forked from LiDan456/MAD-GANs
-
Notifications
You must be signed in to change notification settings - Fork 0
/
RGAN.py
190 lines (167 loc) · 8.39 KB
/
RGAN.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
import numpy as np
import tensorflow as tf
import pdb
import random
import json
from scipy.stats import mode
import data_utils
import plotting
import model
import utils
import eval
import DR_discriminator
from time import time
from math import floor
from mmd import rbf_mmd2, median_pairwise_distance, mix_rbf_mmd2_and_ratio
begin = time()
tf.logging.set_verbosity(tf.logging.ERROR)
# --- get settings --- #
# parse command line arguments, or use defaults
parser = utils.rgan_options_parser()
settings = vars(parser.parse_args())
# if a settings file is specified, it overrides command line arguments/defaults
if settings['settings_file']: settings = utils.load_settings_from_file(settings)
# --- get data, split --- #
# samples, pdf, labels = data_utils.get_data(settings)
data_path = './experiments/data/' + settings['data_load_from'] + '.data.npy'
print('Loading data from', data_path)
settings["eval_an"] = False
settings["eval_single"] = False
samples, labels, index = data_utils.get_data(settings["data"], settings["seq_length"], settings["seq_step"],
settings["num_signals"], settings['sub_id'], settings["eval_single"],
settings["eval_an"], data_path)
print('samples_size:',samples.shape)
# -- number of variables -- #
num_variables = samples.shape[2]
print('num_variables:', num_variables)
# --- save settings, data --- #
print('Ready to run with settings:')
for (k, v) in settings.items(): print(v, '\t', k)
# add the settings to local environment
# WARNING: at this point a lot of variables appear
locals().update(settings)
json.dump(settings, open('./experiments/settings/' + identifier + '.txt', 'w'), indent=0)
# --- build model --- #
# preparation: data placeholders and model parameters
Z, X, T = model.create_placeholders(batch_size, seq_length, latent_dim, num_variables)
discriminator_vars = ['hidden_units_d', 'seq_length', 'batch_size', 'batch_mean']
discriminator_settings = dict((k, settings[k]) for k in discriminator_vars)
generator_vars = ['hidden_units_g', 'seq_length', 'batch_size', 'learn_scale']
generator_settings = dict((k, settings[k]) for k in generator_vars)
generator_settings['num_signals'] = num_variables
# model: GAN losses
D_loss, G_loss = model.GAN_loss(Z, X, generator_settings, discriminator_settings)
D_solver, G_solver, priv_accountant = model.GAN_solvers(D_loss, G_loss, learning_rate, batch_size,
total_examples=samples.shape[0],
l2norm_bound=l2norm_bound,
batches_per_lot=batches_per_lot, sigma=dp_sigma, dp=dp)
# model: generate samples for visualization
G_sample = model.generator(Z, **generator_settings, reuse=True)
# # --- evaluation settings--- #
#
# # frequency to do visualisations
# num_samples = samples.shape[0]
# vis_freq = max(6600 // num_samples, 1)
# eval_freq = max(6600// num_samples, 1)
#
# # get heuristic bandwidth for mmd kernel from evaluation samples
# heuristic_sigma_training = median_pairwise_distance(samples)
# best_mmd2_so_far = 1000
#
# # optimise sigma using that (that's t-hat)
# batch_multiplier = 5000 // batch_size
# eval_size = batch_multiplier * batch_size
# eval_eval_size = int(0.2 * eval_size)
# eval_real_PH = tf.placeholder(tf.float32, [eval_eval_size, seq_length, num_generated_features])
# eval_sample_PH = tf.placeholder(tf.float32, [eval_eval_size, seq_length, num_generated_features])
# n_sigmas = 2
# sigma = tf.get_variable(name='sigma', shape=n_sigmas, initializer=tf.constant_initializer(
# value=np.power(heuristic_sigma_training, np.linspace(-1, 3, num=n_sigmas))))
# mmd2, that = mix_rbf_mmd2_and_ratio(eval_real_PH, eval_sample_PH, sigma)
# with tf.variable_scope("SIGMA_optimizer"):
# sigma_solver = tf.train.RMSPropOptimizer(learning_rate=0.05).minimize(-that, var_list=[sigma])
# # sigma_solver = tf.train.AdamOptimizer().minimize(-that, var_list=[sigma])
# # sigma_solver = tf.train.AdagradOptimizer(learning_rate=0.1).minimize(-that, var_list=[sigma])
# sigma_opt_iter = 2000
# sigma_opt_thresh = 0.001
# sigma_opt_vars = [var for var in tf.global_variables() if 'SIGMA_optimizer' in var.name]
# --- run the program --- #
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
