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Gene_ClusterGAN.py
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Gene_ClusterGAN.py
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import os
import time
import dateutil.tz
import datetime
import argparse
import importlib
import tensorflow as tf
import numpy as np
from sklearn.cluster import KMeans
from sklearn.metrics.cluster import normalized_mutual_info_score, adjusted_rand_score
import metric
import util
tf.set_random_seed(0)
class clusGAN(object):
def __init__(self, g_net, d_net, enc_net, x_sampler, z_sampler, data, model, sampler,
num_classes, dim_gen, n_cat, batch_size, beta_cycle_gen, beta_cycle_label):
self.model = model
self.data = data
self.sampler = sampler
self.g_net = g_net
self.d_net = d_net
self.enc_net = enc_net
self.x_sampler = x_sampler
self.z_sampler = z_sampler
self.num_classes = num_classes
self.dim_gen = dim_gen
self.n_cat = n_cat
self.batch_size = batch_size
scale = 10.0
self.beta_cycle_gen = beta_cycle_gen
self.beta_cycle_label = beta_cycle_label
self.x_dim = self.d_net.x_dim
self.z_dim = self.g_net.z_dim
self.x = tf.placeholder(tf.float32, [None, self.x_dim], name='x')
self.z = tf.placeholder(tf.float32, [None, self.z_dim], name='z')
self.z_gen = self.z[:,0:self.dim_gen]
self.z_hot = self.z[:,self.dim_gen:]
self.x_ = self.g_net(self.z)
self.z_enc_gen, self.z_enc_label, self.z_enc_logits = self.enc_net(self.x_, reuse=False)
self.z_infer_gen, self.z_infer_label, self.z_infer_logits = self.enc_net(self.x)
self.d = self.d_net(self.x, reuse=False)
self.d_ = self.d_net(self.x_)
self.g_loss = tf.reduce_mean(self.d_) + \
self.beta_cycle_gen * tf.reduce_mean(tf.square(self.z_gen - self.z_enc_gen)) +\
self.beta_cycle_label * tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(logits=self.z_enc_logits,labels=self.z_hot))
self.d_loss = tf.reduce_mean(self.d) - tf.reduce_mean(self.d_)
epsilon = tf.random_uniform([], 0.0, 1.0)
x_hat = epsilon * self.x + (1 - epsilon) * self.x_
d_hat = self.d_net(x_hat)
ddx = tf.gradients(d_hat, x_hat)[0]
ddx = tf.sqrt(tf.reduce_sum(tf.square(ddx), axis=1))
ddx = tf.reduce_mean(tf.square(ddx - 1.0) * scale)
self.d_loss = self.d_loss + ddx
self.d_adam = tf.train.AdamOptimizer(learning_rate=1e-4, beta1=0.5, beta2=0.9) \
.minimize(self.d_loss, var_list=self.d_net.vars)
self.g_adam = tf.train.AdamOptimizer(learning_rate=1e-4, beta1=0.5, beta2=0.9) \
.minimize(self.g_loss, var_list=[self.g_net.vars, self.enc_net.vars])
# Reconstruction Nodes
self.recon_loss = tf.reduce_mean(tf.abs(self.x - self.x_), 1)
self.compute_grad = tf.gradients(self.recon_loss, self.z)
self.saver = tf.train.Saver()
run_config = tf.ConfigProto()
run_config.gpu_options.per_process_gpu_memory_fraction = 1.0
run_config.gpu_options.allow_growth = True
self.sess = tf.Session(config=run_config)
def train(self, num_batches=100000):
now = datetime.datetime.now(dateutil.tz.tzlocal())
timestamp = now.strftime('%Y_%m_%d_%H_%M_%S')
batch_size = self.batch_size
self.sess.run(tf.global_variables_initializer())
start_time = time.time()
print(
'Training {} on {}, sampler = {}, z = {} dimension, beta_n = {}, beta_c = {}'.
