RuntimeError: Given groups=1, weight of size [16, 1, 1], expected input[1, 16, 2] to have 1 channels, but got 16 channels instead

Hi, I am trying to run CNN model on synthetic dataset consisting of semi simple random data points generated by following code snippet followed by the code snippet of CNN model and its training and at the end is received error message. Whenever i change batch size it effects the expected input size i don’t understand how? Kindly guide me through…!

X1, Y1 = make_classification(n_samples=4000, n_features=2, n_redundant=0, n_informative=2, n_clusters_per_class=1, n_classes=2)
plt.scatter(X1[:, 0], X1[:, 1], marker=‘o’, c=Y1, s=25, edgecolor=‘k’)
x = torch.from_numpy(X1).to(device, dtype=torch.float)
y = torch.from_numpy(Y1).to(device, dtype=torch.long)
y = y.unsqueeze(1).repeat(1,1)

class Net(nn.Module):
def __init__(self, Bias):
    super(Net, self).__init__()
    self.conv1 = nn.Conv1d(1, 16, kernel_size=1, stride=1)
    self.relu = nn.ReLU()
    self.fc1 = nn.Linear(2*1, 16, bias = Bias)
    self.fc2 = nn.Linear(16, 1, bias = Bias)
def forward(self, x):
    x = self.relu(self.conv1(x))
    x = x.reshape(x.size(0), -1)
    x = self.relu(self.fc1(x))
    x = self.fc2(x)
    return x
   biases = False
   epochs = 50
   alpha = 1e-2
   batch_size = 16
   criterion = nn.CrossEntropyLoss()
   model = Net(biases).to(device)
   model=train(,criterion,x,y,alpha,epochs,batch_size)def dataloader(x, y):
x = x.float()
y = y.float()
data_train =, y), dim=1)
train_size = int(0.8 * len(data_train))
test_size = len(data_train) - train_size
train_dataset, test_dataset =, [train_size, test_size])

train_dataset = data_train[train_dataset.indices,:]
test_dataset = data_train[test_dataset.indices,:]
trainx = train_dataset [:,0:train_dataset.shape[1]-1]
trainy = train_dataset [:,train_dataset.shape[1]-1]
testx = test_dataset [:,0:test_dataset.shape[1]-1]
testy = test_dataset [:,test_dataset.shape[1]-1]

return trainx, trainy, testx,testy
def train(model,criterion, x, y, alpha ,epochs,batchsize):
costs = []
optimizer = torch.optim.SGD(model.parameters(), lr=alpha)    
trainx, trainy, testx,testy= dataloader(x,y)
data_train =, y), dim=1)
data_train_loader = DataLoader(data_train, batch_size=batchsize, shuffle=True)
j = 0
for i in range(epochs):
    for index,samples in enumerate(data_train_loader):
        j += 1
        if (j%50 == 0):
            acc = accuracy(model,testx,testy)
            print(f'Test accuracy is #{acc:.2f} , Iteration number is = {j}')
        cost = criterion(model(x1), y1.long())
return model
def accuracy(model,x,y):
prediction = model(x).detach().cpu().numpy()
return (np.sum(np.argmax(prediction,axis=1).reshape(-1,1)==np.array(y.cpu().numpy()).reshape(-1,1))/len(y.cpu().numpy()))*100

RuntimeError: Given groups=1, weight of size [16, 1, 1], expected input[1, 16, 2] to have 1 channels, but got 16 channels instead

Based on the error message the input shape is wrong and the error is raised in self.conv1.
Your code is neither executable nor properly formatted so I won’t be able to directly debug it.
However, print the shape of the input and check why 2 channels are used instead of 1.