45 lines
1.2 KiB
Python
45 lines
1.2 KiB
Python
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import os
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import torch
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import numpy as np
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from torch.nn import functional as F
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import config_bayesian as cfg
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# cifar10 classes
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cifar10_classes = ['airplane', 'automobile', 'bird', 'cat', 'deer',
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'dog', 'frog', 'horse', 'ship', 'truck']
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def logmeanexp(x, dim=None, keepdim=False):
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"""Stable computation of log(mean(exp(x))"""
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if dim is None:
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x, dim = x.view(-1), 0
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x_max, _ = torch.max(x, dim, keepdim=True)
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x = x_max + torch.log(torch.mean(torch.exp(x - x_max), dim, keepdim=True))
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return x if keepdim else x.squeeze(dim)
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# check if dimension is correct
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# def dimension_check(x, dim=None, keepdim=False):
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# if dim is None:
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# x, dim = x.view(-1), 0
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# return x if keepdim else x.squeeze(dim)
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def adjust_learning_rate(optimizer, lr):
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"""Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
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for param_group in optimizer.param_groups:
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param_group['lr'] = lr
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def save_array_to_file(numpy_array, filename):
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file = open(filename, 'a')
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shape = " ".join(map(str, numpy_array.shape))
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np.savetxt(file, numpy_array.flatten(), newline=" ", fmt="%.3f")
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file.write("\n")
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file.close()
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