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| Original file line number | Diff line number | Diff line change |
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| import hyper, gaussian | ||
| import torch, random, sys | ||
| from torch.autograd import Variable | ||
| from torch.nn import Parameter | ||
| from torch.nn.functional import sigmoid | ||
| from torch import nn, optim | ||
| from tqdm import trange | ||
| from tensorboardX import SummaryWriter | ||
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| import matplotlib.pyplot as plt | ||
| import util, logging, time, gc | ||
| import numpy as np | ||
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| from argparse import ArgumentParser | ||
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| logging.basicConfig(filename='run.log',level=logging.INFO) | ||
| LOG = logging.getLogger() | ||
|
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| """ | ||
| Simple experiment: learn the identity function from one tensor to another | ||
| """ | ||
| w = SummaryWriter() | ||
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| def go(iterations=30000, additional=64, batch=4, size=32, cuda=False, plot_every=50, | ||
| lr=0.01, fv=False, sigma_scale=0.1, min_sigma=0.0, seed=0): | ||
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| SHAPE = (size,) | ||
| MARGIN = 0.1 | ||
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| torch.manual_seed(seed) | ||
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| nzs = hyper.prod(SHAPE) | ||
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| util.makedirs('./identity/') | ||
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| params = None | ||
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| gaussian.PROPER_SAMPLING = False | ||
| model = gaussian.ParamASHLayer(SHAPE, SHAPE, k=size, additional=additional, sigma_scale=sigma_scale, has_bias=False, fix_values=fv, min_sigma=min_sigma) | ||
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| if cuda: | ||
| model.cuda() | ||
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| criterion = nn.MSELoss() | ||
| optimizer = optim.Adam(model.parameters(), lr=lr) | ||
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| for i in trange(iterations): | ||
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| x = torch.zeros((batch,) + SHAPE) + (1.0/16.0) | ||
| x = torch.bernoulli(x) | ||
| if cuda: | ||
| x = x.cuda() | ||
| x = Variable(x) | ||
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| optimizer.zero_grad() | ||
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| y = model(x) | ||
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| loss = criterion(y, x) | ||
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| t0 = time.time() | ||
| loss.backward() # compute the gradients | ||
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| optimizer.step() | ||
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| w.add_scalar('identity32/loss', loss.data[0], i*batch) | ||
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| if plot_every > 0 and i % plot_every == 0: | ||
| plt.figure(figsize=(7, 7)) | ||
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| print(loss) | ||
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| means, sigmas, values = model.hyper(x) | ||
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| plt.cla() | ||
| util.plot(means, sigmas, values, shape=(SHAPE[0], SHAPE[0])) | ||
| plt.xlim((-MARGIN*(SHAPE[0]-1), (SHAPE[0]-1) * (1.0+MARGIN))) | ||
| plt.ylim((-MARGIN*(SHAPE[0]-1), (SHAPE[0]-1) * (1.0+MARGIN))) | ||
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| plt.savefig('./identity/means{:04}.png'.format(i)) | ||
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| return float(loss.data[0]) | ||
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| if __name__ == "__main__": | ||
|
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| ## Parse the command line options | ||
| parser = ArgumentParser() | ||
|
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| parser.add_argument("-s", "--size", | ||
| dest="size", | ||
| help="Size (nr of dimensions) of the input.", | ||
| default=32, type=int) | ||
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| parser.add_argument("-b", "--batch-size", | ||
| dest="batch_size", | ||
| help="The batch size.", | ||
| default=64, type=int) | ||
|
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| parser.add_argument("-i", "--iterations", | ||
| dest="iterations", | ||
| help="The number of iterations (ie. the nr of batches).", | ||
| default=3000, type=int) | ||
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| parser.add_argument("-a", "--additional", | ||
| dest="additional", | ||
| help="Number of additional points sampled", | ||
| default=512, type=int) | ||
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| parser.add_argument("-c", "--cuda", dest="cuda", | ||
| help="Whether to use cuda.", | ||
| action="store_true") | ||
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| parser.add_argument("-F", "--fix_values", dest="fix_values", | ||
| help="Whether to fix the values to 1.", | ||
| action="store_true") | ||
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| parser.add_argument("-l", "--learn-rate", | ||
| dest="lr", | ||
| help="Learning rate", | ||
| default=0.005, type=float) | ||
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| parser.add_argument("-S", "--sigma-scale", | ||
| dest="sigma_scale", | ||
| help="Sigma scale", | ||
| default=0.1, type=float) | ||
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| parser.add_argument("-M", "--min_sigma", | ||
| dest="min_sigma", | ||
| help="Minimum variance for the components.", | ||
| default=0.0, type=float) | ||
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| parser.add_argument("-p", "--plot-every", | ||
| dest="plot_every", | ||
| help="Plot every x iterations", | ||
| default=50, type=int) | ||
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| parser.add_argument("-r", "--random-seed", | ||
| dest="seed", | ||
| help="Random seed.", | ||
| default=32, type=int) | ||
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| options = parser.parse_args() | ||
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| print('OPTIONS ', options) | ||
| LOG.info('OPTIONS ' + str(options)) | ||
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| go(batch=options.batch_size, size=options.size, | ||
| additional=options.additional, iterations=options.iterations, cuda=options.cuda, | ||
| lr=options.lr, plot_every=options.plot_every, fv=options.fix_values, | ||
| sigma_scale=options.sigma_scale, min_sigma=options.min_sigma, seed=options.seed) |
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