From 7c59905eb04169766437735f11ef6b4611d44c42 Mon Sep 17 00:00:00 2001 From: brian herman Date: Sat, 17 Jun 2017 20:20:46 -0500 Subject: [PATCH 1/8] run 2to3 on all python files. --- gan_64x64.py | 32 ++++++++++++++++---------------- gan_cifar.py | 8 ++++---- gan_language.py | 26 +++++++++++++------------- gan_mnist.py | 6 +++--- gan_toy.py | 24 ++++++++++++------------ language_helpers.py | 12 ++++++------ tflib/__init__.py | 16 ++++++++-------- tflib/cifar10.py | 6 +++--- tflib/inception_score.py | 6 +++--- tflib/mnist.py | 14 +++++++------- tflib/ops/batchnorm.py | 2 +- tflib/ops/conv1d.py | 4 ++-- tflib/ops/conv2d.py | 4 ++-- tflib/ops/layernorm.py | 4 ++-- tflib/plot.py | 10 +++++----- tflib/small_imagenet.py | 4 ++-- 16 files changed, 89 insertions(+), 89 deletions(-) diff --git a/gan_64x64.py b/gan_64x64.py index 6b989c95..6f4232a8 100644 --- a/gan_64x64.py +++ b/gan_64x64.py @@ -65,7 +65,7 @@ def GeneratorAndDiscriminator(): raise Exception('You must choose an architecture!') -DEVICES = ['/gpu:{}'.format(i) for i in xrange(N_GPUS)] +DEVICES = ['/gpu:{}'.format(i) for i in range(N_GPUS)] def LeakyReLU(x, alpha=0.2): return tf.maximum(alpha*x, x) @@ -220,19 +220,19 @@ def ResnetGenerator(n_samples, noise=None, dim=DIM): output = lib.ops.linear.Linear('Generator.Input', 128, 4*4*8*dim, noise) output = tf.reshape(output, [-1, 8*dim, 4, 4]) - for i in xrange(6): + for i in range(6): output = ResidualBlock('Generator.4x4_{}'.format(i), 8*dim, 8*dim, 3, output, resample=None) output = ResidualBlock('Generator.Up1', 8*dim, 4*dim, 3, output, resample='up') - for i in xrange(6): + for i in range(6): output = ResidualBlock('Generator.8x8_{}'.format(i), 4*dim, 4*dim, 3, output, resample=None) output = ResidualBlock('Generator.Up2', 4*dim, 2*dim, 3, output, resample='up') - for i in xrange(6): + for i in range(6): output = ResidualBlock('Generator.16x16_{}'.format(i), 2*dim, 2*dim, 3, output, resample=None) output = ResidualBlock('Generator.Up3', 2*dim, 1*dim, 3, output, resample='up') - for i in xrange(6): + for i in range(6): output = ResidualBlock('Generator.32x32_{}'.format(i), 1*dim, 1*dim, 3, output, resample=None) output = ResidualBlock('Generator.Up4', 1*dim, dim/2, 3, output, resample='up') - for i in xrange(5): + for i in range(5): output = ResidualBlock('Generator.64x64_{}'.format(i), dim/2, dim/2, 3, output, resample=None) output = lib.ops.conv2d.Conv2D('Generator.Out', dim/2, 3, 1, output, he_init=False) @@ -304,19 +304,19 @@ def ResnetDiscriminator(inputs, dim=DIM): output = tf.reshape(inputs, [-1, 3, 64, 64]) output = lib.ops.conv2d.Conv2D('Discriminator.In', 3, dim/2, 1, output, he_init=False) - for i in xrange(5): + for i in range(5): output = ResidualBlock('Discriminator.64x64_{}'.format(i), dim/2, dim/2, 3, output, resample=None) output = ResidualBlock('Discriminator.Down1', dim/2, dim*1, 3, output, resample='down') - for i in xrange(6): + for i in range(6): output = ResidualBlock('Discriminator.32x32_{}'.format(i), dim*1, dim*1, 3, output, resample=None) output = ResidualBlock('Discriminator.Down2', dim*1, dim*2, 3, output, resample='down') - for i in xrange(6): + for i in range(6): output = ResidualBlock('Discriminator.16x16_{}'.format(i), dim*2, dim*2, 3, output, resample=None) output = ResidualBlock('Discriminator.Down3', dim*2, dim*4, 3, output, resample='down') - for i in xrange(6): + for i in range(6): output = ResidualBlock('Discriminator.8x8_{}'.format(i), dim*4, dim*4, 3, output, resample=None) output = ResidualBlock('Discriminator.Down4', dim*4, dim*8, 3, output, resample='down') - for i in xrange(6): + for i in range(6): output = ResidualBlock('Discriminator.4x4_{}'.format(i), dim*8, dim*8, 3, output, resample=None) output = tf.reshape(output, [-1, 4*4*8*dim]) @@ -327,7 +327,7 @@ def ResnetDiscriminator(inputs, dim=DIM): def FCDiscriminator(inputs, FC_DIM=512, n_layers=3): output = LeakyReLULayer('Discriminator.Input', OUTPUT_DIM, FC_DIM, inputs) - for i in xrange(n_layers): + for i in range(n_layers): output = LeakyReLULayer('Discriminator.