+00],
        [-2.4840e-01,  1.4488e-01,  2.4145e+00],
        [ 8.9746e-01,  1.0499e+00, -6.6947e-01],
        [-2.4840e-01, -2.8381e-01, -7.0078e-01],
        [ 3.2453e-01,  8.5937e-01, -1.0167e+00],
        [ 3.2453e-01,  8.5937e-01,  1.6319e+00],
        [-2.4840e-01,  9.7248e-02, -6.9924e-01],
        [-8.2133e-01, -7.1251e-01, -6.7913e-01],
        [-8.2133e-01, -1.0459e+00, -1.0418e+00],
        [ 8.9746e-01,  1.0499e+00,  5.6785e-01],
        [-8.2133e-01, -7.1251e-01, -1.0349e+00],
        [-2.4840e-01,  1.9823e-03, -6.7875e-01],
        [-8.2133e-01, -7.1251e-01, -7.0233e-01],
        [-8.2133e-01, -9.9830e-01, -1.0264e+00],
        [ 1.4704e+00,  1.3357e+00, -1.0472e+00],
        [-2.4840e-01, -2.3618e-01, -6.7875e-01],
        [-2.4840e-01,  1.9823e-03,  2.2565e-01],
        [ 2.6163e+00,  1.8597e+00, -1.0472e+00],
        [ 3.2453e-01,  6.6884e-01,  4.6074e-01]])
tensor([-0.1082,  0.0319,  0.0195,  0.0483,  0.2727, -0.0772,  0.0319,  0.0294,
         0.0039,  0.0032, -0.4534,  0.0392,  0.0242, -0.0643,  0.1256,  0.0039,
        -0.0290,  0.0392,  0.2644,  0.0690,  0.0232, -0.0290,  0.0394,  0.1216,
         0.0429, -0.0664, -0.0841,  0.0420, -0.0397, -0.0127,  0.0039,  0.0036,
         0.3039,  0.0319,  0.1322, -0.0994,  0.0392,  0.0015,  0.0195,  0.0561,
        -0.0546, -0.0596,  0.0472, -0.0190, -0.0119,  0.0733,  0.0008,  0.2031,
         0.1742, -0.0218,  0.3039, -0.1418, -0.0641,  0.0671,  0.4332,  0.0111,
         0.3039,  0.1665,  0.0903,  0.0294, -0.0290, -0.0218,  0.2644,  0.0653,
         0.0251, -0.0397, -0.0982,  0.0227, -0.1652,  0.3822, -0.0301, -0.0841,
         0.0733,  0.0394])