Tensorflow 到 PyTorch - model.predict 等效

Question

1. I am attempting to retrieve the mean squared error of my training. within the original code based in TensorFlow, I am moving this code over to PyTorch (for research reasons).

   the original TensorFlow code:

    print("Calculating threshold")
    x_opt_predictions = model.predict(x_opt)
    print("Calculating MSE on optimization set...")
    mse = np.mean(np.power(x_opt - x_opt_predictions, 2), axis=1)
    print("mean is %.5f" % mse.mean())
    print("min is %.5f" % mse.min())
    print("max is %.5f" % mse.max())
    print("std is %.5f" % mse.std())
    tr = mse.mean() + mse.std()

the training method of pytorch:

def train(net, x_train, x_opt, BATCH_SIZE, EPOCHS, input_dim):
    outputs = 0
    mse = 0
    optimizer = optim.SGD(net.parameters(), lr=0.001)
    loss_function = nn.MSELoss()
    loss = 0
    for epoch in range(EPOCHS):
        for i in tqdm(range(0, len(x_train), BATCH_SIZE)):
            batch_y = x_opt[i:i + BATCH_SIZE]
            
            net.zero_grad()
            
            outputs = net(batch_y)
            

            loss = loss_function(outputs, batch_y)
            loss.backward()
            optimizer.step()

        print(f"Epoch: {epoch}. Loss: {loss}")
        print("opt", x_opt.size(), "output", outputs.__sizeof__())

    # VVVVVVVVVVVVVVVVVVVVVVVVVVVVVV
    return np.mean(np.power(x_opt - outputs, 2), axis=1)
    # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

2. as seen above the line "outputs" is not a numpy array of predictions, and acquiring this equivalent to generate the threshold

3. If there are any other (improved or missing) ways to acquire this value, appreciation in advance.

Answer 1

The variable output is a pytorch tensor to convert it to numpy all you have to change is change this line of code return np.mean(np.power(x_opt - outputs, 2), axis=1) to this return np.mean(np.power(x_opt - outputs.cpu().data.numpy(), 2), axis=1) That will convert the tensor to a numpy array. If you are not using cuda with your network you do not need the.cpu() part.