def init_mm_params(nb_components, latent_dims, alpha_scale=.1, beta_scale=1e-5, v_init=10., m_scale=1., C_scale=10.,
seed=0, as_variables=True, trainable=False, device='/gpu:0', name='gmm'):
with tf.name_scope('gmm_initialization'):
alpha_init = alpha_scale * tf.ones((nb_components,))
beta_init = beta_scale * tf.ones((nb_components,))
v_init = tf.tile([float(latent_dims + v_init)], [nb_components])
means_init = m_scale * tf.random_uniform((nb_components, latent_dims), minval=-1, maxval=1, seed=seed)
covariance_init = C_scale * tf.tile(tf.expand_dims(tf.eye(latent_dims), axis=0), [nb_components, 1, 1])
# transform to natural parameters
A, b, beta, v_hat = niw.standard_to_natural(beta_init, means_init, covariance_init, v_init)
alpha = dirichlet.standard_to_natural(alpha_init)
# init variable
if as_variables:
with tf.variable_scope(name):
alpha = variable_on_device('alpha_k', shape=None, initializer=alpha, trainable=trainable, device=device)
A = variable_on_device('beta_k', shape=None, initializer=A, trainable=trainable, device=device)
b = variable_on_device('m_k', shape=None, initializer=b, trainable=trainable, device=device)
beta = variable_on_device('C_k', shape=None, initializer=beta, trainable=trainable, device=device)
v_hat = variable_on_device('v_k', shape=None, initializer=v_hat, trainable=trainable, device=device)
params = alpha, A, b, beta, v_hat
return params
def make_nnet(input, layerspecs, stddev, name, param_device='/gpu:0', seed=0):
with tf.variable_scope(name):
# ravel inputs: (M, K, D) -> (M*K, D)
input_shape = input.get_shape()
input_dim = int(input_shape[-1])
input = tf.reshape(input, (-1, input_dim))
prev_layer = input
# create all layers except the output layer
for i, (hidden_units, activation) in enumerate(layerspecs[:-1]):
prev_layer = make_layer(prev_layer, hidden_units, stddev, activation, 'layer_%d' % i, param_device, seed)
# create output layer
output_dim, type = layerspecs[-1]
if type == 'bernoulli':
out_mlp = make_bernoulli_layer(prev_layer, output_dim, stddev, param_device=param_device, seed=seed)
else:
out_mlp = make_gaussian_layer(prev_layer, output_dim, stddev, type, param_device=param_device, seed=seed)
# create resnet-like shortcut (as in Johnson's SVAE code)
with tf.variable_scope('shortcut'):
orthonormal_cols = tf.constant(rand_partial_isometry(input_dim, output_dim, 1., seed=seed), dtype=tf.float32)
W = variable_on_device('W', shape=None, initializer=orthonormal_cols, trainable=True, device=param_device,
dtype=tf.float32)
b1 = variable_on_device('b1', shape=None, initializer=tf.zeros(output_dim), trainable=True,
device=param_device, dtype=tf.float32)
out_res = tf.add(tf.matmul(input, W), b1, name='res_shortcut_1')
# create shortcut for second output (in Gaussian case)
if type != 'bernoulli':
b2 = variable_on_device('b2', shape=None, initializer=tf.zeros(output_dim), trainable=True,
device=param_device, dtype=tf.float32)
if type == 'standard':
a = tf.constant(1., dtype=tf.float32)
elif type == 'natparam':
a = tf.constant(-0.5, dtype=tf.float32)
else:
raise NotImplementedError
out_res = (out_res, tf.multiply(a, tf.log1p(tf.exp(b2)), name='res_shortcut_2'))
with tf.variable_scope('resnet_out'):
# unravel output: (M*K, D) -> (M, K, D)
output_shape = input_shape[:-1].concatenate(output_dim)
if type == 'bernoulli':
outputs = tf.reshape(tf.add(out_mlp, out_res), output_shape, name='output')
else:
outputs = (
tf.reshape(tf.add(out_mlp[0], out_res[0]), output_shape, name='output_0'),
tf.reshape(tf.add(out_mlp[1], out_res[1]), output_shape, name='output_1')
)
return outputs
def make_loc_scale_variables(theta, param_device='/gpu:0', name='copy_m_v'):
# create location/scale variables for point estimations
with tf.name_scope(name):
theta_copied = niw.natural_to_standard(tf.identity(theta[1]), tf.identity(theta[2]),
tf.identity(theta[3]), tf.identity(theta[4]))
mu_k_init, sigma_k = niw.expected_values(theta_copied)
L_k_init = tf.cholesky(sigma_k)
mu_k = variable_on_device('mu_k', shape=None, initializer=mu_k_init, trainable=True, device=param_device)
L_k = variable_on_device('L_k', shape=None, initializer=L_k_init, trainable=True, device=param_device)
return mu_k, L_k
def init_recognition_params(theta, nb_components, seed=0, param_device='/gpu:0', var_scope='phi_gmm'):
# make parameters for PGM part of recognition network
with tf.name_scope('init_' + var_scope):
pi_k_init = tf.nn.softmax(tf.random_normal(shape=(nb_components,), mean=0.0, stddev=1., seed=seed))
with tf.variable_scope(var_scope):
mu_k, L_k = make_loc_scale_variables(theta, param_device)
pi_k = variable_on_device('log_pi_k', shape=None, initializer=pi_k_init, trainable=True, device=param_device)
return mu_k, L_k, pi_k
# init helper values for SMM (theta is a constant, we just need it to init the rec GMM below)
gmm_prior, theta = svae.init_mm(config['K'],
config['L'],
seed=config['seed'],
param_device=param_device,
theta_as_variable=False)
# create tensor for Student-t parameters
with tf.variable_scope('theta'):
mu_k, L_k = svae.make_loc_scale_variables(
gmm_prior, param_device=param_device)
DoF = config['DoF'] * tf.ones(
(config['K'], ), dtype=tf.float32)
DoF = variable_on_device('DoF_k',
shape=None,
initializer=DoF,
trainable=False,
device=param_device)
alpha_k = variable_on_device('alpha_k',
shape=None,
initializer=theta[0],
trainable=False,
device=param_device)
# init inference GMM parameters
phi_gmm = svae.init_recognition_params(
theta,
config['K'],
seed=config['seed'],
param_device=param_device)
def visualize_svae(ax,
config,
log_path,
ratio_tr=0.7,
nb_samples=20,
grid_density=100,
window=((-20, 20), (-20, 20)),
param_device='/cpu:0'):
with tf.device(param_device):
if config['dataset'] in ['mnist', 'fashion']:
binarise = True
size_minibatch = 1024
output_type = 'bernoulli'
else:
binarise = False
size_minibatch = -1
output_type = 'standard'
