From 1ef32d0b85a179a36a1cd7899b8301bcb8e563d2 Mon Sep 17 00:00:00 2001 From: Francois Fleuret Date: Thu, 22 Nov 2018 22:33:43 +0100 Subject: [PATCH] Update. --- mine_mnist.py | 78 +++++++++++++++++++++++-------------------- mine_mnist.py.xremote | 4 +++ 2 files changed, 45 insertions(+), 37 deletions(-) create mode 100755 mine_mnist.py.xremote diff --git a/mine_mnist.py b/mine_mnist.py index 6f65136..5ab427f 100755 --- a/mine_mnist.py +++ b/mine_mnist.py @@ -1,10 +1,5 @@ #!/usr/bin/env python -# @XREMOTE_HOST: elk.fleuret.org -# @XREMOTE_EXEC: ~/conda/bin/python -# @XREMOTE_PRE: ln -s ~/data/pytorch ./data -# @XREMOTE_PRE: killall -q -9 python || true - import math, sys, torch, torchvision from torch import nn @@ -12,21 +7,44 @@ from torch.nn import functional as F ###################################################################### -# Returns a pair of tensors (a, b, c), where a and b are tensors -# containing each half of the samples, with a[i] and b[i] of same -# class for any i, and c is a 1d long tensor with the count of pairs -# per class used. +train_set = torchvision.datasets.MNIST('./data/mnist/', train = True, download = True) +train_input = train_set.train_data.view(-1, 1, 28, 28).float() +train_target = train_set.train_labels + +test_set = torchvision.datasets.MNIST('./data/mnist/', train = False, download = True) +test_input = test_set.test_data.view(-1, 1, 28, 28).float() +test_target = test_set.test_labels -def create_pair_set(used_classes, input, target): +mu, std = train_input.mean(), train_input.std() +train_input.sub_(mu).div_(std) +test_input.sub_(mu).div_(std) + +used_MNIST_classes = torch.tensor([ 0, 1, 3, 5, 6, 7, 8, 9]) +# used_MNIST_classes = torch.tensor([ 0, 9, 7 ]) +# used_MNIST_classes = torch.tensor([ 3, 4, 7, 0 ]) + +###################################################################### + +# Returns a triplet of tensors (a, b, c), where a and b contain each +# half of the samples, with a[i] and b[i] of same class for any i, and +# c is a 1d long tensor with the count of pairs per class used. + +def create_MNIST_pair_set(train = False): ua, ub = [], [] - for i in used_classes: + if train: + input, target = train_input, train_target + else: + input, target = test_input, test_target + + for i in used_MNIST_classes: used_indices = torch.arange(input.size(0), device = target.device)\ .masked_select(target == i.item()) x = input[used_indices] x = x[torch.randperm(x.size(0))] - ua.append(x.narrow(0, 0, x.size(0)//2)) - ub.append(x.narrow(0, x.size(0)//2, x.size(0)//2)) + hs = x.size(0)//2 + ua.append(x.narrow(0, 0, hs)) + ub.append(x.narrow(0, hs, hs)) a = torch.cat(ua, 0) b = torch.cat(ub, 0) @@ -48,6 +66,7 @@ class Net(nn.Module): self.fc2 = nn.Linear(200, 1) def forward(self, a, b): + # Make the two images a single two-channel image x = torch.cat((a, b), 1) x = F.relu(F.max_pool2d(self.conv1(x), kernel_size = 3)) x = F.relu(F.max_pool2d(self.conv2(x), kernel_size = 2)) @@ -58,28 +77,12 @@ class Net(nn.Module): ###################################################################### -train_set = torchvision.datasets.MNIST('./data/mnist/', train = True, download = True) -train_input = train_set.train_data.view(-1, 1, 28, 28).float() -train_target = train_set.train_labels - -test_set = torchvision.datasets.MNIST('./data/mnist/', train = False, download = True) -test_input = test_set.test_data.view(-1, 1, 28, 28).float() -test_target = test_set.test_labels - -mu, std = train_input.mean(), train_input.std() -train_input.sub_(mu).div_(std) -test_input.sub_(mu).div_(std) - -###################################################################### - -# The information bound is the log of the number of classes in there - -# used_classes = torch.tensor([ 0, 1, 3, 5, 6, 7, 8, 9]) -used_classes = torch.tensor([ 3, 4, 7, 0 ]) - nb_epochs, batch_size = 50, 100 model = Net() + +print('nb_parameters %d' % sum(x.numel() for x in model.parameters())) + optimizer = torch.optim.Adam(model.parameters(), lr = 1e-3) if torch.cuda.is_available(): @@ -89,8 +92,9 @@ if torch.cuda.is_available(): for e in range(nb_epochs): - input_a, input_b, count = create_pair_set(used_classes, train_input, train_target) + input_a, input_b, count = create_MNIST_pair_set(train = True) + # The information bound is the entropy of the class distribution class_proba = count.float() class_proba /= class_proba.sum() class_entropy = - (class_proba.log() * class_proba).sum().item() @@ -111,13 +115,13 @@ for e in range(nb_epochs): acc_mi /= (input_a.size(0) // batch_size) - print('%d %.04f %.04f'%(e, acc_mi / math.log(2), class_entropy / math.log(2))) + print('%d %.04f %.04f' % (e, acc_mi / math.log(2), class_entropy / math.log(2))) sys.stdout.flush() ###################################################################### -input_a, input_b, count = create_pair_set(used_classes, test_input, test_target) +input_a, input_b, count = create_MNIST_pair_set(train = False) for e in range(nb_epochs): class_proba = count.float() @@ -131,8 +135,8 @@ for e in range(nb_epochs): for batch_a, batch_b, batch_br in zip(input_a.split(batch_size), input_b.split(batch_size), input_br.split(batch_size)): - loss = - (model(batch_a, batch_b).mean() - model(batch_a, batch_br).exp().mean().log()) - acc_mi -= loss.item() + mi = model(batch_a, batch_b).mean() - model(batch_a, batch_br).exp().mean().log() + acc_mi += mi.item() acc_mi /= (input_a.size(0) // batch_size) diff --git a/mine_mnist.py.xremote b/mine_mnist.py.xremote new file mode 100755 index 0000000..3217f50 --- /dev/null +++ b/mine_mnist.py.xremote @@ -0,0 +1,4 @@ +@XREMOTE_HOST: elk.fleuret.org +@XREMOTE_EXEC: ~/conda/bin/python +@XREMOTE_PRE: ln -s ~/data/pytorch ./data +@XREMOTE_PRE: killall -q -9 python || true -- 2.20.1