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gaussian-toy-regularized.py
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gaussian-toy-regularized.py
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from collections import OrderedDict
import tensorflow as tf
import numpy as np
import matplotlib as mpl
mpl.use('Agg')
from matplotlib.pyplot import *
import seaborn as sns
from tqdm import tqdm
ds = tf.contrib.distributions
slim = tf.contrib.slim
graph_replace = tf.contrib.graph_editor.graph_replace
from keras.optimizers import Adam
def sample_mog(batch_size, n_mixture=8, std=0.02, radius=2.0):
thetas = np.linspace(0, 2 * np.pi * (n_mixture-1)/float(n_mixture), n_mixture)
xs, ys = radius * np.sin(thetas), radius * np.cos(thetas)
cat = ds.Categorical(tf.zeros(n_mixture))
comps = [ds.MultivariateNormalDiag([xi, yi], [std, std]) for xi, yi in zip(xs.ravel(), ys.ravel())]
data = ds.Mixture(cat, comps)
return data.sample(batch_size)
params = dict(
batch_size=512,
disc_learning_rate=1e-4,
gen_learning_rate=1e-4,
beta1=0.5,
epsilon=1e-8,
max_iter=100001,
viz_every=1000,
z_dim=256,
x_dim=2,
unrolling_steps=0,
regularizer_weight=0.5,
)
def extract_step_dict(update_ops):
"""Extract variables and their new values from Assign and AssignAdd ops.
Args:
update_ops: list of Assign and AssignAdd ops, typically computed using Keras' opt.get_updates()
Returns:
dict mapping from variables to their step updates
"""
name_to_var = {v.name: v for v in tf.global_variables()}
updates = OrderedDict()
for update in update_ops:
var_name = update.op.inputs[0].name
var = name_to_var[var_name]
value = update.op.inputs[1]
if update.op.type == 'Assign':
updates[var] = value - var
elif update.op.type == 'AssignAdd':
updates[var] = value
else:
raise ValueError("Update op type (%s) must be of type Assign or AssignAdd"%update_op.op.type)
return updates
def generator(z, output_dim=2, n_hidden=128, n_layer=2):
with tf.variable_scope("generator"):
h = slim.stack(z, slim.fully_connected, [n_hidden] * n_layer, activation_fn=tf.nn.relu)
x = slim.fully_connected(h, output_dim, activation_fn=None)
return x
def discriminator(x, n_hidden=128, n_layer=1, reuse=False):
with tf.variable_scope("discriminator", reuse=reuse):
h = slim.stack(tf.divide(x,4.0), slim.fully_connected, [n_hidden] * n_layer, activation_fn=tf.nn.relu)
log_d = slim.fully_connected(h, 1, activation_fn=None)
return log_d
tf.reset_default_graph()
data = sample_mog(params['batch_size'])
noise = ds.Normal(tf.zeros(params['z_dim']),
tf.ones(params['z_dim'])).sample(params['batch_size'])
# Construct generator and discriminator nets
with slim.arg_scope([slim.fully_connected], weights_initializer=tf.orthogonal_initializer(gain=0.8)):
samples = generator(noise, output_dim=params['x_dim'])
real_score = discriminator(data)
fake_score = discriminator(samples, reuse=True)
# D maximizes this, G minimizes this + a regularizer
V = -tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=real_score, labels=tf.ones_like(real_score)) +
tf.nn.sigmoid_cross_entropy_with_logits(logits=fake_score, labels=tf.zeros_like(fake_score)))
gen_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, "generator")
disc_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, "discriminator")
# Vanilla discriminator update
d_opt = Adam(lr=params['disc_learning_rate'], beta_1=params['beta1'], epsilon=params['epsilon'])
d_updates = d_opt.get_updates(disc_vars, [], -V)
d_train_op = tf.group(*d_updates, name="d_train_op")
step_dict = extract_step_dict(d_updates)
# Penalty
d_gradient_norm_sq = tf.square(tf.global_norm(tf.gradients(V, disc_vars)))
g_opt = Adam(lr=params['gen_learning_rate'], beta_1=params['beta1'], epsilon=params['epsilon'])
g_updates = g_opt.get_updates(gen_vars, [], V +params['regularizer_weight']*d_gradient_norm_sq)
g_train_op = tf.group(*g_updates, name="g_train_op")
# Or alternatively:
'''
d_opt = tf.train.AdamOptimizer(params['disc_learning_rate'], beta1=params['beta1'], epsilon=params['epsilon'])
g_opt = tf.train.AdamOptimizer(params['gen_learning_rate'], beta1=params['beta1'], epsilon=params['epsilon'])
d_train_op = d_opt.minimize(-V, var_list=disc_vars)
g_train_op = g_opt.minimize(V+params['regularizer_weight']*d_gradient_norm_sq, var_list=gen_vars)
'''
norm_d = tf.global_norm(tf.gradients(V, disc_vars))
norm_g = tf.global_norm(tf.gradients(V, gen_vars))
sess = tf.InteractiveSession(config=tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True)))
sess.run(tf.global_variables_initializer())
xmax = 3
fs = []
frames = []
np_samples = []
ds = [] # first gradients
gs = []
n_batches_viz = 10
viz_every = params['viz_every']
for i in tqdm(range(params['max_iter'])):
sess.run(d_train_op)
sess.run(g_train_op)
d, g, f, = sess.run([norm_d, norm_g, V])
fs.append(f)
ds.append(d)
gs.append(g)
if i % viz_every == 0:
np_samples.append(np.vstack([sess.run(samples) for _ in range(n_batches_viz)]))
xx, yy = sess.run([samples, data])
fig = figure(figsize=(5,5))
scatter(xx[:, 0], xx[:, 1], edgecolor='none')
scatter(yy[:, 0], yy[:, 1], c='g', edgecolor='none')
axis('off')
fig.savefig('fig'+str(i)+'.pdf')
close(fig)
np.savetxt("d_norm.out",np.array(ds))
np.savetxt("g_norm.out",np.array(gs))
fig = figure()
ax = subplot(111)
ax.set_ylabel('Discriminator Gradient L2 Norm')
ax.set_xlabel('Iteration')
plot(range(len(ds)), ds)
fig.savefig('d_norm.pdf')
fig = figure()
ax = subplot(111)
plot(range(len(gs)), gs)
ax.set_ylabel('Generator Gradient L2 Norm')
ax.set_xlabel('Iteration')
fig.savefig('g_norm.pdf')
np_samples_ = np_samples[::1]
cols = len(np_samples_)
bg_color = sns.color_palette('Greens', n_colors=256)[0]
fig=figure(figsize=(2*cols, 2))
for i, samps in enumerate(np_samples_):
if i == 0:
ax = subplot(1,cols,1)
else:
subplot(1,cols,i+1, sharex=ax, sharey=ax)
ax2 = sns.kdeplot(samps[:, 0], samps[:, 1], shade=True, cmap='Greens', n_levels=20, clip=[[-xmax,xmax]]*2)
ax2.set_axis_bgcolor(bg_color)
xticks([]); yticks([])
title('step %d'%(i*viz_every))
ax.set_ylabel('%d unrolling steps'%params['unrolling_steps'])
gcf().tight_layout()
fig.savefig('series.pdf')
np.savetxt("loss.out",np.array(fs))
fig=figure()
fs = np.array(fs)
plot(range(len(fs)),fs)
ax = subplot(111)
ax.set_ylabel('Loss')
ax.set_xlabel('Iteration')
fig.savefig('loss.pdf')