{}'.format(i), FC_DIM, FC_DIM, output) output = lib.ops.linear.Linear('Discriminator.Out', FC_DIM, 1, output) @@ -492,7 +492,7 @@ def inf_train_gen(): yield images # Save a batch of ground-truth samples - _x = inf_train_gen().next() + _x = next(inf_train_gen()) _x_r = session.run(real_data, feed_dict={real_data_conv: _x}) _x_r = ((_x_r+1.)*(255.99/2)).astype('int32') lib.save_images.save_images(_x_r.reshape((BATCH_SIZE, 3, 64, 64)), 'samples_groundtruth.png') @@ -501,7 +501,7 @@ def inf_train_gen(): # Train loop session.run(tf.initialize_all_variables()) gen = inf_train_gen() - for iteration in xrange(ITERS): + for iteration in range(ITERS): start_time = time.time() @@ -514,8 +514,8 @@ def inf_train_gen(): disc_iters = 1 else: disc_iters = CRITIC_ITERS - for i in xrange(disc_iters): - _data = gen.next() + for i in range(disc_iters): + _data = next(gen) _disc_cost, _ = session.run([disc_cost, disc_train_op], feed_dict={all_real_data_conv: _data}) if MODE == 'wgan': _ = session.run([clip_disc_weights]) diff --git a/gan_cifar.py b/gan_cifar.py index 5db8d579..b7d9a7e9 100644 --- a/gan_cifar.py +++ b/gan_cifar.py @@ -160,7 +160,7 @@ def generate_image(frame, true_dist): samples_100 = Generator(100) def get_inception_score(): all_samples = [] - for i in xrange(10): + for i in range(10): all_samples.append(session.run(samples_100)) all_samples = np.concatenate(all_samples, axis=0) all_samples = ((all_samples+1.)*(255./2)).astype('int32') @@ -179,7 +179,7 @@ def inf_train_gen(): session.run(tf.initialize_all_variables()) gen = inf_train_gen() - for iteration in xrange(ITERS): + for iteration in range(ITERS): start_time = time.time() # Train generator if iteration > 0: @@ -189,8 +189,8 @@ def inf_train_gen(): disc_iters = 1 else: disc_iters = CRITIC_ITERS - for i in xrange(disc_iters): - _data = gen.next() + for i in range(disc_iters): + _data = next(gen) _disc_cost, _ = session.run([disc_cost, disc_train_op], feed_dict={real_data_int: _data}) if MODE == 'wgan': _ = session.run(clip_disc_weights) diff --git a/gan_language.py b/gan_language.py index 7c1c8ddf..9effaf63 100644 --- a/gan_language.py +++ b/gan_language.py @@ -118,7 +118,7 @@ def Discriminator(inputs): def inf_train_gen(): while True: np.random.shuffle(lines) - for i in xrange(0, len(lines)-BATCH_SIZE+1, BATCH_SIZE): + for i in range(0, len(lines)-BATCH_SIZE+1, BATCH_SIZE): yield np.array( [[charmap[c] for c in l] for l in lines[i:i+BATCH_SIZE]], dtype='int32' @@ -127,11 +127,11 @@ def inf_train_gen(): # During training we monitor JS divergence between the true & generated ngram # distributions for n=1,2,3,4. To get an idea of the optimal values, we # evaluate these statistics on a held-out set first. -true_char_ngram_lms = [language_helpers.NgramLanguageModel(i+1, lines[10*BATCH_SIZE:], tokenize=False) for i in xrange(4)] -validation_char_ngram_lms = [language_helpers.NgramLanguageModel(i+1, lines[:10*BATCH_SIZE], tokenize=False) for i in xrange(4)] -for i in xrange(4): - print "validation set JSD for n={}: {}".format(i+1, true_char_ngram_lms[i].js_with(validation_char_ngram_lms[i])) -true_char_ngram_lms = [language_helpers.NgramLanguageModel(i+1, lines, tokenize=False) for i in xrange(4)] +true_char_ngram_lms = [language_helpers.NgramLanguageModel(i+1, lines[10*BATCH_SIZE:], tokenize=False) for i in range(4)] +validation_char_ngram_lms = [language_helpers.NgramLanguageModel(i+1, lines[:10*BATCH_SIZE], tokenize=False) for i in range(4)] +for i in