25 lines
855 B
Python
25 lines
855 B
Python
from traindata import *
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from model import *
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# Function to test the model
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def test():
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# Load the model that we saved at the end of the training loop
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model001 = EEGNet()
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data_dir = "C:/DATA/M1/Stages/Fablab/dataclean"
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# path = "NetModel.pth"
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model001.load_state_dict(torch.load(data_dir))
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running_accuracy = 0
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total = 0
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with torch.no_grad():
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for data in test_loader:
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inputs, outputs = data
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outputs = outputs.to(torch.float32)
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predicted_outputs = model(inputs)
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_, predicted = torch.max(predicted_outputs, 1)
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total += outputs.size(0)
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running_accuracy += (predicted == outputs).sum().item()
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print('Accuracy of the model based on the test set of', test_split ,'inputs is: %d %%' % (100 * running_accuracy / total)) |