From: Francois Fleuret Date: Fri, 4 Jan 2019 14:33:06 +0000 (+0100) Subject: Update. X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=commitdiff_plain;h=75267f198e8f6cf476cb73d2846653494d7164b6;p=pytorch.git Update. --- diff --git a/confidence.py b/confidence.py index ff4b395..1be3420 100755 --- a/confidence.py +++ b/confidence.py @@ -23,14 +23,17 @@ y = y.view(-1, 1) ###################################################################### -nh = 100 +nh = 400 model = nn.Sequential(nn.Linear(1, nh), nn.ReLU(), + nn.Dropout(0.25), nn.Linear(nh, nh), nn.ReLU(), + nn.Dropout(0.25), nn.Linear(nh, 1)) +model.train(True) criterion = nn.MSELoss() -optimizer = torch.optim.Adam(model.parameters(), lr = 1e-3) +optimizer = torch.optim.Adam(model.parameters(), lr = 1e-4) for k in range(10000): loss = criterion(model(x), y) @@ -44,10 +47,17 @@ for k in range(10000): import matplotlib.pyplot as plt fig, ax = plt.subplots() + +u = torch.linspace(0, 1, 101) +v = u.view(-1, 1).expand(-1, 25).reshape(-1, 1) +v = model(v).reshape(101, -1) +mean = v.mean(1) +std = v.std(1) + +ax.fill_between(u.numpy(), (mean-std).detach().numpy(), (mean+std).detach().numpy(), color = '#e0e0e0') +ax.plot(u.numpy(), mean.detach().numpy(), color = 'red') ax.scatter(x.numpy(), y.numpy()) -u = torch.linspace(0, 1, 100).view(-1, 1) -ax.plot(u.numpy(), model(u).detach().numpy(), color = 'red') plt.show() ###################################################################### diff --git a/mine_mnist.py b/mine_mnist.py index 389544b..1d69640 100755 --- a/mine_mnist.py +++ b/mine_mnist.py @@ -1,5 +1,22 @@ #!/usr/bin/env python +######################################################################### +# This program is free software: you can redistribute it and/or modify # +# it under the terms of the version 3 of the GNU General Public License # +# as published by the Free Software Foundation. # +# # +# This program is distributed in the hope that it will be useful, but # +# WITHOUT ANY WARRANTY; without even the implied warranty of # +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # +# General Public License for more details. # +# # +# You should have received a copy of the GNU General Public License # +# along with this program. If not, see . # +# # +# Written by and Copyright (C) Francois Fleuret # +# Contact for comments & bug reports # +######################################################################### + import argparse, math, sys from copy import deepcopy @@ -19,13 +36,28 @@ else: ###################################################################### parser = argparse.ArgumentParser( - description = 'An implementation of Mutual Information estimator with a deep model', + description = '''An implementation of a Mutual Information estimator with a deep model + +Three different toy data-sets are implemented: + + (1) Two MNIST images of same class. The "true" MI is the log of the + number of used MNIST classes. + + (2) One MNIST image and a pair of real numbers whose difference is + the class of the image. The "true" MI is the log of the number of + used MNIST classes. + + (3) Two 1d sequences, the first with a single peak, the second with + two peaks, and the height of the peak in the first is the + difference of timing of the peaks in the second. The "true" MI is + the log of the number of possible peak heights.''', + formatter_class = argparse.ArgumentDefaultsHelpFormatter ) parser.add_argument('--data', type = str, default = 'image_pair', - help = 'What data') + help = 'What data: image_pair, image_values_pair, sequence_pair') parser.add_argument('--seed', type = int, default = 0, @@ -47,9 +79,23 @@ parser.add_argument('--batch_size', type = int, default = 100, help = 'Batch size') +parser.add_argument('--learning_rate', + type = float, default = 1e-3, + help = 'Batch size') + parser.add_argument('--independent', action = 'store_true', help = 'Should the pair components be independent') + +###################################################################### + +args = parser.parse_args() + +if args.seed >= 0: + torch.manual_seed(args.seed) + +used_MNIST_classes = torch.tensor(eval('[' + args.mnist_classes + ']'), device = device) + ###################################################################### def entropy(target): @@ -61,21 +107,6 @@ def entropy(target): probas /= probas.sum() return - (probas * probas.log()).sum().item() -def robust_log_mean_exp(x): - # a = x.max() - # return (x-a).exp().mean().log() + a - # a = x.max() - return x.exp().mean().log() - -###################################################################### - -args = parser.parse_args() - -if args.seed >= 0: - torch.manual_seed(args.seed) - -used_MNIST_classes = torch.tensor(eval('[' + args.mnist_classes + ']'), device = device) - ###################################################################### train_set = torchvision.datasets.MNIST('./data/mnist/', train = True, download = True) @@ -129,7 +160,7 @@ def create_image_pairs(train = False): ###################################################################### # Returns a triplet a, b, c where a are the standard MNIST images, c -# the classes, and b is a Nx2 tensor, eith for every n: +# the classes, and b is a Nx2 tensor, with for every n: # # b[n, 0] ~ Uniform(0, 10) # b[n, 1] ~ b[n, 0] + Uniform(0, 0.5) + c[n] @@ -158,7 +189,8 @@ def create_image_values_pairs(train = False): b[:, 1].uniform_(0.0, 0.5) if args.independent: - b[:, 1] += b[:, 0] + used_MNIST_classes[torch.randint(len(used_MNIST_classes), target.size())] + b[:, 1] += b[:, 0] + \ + used_MNIST_classes[torch.randint(len(used_MNIST_classes), target.size())] else: b[:, 1] += b[:, 0] + target.float() @@ -189,6 +221,7 @@ def create_sequences_pairs(train = False): b1 = b1 - pos.view(nb, 1) b1 = (b1 >= 0).float() * torch.exp(-b1 * math.log(2) / 0.1) * 0.25 pos = pos + hb.float() / (args.nb_classes + 1) * 0.5 + # pos += pos.new(hb.size()).uniform_(0.0, 0.01) b2 = torch.linspace(0, 1, length, device = device).view(1, -1).expand(nb, -1) b2 = b2 - pos.view(nb, 1) b2 = (b2 >= 0).float() * torch.exp(-b2 * math.log(2) / 0.1) * 0.25 @@ -317,40 +350,43 @@ class NetForSequencePair(nn.Module): if args.data == 'image_pair': create_pairs = create_image_pairs model = NetForImagePair() + elif args.data == 'image_values_pair': create_pairs = create_image_values_pairs model = NetForImageValuesPair() + elif args.data == 'sequence_pair': create_pairs = create_sequences_pairs model = NetForSequencePair() - ###################################################################### + + ## Save for figures a, b, c = create_pairs() for k in range(10): file = open(f'train_{k:02d}.dat', 'w') for i in range(a.size(1)): file.write(f'{a[k, i]:f} {b[k,i]:f}\n') file.close() - # exit(0) - ###################################################################### + else: raise Exception('Unknown data ' + args.data) ###################################################################### +# Train -print('nb_parameters %d' % sum(x.numel() for x in model.parameters())) +print(f'nb_parameters {sum(x.numel() for x in model.parameters())}') model.to(device) +input_a, input_b, classes = create_pairs(train = True) + for e in range(args.nb_epochs): - input_a, input_b, classes = create_pairs(train = True) + optimizer = torch.optim.Adam(model.parameters(), lr = args.learning_rate) input_br = input_b[torch.randperm(input_b.size(0))] acc_mi = 0.0 - optimizer = torch.optim.Adam(model.parameters(), lr = 1e-4) - for batch_a, batch_b, batch_br in zip(input_a.split(args.batch_size), input_b.split(args.batch_size), input_br.split(args.batch_size)): @@ -363,11 +399,12 @@ for e in range(args.nb_epochs): acc_mi /= (input_a.size(0) // args.batch_size) - print('%d %.04f %.04f' % (e + 1, acc_mi / math.log(2), entropy(classes) / math.log(2))) + print(f'{e+1} {acc_mi / math.log(2):.04f} {entropy(classes) / math.log(2):.04f}') sys.stdout.flush() ###################################################################### +# Test input_a, input_b, classes = create_pairs(train = False) @@ -383,6 +420,6 @@ for batch_a, batch_b, batch_br in zip(input_a.split(args.batch_size), acc_mi /= (input_a.size(0) // args.batch_size) -print('test %.04f %.04f'%(acc_mi / math.log(2), entropy(classes) / math.log(2))) +print(f'test {acc_mi / math.log(2):.04f} {entropy(classes) / math.log(2):.04f}') ######################################################################