range(4): + print("validation set JSD for n={}: {}".format(i+1, true_char_ngram_lms[i].js_with(validation_char_ngram_lms[i]))) +true_char_ngram_lms = [language_helpers.NgramLanguageModel(i+1, lines, tokenize=False) for i in range(4)] with tf.Session() as session: @@ -141,16 +141,16 @@ def generate_samples(): samples = session.run(fake_inputs) samples = np.argmax(samples, axis=2) decoded_samples = [] - for i in xrange(len(samples)): + for i in range(len(samples)): decoded = [] - for j in xrange(len(samples[i])): + for j in range(len(samples[i])): decoded.append(inv_charmap[samples[i][j]]) decoded_samples.append(tuple(decoded)) return decoded_samples gen = inf_train_gen() - for iteration in xrange(ITERS): + for iteration in range(ITERS): start_time = time.time() # Train generator @@ -158,8 +158,8 @@ def generate_samples(): _ = session.run(gen_train_op) # Train critic - for i in xrange(CRITIC_ITERS): - _data = gen.next() + for i in range(CRITIC_ITERS): + _data = next(gen) _disc_cost, _ = session.run( [disc_cost, disc_train_op], feed_dict={real_inputs_discrete:_data} @@ -170,10 +170,10 @@ def generate_samples(): if iteration % 100 == 99: samples = [] - for i in xrange(10): + for i in range(10): samples.extend(generate_samples()) - for i in xrange(4): + for i in range(4): lm = language_helpers.NgramLanguageModel(i+1, samples, tokenize=False) lib.plot.plot('js{}'.format(i+1), lm.js_with(true_char_ngram_lms[i])) diff --git a/gan_mnist.py b/gan_mnist.py index f979f5a3..66921338 100644 --- a/gan_mnist.py +++ b/gan_mnist.py @@ -211,7 +211,7 @@ def inf_train_gen(): gen = inf_train_gen() - for iteration in xrange(ITERS): + for iteration in range(ITERS): start_time = time.time() if iteration > 0: @@ -221,8 +221,8 @@ def inf_train_gen(): disc_iters = 1 else: disc_iters = CRITIC_ITERS - for i in xrange(disc_iters): - _data = gen.next() + for i in range(disc_iters): + _data = next(gen) _disc_cost, _ = session.run( [disc_cost, disc_train_op], feed_dict={real_data: _data} diff --git a/gan_toy.py b/gan_toy.py index 119fd7c4..fd5d7430 100644 --- a/gan_toy.py +++ b/gan_toy.py @@ -131,12 +131,12 @@ def Discriminator(inputs): ) clip_disc_weights = tf.group(*clip_ops) -print "Generator params:" +print("Generator params:") for var in lib.params_with_name('Generator'): - print "\t{}\t{}".format(var.name, var.get_shape()) -print "Discriminator params:" + print("\t{}\t{}".format(var.name, var.get_shape())) +print("Discriminator params:") for var in lib.params_with_name('Discriminator'): - print "\t{}\t{}".format(var.name, var.get_shape()) + print("\t{}\t{}".format(var.name, var.get_shape())) frame_index = [0] def generate_image(true_dist): @@ -173,9 +173,9 @@ def inf_train_gen(): if DATASET == '25gaussians': dataset = [] - for i in xrange(100000/25): - for x in xrange(-2, 3): - for y in xrange(-2, 3): + for i in range(100000/25): + for x in range(-2, 3): + for y in range(-2, 3): point = np.random.randn(2)*0.05 point[0] += 2*x point[1] += 2*y @@ -184,7 +184,7 @@ def inf_train_gen(): np.random.shuffle(dataset) dataset /= 2.828 # stdev while True: - for i in xrange(len(dataset)/BATCH_SIZE): + for i in range(len(dataset)/BATCH_SIZE): yield dataset[i*BATCH_SIZE:(i+1)*BATCH_SIZE] elif DATASET == 'swissroll': @@ -214,7 +214,7 @@ def inf_train_gen(): centers = [(scale*x,scale*y) for x,y in centers] while True: dataset = [] - for i in xrange(BATCH_SIZE): + for i in range(BATCH_SIZE): point = np.random.randn(2)*.02 center = random.choice(centers) point[0] += center[0] @@ -228,13 +228,13 @@ def inf_train_gen(): with tf.Session() as session: session.run(tf.initialize_all_variables()) gen = inf_train_gen() - for iteration in xrange(ITERS): + for iteration in range(ITERS): # Train generator if iteration > 0: _ = session.run(gen_train_op) # Train