format(self.model, self.data, self.sampler, self.z_dim, self.beta_cycle_gen, self.beta_cycle_label))
for t in range(0, num_batches):
d_iters = 5
for _ in range(0, d_iters):
bx = self.x_sampler.train(batch_size)
bz = self.z_sampler(batch_size, self.z_dim, self.sampler, self.num_classes, self.n_cat)
self.sess.run(self.d_adam, feed_dict={self.x: bx, self.z: bz})
bz = self.z_sampler(batch_size, self.z_dim, self.sampler, self.num_classes, self.n_cat)
self.sess.run(self.g_adam, feed_dict={self.z: bz})
if (t+1) % 100 == 0:
bx = self.x_sampler.train(batch_size)
bz = self.z_sampler(batch_size, self.z_dim, self.sampler, self.num_classes, self.n_cat)
d_loss = self.sess.run(
self.d_loss, feed_dict={self.x: bx, self.z: bz}
)
g_loss = self.sess.run(
self.g_loss, feed_dict={self.z: bz}
)
print('Iter [%8d] Time [%5.4f] d_loss [%.4f] g_loss [%.4f]' %
(t+1, time.time() - start_time, d_loss, g_loss))
self.recon_enc(timestamp, val=True)
self.save(timestamp)
def save(self, timestamp):
checkpoint_dir = 'checkpoint_dir/{}/{}_{}_{}_z{}_cyc{}_gen{}'.format(self.data, timestamp, self.model, self.sampler,
self.z_dim, self.beta_cycle_label,
self.beta_cycle_gen)
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
self.saver.save(self.sess, os.path.join(checkpoint_dir, 'model.ckpt'))
def load(self, pre_trained = False, timestamp = ''):
if pre_trained == True:
print('Loading Pre-trained Model...')
checkpoint_dir = 'pre_trained_models/{}/{}_{}_z{}_cyc{}_gen{}'.format(self.data, self.model, self.sampler,
self.z_dim, self.beta_cycle_label, self.beta_cycle_gen)
else:
if timestamp == '':
print('Best Timestamp not provided. Abort !')
checkpoint_dir = ''
else:
checkpoint_dir = 'checkpoint_dir/{}/{}_{}_{}_z{}_cyc{}_gen{}'.format(self.data, timestamp, self.model, self.sampler,
self.z_dim, self.beta_cycle_label,
self.beta_cycle_gen)
self.saver.restore(self.sess, os.path.join(checkpoint_dir, 'model.ckpt'))
print('Restored model weights.')
def _gen_samples(self, num_samples):
batch_size = self.batch_size
bz = self.z_sampler(batch_size, self.z_dim, self.sampler, self.num_classes, self.n_cat)
fake_samples = self.sess.run(self.x_, feed_dict = {self.z : bz})
for t in range(num_samples // batch_size):
bz = self.z_sampler(batch_size, self.z_dim, self.sampler, self.num_classes, self.n_cat)
samp = self.sess.run(self.x_, feed_dict = {self.z : bz})
fake_samples = np.vstack((fake_samples, samp))
print(' Generated {} samples .'.format(fake_samples.shape[0]))
np.save('./Image_samples/{}/{}_{}_K_{}_gen_images.npy'.format(self.data, self.model, self.sampler, self.num_classes), fake_samples)
def recon_enc(self, timestamp, val = True):
if val:
data_recon, label_recon = self.x_sampler.validation()
else:
data_recon, label_recon = self.x_sampler.test()
#data_recon, label_recon = self.x_sampler.load_all()
num_pts_to_plot = data_recon.shape[0]
recon_batch_size = self.batch_size
latent = np.zeros(shape=(num_pts_to_plot, self.z_dim))
print('Data Shape = {}, Labels Shape = {}'.format(data_recon.shape, label_recon.shape))
for b in range(int(np.ceil(num_pts_to_plot*1.0 / recon_batch_size))):
if (b+1)*recon_batch_size > num_pts_to_plot:
pt_indx = np.arange(b*recon_batch_size, num_pts_to_plot)
else:
pt_indx = np.arange(b*recon_batch_size, (b+1)*recon_batch_size)
xtrue = data_recon[pt_indx, :]
zhats_gen, zhats_label = self.sess.run([self.z_infer_gen, self.z_infer_label], feed_dict={self.x : xtrue})
latent[pt_indx, :] = np.concatenate((zhats_gen, zhats_label), axis=1)
if self.beta_cycle_gen == 0:
self._eval_cluster(latent[:, self.dim_gen:], label_recon, timestamp, val)
else:
self._eval_cluster(latent, label_recon, timestamp, val)
def _eval_cluster(self, latent_rep, labels_true, timestamp, val):
map_labels = {0: 0, 1: 1, 2: 2, 4: 3, 6: 4, 7: 5, 8: 6, 9: 7}
labels_true = np.array([map_labels[i] for i in labels_true])
km = KMeans(n_clusters=max(self.num_classes, len(np.unique(labels_true))), random_state=0).fit(latent_rep)
labels_pred = km.labels_
purity = metric.compute_purity(labels_pred, labels_true)
ari = adjusted_rand_score(labels_true, labels_pred)
nmi = normalized_mutual_info_score(labels_true, labels_pred)
if val:
data_split = 'Validation'
else:
data_split = 'Test'
#data_split = 'All'
print('Data = {}, Model = {}, sampler = {}, z_dim = {}, beta_label = {}, beta_gen = {} '
.format(self.data, self.model, self.sampler, self.z_dim, self.beta_cycle_label, self.beta_cycle_gen))
print(' #Points = {}, K = {}, Purity = {}, NMI = {}, ARI = {}, '
.format(latent_rep.shape[0], self.num_classes, purity, nmi, ari))
with open('logs/Res_{}_{}.txt'.format(self.data, self.model), 'a+') as f:
f.write('{}, {} : K = {}, z_dim = {}, beta_label = {}, beta_gen = {}, sampler = {}, Purity = {}, NMI = {}, ARI = {}\n'
.format(timestamp, data_split, self.num_classes, self.z_dim, self.beta_cycle_label, self.beta_cycle_gen,
self.sampler, purity, nmi, ari))
f.flush()
if __name__ == '__main__':
parser = argparse.ArgumentParser('')
parser.add_argument('--data', type=str, default='10x_73k')
parser.add_argument('--model', type=str, default='clus_wgan')
parser.add_argument('--sampler', type=str, default='one_hot')
parser.add_argument('--K', type=int, default=8)
parser.add_argument('--dz', type=int, default=30)
parser.add_argument('--bs', type=int, default=64)
parser.add_argument('--beta_n', type=float, default=10.0)
parser.add_argument('--beta_c', type=float, default=10.0)
parser.add_argument('--timestamp', type=str, default='')
parser.add_argument('--train', type=str, default='False')
args = parser.parse_args()
data = importlib.import_module(args.data)
model = importlib.import_module(args.data + '.' + args.model)
num_classes = args.K
dim_gen = args.dz
n_cat = 1
batch_size = args.bs
beta_cycle_gen = args.beta_n
beta_cycle_label = args.beta_c
timestamp = args.timestamp
z_dim = dim_gen + num_classes * n_cat
d_net = model.Discriminator()
g_net = model.Generator(z_dim=z_dim)
enc_net = model.Encoder(z_dim=z_dim, dim_gen = dim_gen)
xs = data.DataSampler()
zs = util.sample_Z
cl_gan = clusGAN(g_net, d_net, enc_net, xs, zs, args.data, args.model, args.sampler,
num_classes, dim_gen, n_cat, batch_size, beta_cycle_gen, beta_cycle_label)
if args.train == 'True':
cl_gan.train()
else:
print('Attempting to Restore Model ...')
if timestamp == '':
cl_gan.load(pre_trained=True)
timestamp = 'pre-trained'
else:
cl_gan.load(pre_trained=False, timestamp = timestamp)
cl_gan.recon_enc(timestamp, val=False)