# sess = tf.Session()
sess.run(tf.global_variables_initializer())
# # -- plot the real samples -- #
vis_real_indices = np.random.choice(len(samples), size=16)
vis_real = np.float32(samples[vis_real_indices, :, :])
plotting.save_plot_sample(vis_real, 0, identifier + '_real', n_samples=16, num_epochs=num_epochs)
plotting.save_samples_real(vis_real, identifier)
# --- train --- #
train_vars = ['batch_size', 'D_rounds', 'G_rounds', 'use_time', 'seq_length', 'latent_dim']
train_settings = dict((k, settings[k]) for k in train_vars)
train_settings['num_signals'] = num_variables
t0 = time()
MMD = np.zeros([num_epochs, ])
for epoch in range(num_epochs):
# for epoch in range(1):
# -- train epoch -- #
D_loss_curr, G_loss_curr = model.train_epoch(epoch, samples, labels, sess, Z, X, D_loss, G_loss,
D_solver, G_solver, **train_settings)
# # -- eval -- #
# # visualise plots of generated samples, with/without labels
# # choose which epoch to visualize
#
# # random input vectors for the latent space, as the inputs of generator
# vis_ZZ = model.sample_Z(batch_size, seq_length, latent_dim, use_time)
#
# # # -- generate samples-- #
# vis_sample = sess.run(G_sample, feed_dict={Z: vis_ZZ})
# # # -- visualize the generated samples -- #
# plotting.save_plot_sample(vis_sample, epoch, identifier, n_samples=16, num_epochs=None, ncol=4)
# # plotting.save_plot_sample(vis_sample, 0, identifier + '_real', n_samples=16, num_epochs=num_epochs)
# # # save the generated samples in cased they might be useful for comparison
# plotting.save_samples(vis_sample, identifier, epoch)
# -- print -- #
print('epoch, D_loss_curr, G_loss_curr, seq_length')
print('%d\t%.4f\t%.4f\t%d' % (epoch, D_loss_curr, G_loss_curr, seq_length))
# # -- compute mmd2 and if available, prob density -- #
# if epoch % eval_freq == 0:
# # how many samples to evaluate with?
# eval_Z = model.sample_Z(eval_size, seq_length, latent_dim, use_time)
# eval_sample = np.empty(shape=(eval_size, seq_length, num_signals))
# for i in range(batch_multiplier):
# eval_sample[i * batch_size:(i + 1) * batch_size, :, :] = sess.run(G_sample, feed_dict={ Z: eval_Z[i * batch_size:(i + 1) * batch_size]})
# eval_sample = np.float32(eval_sample)
# eval_real = np.float32(samples['vali'][np.random.choice(len(samples['vali']), size=batch_multiplier * batch_size), :, :])
#
# eval_eval_real = eval_real[:eval_eval_size]
# eval_test_real = eval_real[eval_eval_size:]
# eval_eval_sample = eval_sample[:eval_eval_size]
# eval_test_sample = eval_sample[eval_eval_size:]
#
# # MMD
# # reset ADAM variables
# sess.run(tf.initialize_variables(sigma_opt_vars))
# sigma_iter = 0
# that_change = sigma_opt_thresh * 2
# old_that = 0
# while that_change > sigma_opt_thresh and sigma_iter < sigma_opt_iter:
# new_sigma, that_np, _ = sess.run([sigma, that, sigma_solver],
# feed_dict={eval_real_PH: eval_eval_real, eval_sample_PH: eval_eval_sample})
# that_change = np.abs(that_np - old_that)
# old_that = that_np
# sigma_iter += 1
# opt_sigma = sess.run(sigma)
# try:
# mmd2, that_np = sess.run(mix_rbf_mmd2_and_ratio(eval_test_real, eval_test_sample, biased=False, sigmas=sigma))
# except ValueError:
# mmd2 = 'NA'
# that = 'NA'
#
# MMD[epoch, ] = mmd2
# -- save model parameters -- #
model.dump_parameters(sub_id + '_' + str(seq_length) + '_' + str(epoch), sess)
np.save('./experiments/plots/gs/' + identifier + '_' + 'MMD.npy', MMD)
end = time() - begin
print('Training terminated | Training time=%d s' %(end) )
print("Training terminated | training time = %ds " % (time() - begin))