Ожидаемые параметры Conv2d

Ниже код:

import torch 
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
import torch.utils.data as data_utils
import numpy as np

train_dataset = []
mu, sigma = 0, 0.1 # mean and standard deviation
num_instances = 20
batch_size_value = 10
for i in range(num_instances) :
    image = []
    image_x = np.random.normal(mu, sigma, 1000).reshape((1 , 100, 10))
    train_dataset.append(image_x)
labels = [1 for i in range(num_instances)]
x2 = torch.tensor(train_dataset).float()
y2 = torch.tensor(labels).long()
my_train2 = data_utils.TensorDataset(x2, y2)
train_loader2 = data_utils.DataLoader(my_train2, batch_size=batch_size_value, shuffle=False)    

# Device configuration
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

# Hyper parameters
num_epochs = 5
num_classes = 1
batch_size = 5
learning_rate = 0.001

# Convolutional neural network (two convolutional layers)
class ConvNet(nn.Module):
    def __init__(self, num_classes=1):
        super(ConvNet, self).__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2),
            nn.BatchNorm2d(16),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2))
        self.layer2 = nn.Sequential(
            nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=2),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2))
        self.fc = nn.Linear(7*7*32, num_classes)

    def forward(self, x):
        out = self.layer1(x)
        out = self.layer2(out)
        out = out.reshape(out.size(0), -1)
        out = self.fc(out)
        return out

model = ConvNet(num_classes).to(device)

# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

# Train the model
total_step = len(train_loader2)
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader2):
        images = images.to(device)
        labels = labels.to(device)

        # Forward pass
        outputs = model(images)
        loss = criterion(outputs, labels)

        # Backward and optimize
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if (i+1) % 100 == 0:
            print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}' 
                   .format(epoch+1, num_epochs, i+1, total_step, loss.item()))

Возвращает ошибку:

RuntimeError: size mismatch, m1: [10 x 1600], m2: [1568 x 1] at /pytorch/aten/src/THC/generic/THCTensorMathBlas.cu:249

Чтение документации для conv2d, я попытался изменить первый параметр на 10X100, чтобы он соответствовал

Input-входной тензор формы (minibatch×in_channels×iH×iW)

Из https://pytorch.org/docs/stable/nn.html#torch.nn.functional.conv2d

Но затем получил ошибку:

RuntimeError: Given groups=1, weight[16, 1000, 5, 5], so expected input[10, 1, 100, 10] to have 1000 channels, but got 1 channels instead

Поэтому я не уверен, исправил ли я первоначальную ошибку или просто вызвал новую. один?

Как должно быть установлено Conv2d, чтобы соответствовать форме изображения (10,100)?