critic - for i in xrange(CRITIC_ITERS): - _data = gen.next() + for i in range(CRITIC_ITERS): + _data = next(gen) _disc_cost, _ = session.run( [disc_cost, disc_train_op], feed_dict={real_data: _data} diff --git a/language_helpers.py b/language_helpers.py index 1cdd27cf..feb41049 100644 --- a/language_helpers.py +++ b/language_helpers.py @@ -24,7 +24,7 @@ def __init__(self, n, samples, tokenize=False): def ngrams(self): n = self._n for sample in self._samples: - for i in xrange(len(sample)-n+1): + for i in range(len(sample)-n+1): yield sample[i:i+n] def unique_ngrams(self): @@ -86,13 +86,13 @@ def js_with(self, p): return 0.5*(kl_p_m + kl_q_m) / np.log(2) def load_dataset(max_length, max_n_examples, tokenize=False, max_vocab_size=2048, data_dir='/home/ishaan/data/1-billion-word-language-modeling-benchmark-r13output'): - print "loading dataset..." + print("loading dataset...") lines = [] finished = False - for i in xrange(99): + for i in range(99): path = data_dir+("/training-monolingual.tokenized.shuffled/news.en-{}-of-00100".format(str(i+1).zfill(5))) with open(path, 'r') as f: for line in f: @@ -136,8 +136,8 @@ def load_dataset(max_length, max_n_examples, tokenize=False, max_vocab_size=2048 filtered_line.append('unk') filtered_lines.append(tuple(filtered_line)) - for i in xrange(100): - print filtered_lines[i] + for i in range(100): + print(filtered_lines[i]) - print "loaded {} lines in dataset".format(len(lines)) + print("loaded {} lines in dataset".format(len(lines))) return filtered_lines, charmap, inv_charmap \ No newline at end of file diff --git a/tflib/__init__.py b/tflib/__init__.py index aae43eb9..7d298ebb 100644 --- a/tflib/__init__.py +++ b/tflib/__init__.py @@ -34,13 +34,13 @@ def param(name, *args, **kwargs): return result def params_with_name(name): - return [p for n,p in _params.items() if name in n] + return [p for n,p in list(_params.items()) if name in n] def delete_all_params(): _params.clear() def alias_params(replace_dict): - for old,new in replace_dict.items(): + for old,new in list(replace_dict.items()): # print "aliasing {} to {}".format(old,new) _param_aliases[old] = new @@ -99,16 +99,16 @@ def delete_param_aliases(): # ) def print_model_settings(locals_): - print "Uppercase local vars:" - all_vars = [(k,v) for (k,v) in locals_.items() if (k.isupper() and k!='T' and k!='SETTINGS' and k!='ALL_SETTINGS')] + print("Uppercase local vars:") + all_vars = [(k,v) for (k,v) in list(locals_.items()) if (k.isupper() and k!='T' and k!='SETTINGS' and k!='ALL_SETTINGS')] all_vars = sorted(all_vars, key=lambda x: x[0]) for var_name, var_value in all_vars: - print "\t{}: {}".format(var_name, var_value) + print("\t{}: {}".format(var_name, var_value)) def print_model_settings_dict(settings): - print "Settings dict:" - all_vars = [(k,v) for (k,v) in settings.items()] + print("Settings dict:") + all_vars = [(k,v) for (k,v) in list(settings.items())] all_vars = sorted(all_vars, key=lambda x: x[0]) for var_name, var_value in all_vars: - print "\t{}: {}".format(var_name, var_value) \ No newline at end of file + print("\t{}: {}".format(var_name, var_value)) \ No newline at end of file diff --git a/tflib/cifar10.py b/tflib/cifar10.py index fb6dc536..99908213 100644 --- a/tflib/cifar10.py +++ b/tflib/cifar10.py @@ -1,9 +1,9 @@ import numpy as np import os -import urllib +import urllib.request, urllib.parse, urllib.error import gzip -import cPickle as pickle +import pickle as pickle def unpickle(file): fo = open(file, 'rb') @@ -21,7 +21,7 @@ def cifar_generator(filenames, batch_size, data_dir): def get_epoch(): np.random.shuffle(images) - for i in xrange(len(images) / batch_size): + for i in range(len(images) / batch_size): yield np.copy(images[i*batch_size:(i+1)*batch_size]) return get_epoch