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https://github.com/labmlai/annotated_deep_learning_paper_implementations.git
synced 2025-11-04 06:16:05 +08:00
86 lines
2.2 KiB
Python
86 lines
2.2 KiB
Python
#!/bin/python
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import torch
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import torchvision
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import torchvision.transforms as transforms
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import torch.nn as nn
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import torch.nn.functional as F
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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn.model_selection import KFold
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from torch.utils.data.sampler import SubsetRandomSampler
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# Plot the loss of multiple runs together
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def PlotLosses(losses, titles, save=None):
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fig = plt.figure()
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fig.set_size_inches(14, 22)
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# Plot results on 3 subgraphs
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# subplot integers:
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# nrows
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# ncols
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# index
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sublplot_str_start = "" + str(len(losses)) + "1"
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for i in range(len(losses)):
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subplot = sublplot_str_start + str(i+1)
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loss = losses[i]
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title = titles[i]
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ax = plt.subplot(int(subplot))
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ax.plot(range(len(loss)), loss)
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ax.set_xlabel("Epoch")
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ax.set_title(title)
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ax.set_ylabel("Loss")
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# Save Figure
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if save:
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plt.savefig(save)
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else:
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plt.show()
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def ClassSpecificTestCifar10(net, testdata, device=None):
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classes = ('plane', 'car', 'bird', 'cat','deer', 'dog', 'frog', 'horse', 'ship', 'truck')
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class_correct = list(0. for i in range(10))
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class_total = list(0. for i in range(10))
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with torch.no_grad():
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for data in testdata:
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if device:
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images, labels = data[0].to(device), data[1].to(device)
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else:
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images, labels = data
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outputs = net(images)
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_, predicted = torch.max(outputs, 1)
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c = (predicted == labels).squeeze()
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for i in range(4):
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label = labels[i]
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class_correct[label] += c[i].item()
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class_total[label] += 1
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# Print out
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for i in range(10):
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print('Accuracy of %5s : %2d %%' % (
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classes[i], 100 * class_correct[i] / class_total[i]))
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def GetActivation(name="relu"):
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if name == "relu":
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return nn.ReLU()
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elif name == "leakyrelu":
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return nn.LeakyReLU()
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elif name == "Sigmoid":
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return nn.Sigmoid()
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elif name == "Tanh":
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return nn.Tanh()
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elif name == "Identity":
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return nn.Identity()
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else:
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return nn.ReLU() |