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47 lines
1.2 KiB
Python
Executable File
47 lines
1.2 KiB
Python
Executable File
#!/usr/bin/env python3
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# test model output by torch_mnist_dense.mpc
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import torchvision
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import torch
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import torch.nn as nn
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import numpy
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net = nn.Sequential(
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nn.Flatten(),
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nn.Linear(28 * 28, 128),
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nn.ReLU(),
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nn.Linear(128, 128),
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nn.ReLU(),
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nn.Linear(128, 10)
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)
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f = open('Player-Data/Binary-Output-P0-0')
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state = net.state_dict()
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for name in state:
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shape = state[name].shape
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size = numpy.prod(shape)
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var = numpy.fromfile(f, 'double', count=size)
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var = var.reshape(shape)
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state[name] = torch.Tensor(var)
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net.load_state_dict(state)
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transform = torchvision.transforms.Compose(
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[torchvision.transforms.ToTensor()])
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with torch.no_grad():
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ds = torchvision.datasets.MNIST(root='/tmp', transform=transform,
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train=False)
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total = correct_classified = 0
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for data in torch.utils.data.DataLoader(ds, batch_size=128):
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inputs, labels = data
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outputs = net(inputs)
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_, predicted = torch.max(outputs.data, 1)
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total += labels.size(0)
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correct_classified += (predicted == labels).sum().item()
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test_acc = (100 * correct_classified / total)
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print('Test accuracy of the network: %.2f %%' % test_acc)
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