diff --git a/tflib/inception_score.py b/tflib/inception_score.py index 38e805d5..9d943159 100644 --- a/tflib/inception_score.py +++ b/tflib/inception_score.py @@ -1,8 +1,8 @@ # From https://github.com/openai/improved-gan/blob/master/inception_score/model.py # Code derived from tensorflow/tensorflow/models/image/imagenet/classify_image.py -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function + + + import os.path import sys diff --git a/tflib/mnist.py b/tflib/mnist.py index faf20d2b..59fb2a25 100644 --- a/tflib/mnist.py +++ b/tflib/mnist.py @@ -1,9 +1,9 @@ import numpy import os -import urllib +import urllib.request, urllib.parse, urllib.error import gzip -import cPickle as pickle +import pickle as pickle def mnist_generator(data, batch_size, n_labelled, limit=None): images, targets = data @@ -13,7 +13,7 @@ def mnist_generator(data, batch_size, n_labelled, limit=None): numpy.random.set_state(rng_state) numpy.random.shuffle(targets) if limit is not None: - print "WARNING ONLY FIRST {} MNIST DIGITS".format(limit) + print("WARNING ONLY FIRST {} MNIST DIGITS".format(limit)) images = images.astype('float32')[:limit] targets = targets.astype('int32')[:limit] if n_labelled is not None: @@ -36,12 +36,12 @@ def get_epoch(): if n_labelled is not None: labelled_batches = labelled.reshape(-1, batch_size) - for i in xrange(len(image_batches)): + for i in range(len(image_batches)): yield (numpy.copy(image_batches[i]), numpy.copy(target_batches[i]), numpy.copy(labelled)) else: - for i in xrange(len(image_batches)): + for i in range(len(image_batches)): yield (numpy.copy(image_batches[i]), numpy.copy(target_batches[i])) return get_epoch @@ -51,8 +51,8 @@ def load(batch_size, test_batch_size, n_labelled=None): url = 'http://www.iro.umontreal.ca/~lisa/deep/data/mnist/mnist.pkl.gz' if not os.path.isfile(filepath): - print "Couldn't find MNIST dataset in /tmp, downloading..." - urllib.urlretrieve(url, filepath) + print("Couldn't find MNIST dataset in /tmp, downloading...") + urllib.request.urlretrieve(url, filepath) with gzip.open('/tmp/mnist.pkl.gz', 'rb') as f: train_data, dev_data, test_data = pickle.load(f) diff --git a/tflib/ops/batchnorm.py b/tflib/ops/batchnorm.py index c04a1622..3ca9bcea 100644 --- a/tflib/ops/batchnorm.py +++ b/tflib/ops/batchnorm.py @@ -77,7 +77,7 @@ def _force_updates(): mean, var = tf.nn.moments(inputs, axes, keep_dims=True) shape = mean.get_shape().as_list() if 0 not in axes: - print "WARNING ({}): didn't find 0 in axes, but not using separate BN params for each item in batch".format(name) + print("WARNING ({}): didn't find 0 in axes, but not using separate BN params for each item in batch".format(name)) shape[0] = 1 offset = lib.param(name+'.offset', np.zeros(shape, dtype='float32')) scale = lib.param(name+'.scale', np.ones(shape, dtype='float32')) diff --git a/tflib/ops/conv1d.py b/tflib/ops/conv1d.py index d375f974..ee51e0ca 100644 --- a/tflib/ops/conv1d.py +++ b/tflib/ops/conv1d.py @@ -31,8 +31,8 @@ def Conv1D(name, input_dim, output_dim, filter_size, inputs, he_init=True, mask_ mask[center+1:, :, :] = 0. # Mask out future channels - for i in xrange(mask_n_channels): - for j in xrange(mask_n_channels): + for i in range(mask_n_channels): + for j in range(mask_n_channels): if (mask_type=='a' and i >= j) or (mask_type=='b' and i > j): mask[ center, diff --git a/tflib/ops/conv2d.py b/tflib/ops/conv2d.py index 818419c9..36a7d382 100644 --- a/tflib/ops/conv2d.py +++ b/tflib/ops/conv2d.py @@ -41,8 +41,8 @@ def Conv2D(name, input_dim, output_dim, filter_size, inputs, he_init=True, mask_ mask[center, center+1:, :, :] = 0. # Mask out future channels - for i in xrange(mask_n_channels): - for j in xrange(mask_n_channels): + for i in range(mask_n_channels): + for j in range(mask_n_channels): if (mask_type=='a' and i >= j) or (mask_type=='b' and i > j): mask[ center, diff --git a/tflib/ops/layernorm.py b/tflib/ops/layernorm.py index db64bcb9..8a531259 100644 --- a/tflib/ops/layernorm.py +++ b/tflib/ops/layernorm.py @@ -13,8 +13,8 @@ def Layernorm(name, norm_axes, inputs): scale = lib.param(name+'.scale', np.ones(n_neurons, dtype='float32')) # Add broadcasting dims to offset and scale (e.g. BCHW conv data) - offset = tf.reshape(offset, [-1] + [1 for i in xrange(len(norm_axes)-1)]) - scale = tf.reshape(scale, [-1] + [1 for i in xrange(len(norm_axes)-1)]) + offset = tf.reshape(offset, [-1] + [1 for i in range(len(norm_axes)-1)]) + scale = tf.reshape(scale, [-1] + [1 for i in range(len(norm_axes)-1)]) result = tf.nn.batch_normalization(inputs, mean, var, offset, scale, 1e-5) diff --git a/tflib/plot.py b/tflib/plot.py index e2c219c2..393e1363 100644 --- a/tflib/plot.py +++ b/tflib/plot.py @@ -6,7 +6,7 @@ import collections import time -import cPickle as pickle +import pickle as pickle _since_beginning = collections.defaultdict(lambda: {}) _since_last_flush = collections.defaultdict(lambda: {}) @@ -21,11 +21,11 @@ def plot(name, value): def flush(): prints = [] - for name, vals in _since_last_flush.items(): - prints.append("{}\t{}".format(name, np.mean(vals.values()))) + for name, vals in list(_since_last_flush.items()): + prints.append("{}\t{}".format(name, np.mean(list(vals.values())))) _since_beginning[name].update(vals) - x_vals = np.sort(_since_beginning[name].keys()) + x_vals = np.sort(list(_since_beginning[name].keys())) y_vals = [_since_beginning[name][x] for x in x_vals] plt.clf() @@ -34,7 +34,7 @@ def flush(): plt.ylabel(name) plt.savefig(name.replace(' ', '_')+'.jpg') - print "iter {}\t{}".format(_iter[0], "\t".join(prints)) + print("iter {}\t{}".format(_iter[0], "\t".join(prints))) _since_last_flush.clear() with open('log.pkl', 'wb') as f: diff --git a/tflib/small_imagenet.py b/tflib/small_imagenet.py index cf8aaa77..d88b0c15 100644 --- a/tflib/small_imagenet.py +++ b/tflib/small_imagenet.py @@ -6,7 +6,7 @@ def make_generator(path, n_files, batch_size): epoch_count = [1] def get_epoch(): images = np.zeros((batch_size, 3, 64, 64), dtype='int32') - files = range(n_files) + files = list(range(n_files)) random_state = np.random.RandomState(epoch_count[0]) random_state.shuffle(files) epoch_count[0] += 1 @@ -27,7 +27,7 @@ def load(batch_size, data_dir='/home/ishaan/data/imagenet64'): train_gen, valid_gen = load(64) t0 = time.time() for i, batch in enumerate(train_gen(), start=1): - print "{}\t{}".format(str(time.time() - t0), batch[0][0,0,0,0]) + print("{}\t{}".format(str(time.time() - t0), batch[0][0,0,0,0])) if i == 1000: break t0 = time.time() \ No newline at end of file From 4e3eee6e4471ce351416c97a96f11c87d7f954d8 Mon Sep 17 00:00:00 2001 From: brian herman Date: Sat, 17 Jun 2017 20:34:13 -0500 Subject: [PATCH 2/8] Update README.md --- README.md | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) diff --git a/README.md b/README.md index 66449fdf..26f1c6f2 100644 --- a/README.md +++ b/README.md @@ -24,3 +24,26 @@ download URL is in the file. - `python gan_64x64.py`: 64x64 architectures (this code trains on ImageNet instead of LSUN bedrooms in the paper) - `python gan_language.py`: Character-level language model - `python gan_cifar.py`: CIFAR-10 + + +## Installation Windows + +Install anaconda +https://www.continuum.io/downloads#windows + + +## Install tensorflow with GPU Support + Install tensorflow + Requirements to run TensorFlow with GPU support + + If you are installing TensorFlow with GPU support using one of the mechanisms described in this guide, then the following NVIDIA software must be installed on your system: + + CUDA® Toolkit 8.0. For details, see