my_net.train(False) with torch.no_grad(): x_test = torch.DoubleTensor(x_test).cuda() answer = my_net(x_test)
时间: 2024-05-25 09:17:16 浏览: 13
These lines of code are disabling the training mode of a neural network model (my_net) and using it to make predictions on a test dataset (x_test).
The first line, my_net.train(False), sets the model to evaluation mode. This means that the model will not update its weights or biases during forward propagation, which is useful for making predictions on a test dataset without altering the model's parameters.
The second line, with torch.no_grad(), specifies that the following block of code should not calculate gradients. This can significantly speed up the execution time and reduce memory usage when making predictions on a test dataset.
The third line, x_test = torch.DoubleTensor(x_test).cuda(), converts the test dataset to a tensor of double precision floating-point numbers and moves it to the GPU for faster processing (assuming that a GPU is available).
The fourth line, answer = my_net(x_test), applies the trained model (my_net) to the test dataset (x_test) to make predictions. The output of the model is stored in the variable answer.
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