NVIDIA's documentation Ensure that you append the relevant Cuda pathnames to the %PATH% environment variable as described in the NVIDIA documentation. + The NVIDIA drivers associated with CUDA Toolkit 8.0. + cuDNN v5.1. For details, see NVIDIA's documentation. Note that cuDNN is typically installed in a different location from the other CUDA DLLs. Ensure that you add the directory where you installed the cuDNN DLL to your %PATH% environment variable. + GPU card with CUDA Compute Capability 3.0 or higher. See NVIDIA documentation for a list of supported GPU cards. + + conda install Pillow + conda install -c anaconda scipy=0.19.0 + conda install scikit-learn + conda install matplotlib From bb2a1d4cef3806e9a6235526076953de477fdcde Mon Sep 17 00:00:00 2001 From: brian herman Date: Sat, 17 Jun 2017 20:45:35 -0500 Subject: [PATCH 3/8] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 26f1c6f2..de9147b9 100644 --- a/README.md +++ b/README.md @@ -32,7 +32,7 @@ Install anaconda https://www.continuum.io/downloads#windows -## Install tensorflow with GPU Support +# Install tensorflow with GPU Support Install tensorflow Requirements to run TensorFlow with GPU support From a22f438e3753497acbc768334d962190d16401a3 Mon Sep 17 00:00:00 2001 From: brian herman Date: Sat, 17 Jun 2017 20:46:21 -0500 Subject: [PATCH 4/8] Update README.md --- README.md | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index de9147b9..f12c1acb 100644 --- a/README.md +++ b/README.md @@ -31,9 +31,8 @@ download URL is in the file. Install anaconda https://www.continuum.io/downloads#windows - -# Install tensorflow with GPU Support - Install tensorflow +Install tensorflow with GPU Support +Install tensorflow Requirements to run TensorFlow with GPU support If you are installing TensorFlow with GPU support using one of the mechanisms described in this guide, then the following NVIDIA software must be installed on your system: @@ -42,7 +41,7 @@ https://www.continuum.io/downloads#windows The NVIDIA drivers associated with CUDA Toolkit 8.0. cuDNN v5.1. For details, see NVIDIA's documentation. Note that cuDNN is typically installed in a different location from the other CUDA DLLs. Ensure that you add the directory where you installed the cuDNN DLL to your %PATH% environment variable. GPU card with CUDA Compute Capability 3.0 or higher. See NVIDIA documentation for a list of supported GPU cards. - +Install other requirements. conda install Pillow conda install -c anaconda scipy=0.19.0 conda install scikit-learn From 9addc989bef2f8e6ae454164c1e57d674eaccbbf Mon Sep 17 00:00:00 2001 From: brian herman Date: Sat, 17 Jun 2017 20:46:34 -0500 Subject: [PATCH 5/8] Update README.md --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index f12c1acb..90a86fa7 100644 --- a/README.md +++ b/README.md @@ -41,7 +41,8 @@ Install tensorflow The NVIDIA drivers associated with CUDA Toolkit 8.0. cuDNN v5.1. For details, see NVIDIA's documentation. Note that cuDNN is typically installed in a different location from the other CUDA DLLs. Ensure that you add the directory where you installed the cuDNN DLL to your %PATH% environment variable. GPU card with CUDA Compute Capability 3.0 or higher. See NVIDIA documentation for a list of supported GPU cards. -Install other requirements. +Install other requirements. + conda install Pillow conda install -c anaconda scipy=0.19.0 conda install scikit-learn From 790333852a3ba147d59de5081e30fa3e853d124e Mon Sep 17 00:00:00 2001 From: brian herman Date: Sat, 17 Jun 2017 20:47:09 -0500 Subject: [PATCH 6/8] Update README.md --- README.md | 13 +------------ 1 file changed, 1 insertion(+), 12 deletions(-) diff --git a/README.md b/README.md index 90a86fa7..9a992003 100644 --- a/README.md +++ b/README.md @@ -31,18 +31,7 @@ download URL is in the file. Install anaconda https://www.continuum.io/downloads#windows -Install tensorflow with GPU Support -Install tensorflow - Requirements to run TensorFlow with GPU support - - If you are installing TensorFlow with GPU support using one of the mechanisms described in this guide, then the following NVIDIA software must be installed on your system: - - CUDA® Toolkit 8.0. For details, see NVIDIA's documentation Ensure that you append the relevant Cuda pathnames to the %PATH% environment variable as described in the NVIDIA documentation. - The NVIDIA drivers associated with CUDA Toolkit 8.0. - cuDNN v5.1. For details, see NVIDIA's documentation. Note that cuDNN is typically installed in a different location from the other CUDA DLLs. Ensure that you add the directory where you installed the cuDNN DLL to your %PATH% environment variable. - GPU card with CUDA Compute Capability 3.0 or higher. See NVIDIA documentation for a list of supported GPU cards. -Install other requirements. - +https://www.tensorflow.org/install/install_windows conda install Pillow conda install -c anaconda scipy=0.19.0 conda install scikit-learn From 25397928f80c2c2991b7a5c970579fc035098a59 Mon Sep 17 00:00:00 2001 From: brian herman Date: Sat, 17 Jun 2017 20:47:23 -0500 Subject: [PATCH 7/8] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 9a992003..841f6fd3 100644 --- a/README.md +++ b/README.md @@ -32,6 +32,7 @@ Install anaconda https://www.continuum.io/downloads#windows https://www.tensorflow.org/install/install_windows + conda install Pillow conda install -c anaconda scipy=0.19.0 conda install scikit-learn From 61dc018a198a6fd22949702362c170f02d3a71d7 Mon Sep 17 00:00:00 2001 From: brian herman Date: Mon, 19 Jun 2017 20:50:52 -0500 Subject: [PATCH 8/8] adding samples and bugfixes to language --- gan_language.py | 4 +- language_helpers.py | 2 +- samples_24299.txt | 640 ++++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 643 insertions(+), 3 deletions(-) create mode 100644 samples_24299.txt diff --git a/gan_language.py b/gan_language.py index 9effaf63..c810299c 100644 --- a/gan_language.py +++ b/gan_language.py @@ -14,7 +14,7 @@ # Download Google Billion Word at http://www.statmt.org/lm-benchmark/ and # fill in the path to the extracted files here! -DATA_DIR = '' +DATA_DIR = 'data' if len(DATA_DIR) == 0: raise Exception('Please specify path to data directory in gan_language.py!') @@ -177,7 +177,7 @@ def generate_samples(): lm = language_helpers.NgramLanguageModel(i+1, samples, tokenize=False) lib.plot.plot('js{}'.format(i+1), lm.js_with(true_char_ngram_lms[i])) - with open('samples_{}.txt'.format(iteration), 'w') as f: + with open('samples_{}.txt'.format(iteration), 'w', encoding="utf-8") as f: for s in samples: s = "".join(s) f.write(s + "\n") diff --git a/language_helpers.py b/language_helpers.py index feb41049..2918c754 100644 --- a/language_helpers.py +++ b/language_helpers.py @@ -94,7 +94,7 @@ def load_dataset(max_length, max_n_examples, tokenize=False, max_vocab_size=2048 for i in range(99): path = data_dir+("/training-monolingual.tokenized.shuffled/news.en-{}-of-00100".format(str(i+1).zfill(5))) - with open(path, 'r') as f: + with open(path, 'r', encoding='utf-8') as f: for line in f: line = line[:-1] if tokenize: diff --git a/samples_24299.txt b/samples_24299.txt new file mode 100644 index 00000000..d1c9fbe3 --- /dev/null +++ b/samples_24299.txt @@ -0,0 +1,640 @@ +In De on the rusuct reacy stith +"omh , other on a great his ufte +But a fice the Fresnago , Hurtie +Mr mide tot , gime , wimy proten +He 's bose of that Uul bleved th +Dow dlew mar then shoula but app +Ner of be than relarrter is to e +Silly j im not hospinsing a late +U. 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