# First we build the model graph so that we can load the learned parameters from a checkpoint.
# Initialisations don't matter, they'll be overwritten with saver.restore().
data, lbl, _, _ = make_minibatch(config['dataset'],
path_datadir='../datasets',
ratio_tr=ratio_tr,
seed_split=0,
size_minibatch=size_minibatch,
size_testbatch=-1,
binarise=binarise)
# define nn-architecture
encoder_layers = [(config['U'], tf.tanh), (config['U'], tf.tanh),
(config['L'], 'natparam')]
decoder_layers = [(config['U'], tf.tanh), (config['U'], tf.tanh),
(int(data.get_shape()[1]), output_type)]
sample_size = 100
if config['dataset'] in ['mnist', 'fashion']:
data = tf.where(tf.equal(data, -1),
tf.zeros_like(data, dtype=tf.float32),
tf.ones_like(data, dtype=tf.float32))
with tf.name_scope('model'):
gmm_prior, theta = svae.init_mm(config['K'],
config['L'],
seed=config['seed'],
param_device='/gpu:0')
theta_copied = niw.natural_to_standard(tf.identity(gmm_prior[1]),
tf.identity(gmm_prior[2]),
tf.identity(gmm_prior[3]),
tf.identity(gmm_prior[4]))
_, sigma_k = niw.expected_values(theta_copied)
pi_k_init = tf.nn.softmax(
tf.random_normal(shape=(config['K'], ),
mean=0.0,
stddev=1.,
seed=config['seed']))
L_k = tf.cholesky(sigma_k)
mu_k = tf.random_normal(shape=(config['K'], config['L']),
stddev=1,
seed=config['seed'])
with tf.variable_scope('phi_gmm'):
mu_k = variable_on_device('mu_k',
shape=None,
initializer=mu_k,
trainable=True,
device=param_device)
L_k = variable_on_device('L_k',
shape=None,
initializer=L_k,
trainable=True,
device=param_device)
pi_k = variable_on_device('log_pi_k',
shape=None,
initializer=pi_k_init,
trainable=True,
device=param_device)
phi_gmm = mu_k, L_k, pi_k
_ = vae.make_encoder(data,
layerspecs=encoder_layers,
stddev_init=.1,
seed=config['seed'])
with tf.name_scope('random_sampling'):
# compute expected theta_pgm
beta_k, m_k, C_k, v_k = niw.natural_to_standard(*theta[1:])
alpha_k = dirichlet.natural_to_standard(theta[0])
mean, cov = niw.expected_values((beta_k, m_k, C_k, v_k))
expected_log_pi = dirichlet.expected_log_pi(alpha_k)
pi = tf.exp(expected_log_pi)
# sample from prior (first from
x_k_samples = tf.contrib.distributions.MultivariateNormalFullCovariance(
loc=mean, covariance_matrix=cov).sample(sample_size)
z_samples = tf.multinomial(logits=tf.reshape(tf.log(pi), (1, -1)),
num_samples=sample_size,
name='k_samples')
z_samples = tf.squeeze(z_samples)
assert z_samples.get_shape() == (sample_size, )
assert x_k_samples.get_shape() == (sample_size, config['K'],
config['L'])
# compute reconstructions
y_k_samples, _ = vae.make_decoder(x_k_samples,
layerspecs=decoder_layers,
stddev_init=.1,
seed=config['seed'])
assert y_k_samples.get_shape() == (sample_size, config['K'],
data.get_shape()[1])
with tf.name_scope('cluster_sample_data'):
tf.get_variable_scope().reuse_variables()
_, clustering = svae.predict(data,
phi_gmm,
encoder_layers,
decoder_layers,
seed=config['seed'])
# load trained model
saver = tf.train.Saver()
model_path = log_path + '/' + generate_log_id(config)
print(model_path)
latest_ckpnt = tf.train.latest_checkpoint(model_path)
sess_config = tf.ConfigProto(allow_soft_placement=True)
sess = tf.Session(config=sess_config)
saver.restore(sess, latest_ckpnt)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
collected_y_samps = []
collected_z_samps = []
for s in range(nb_samples):
y_samps, z_samps = sess.run((y_k_samples, z_samples))
collected_y_samps.append(y_samps)
collected_z_samps.append(z_samps)
collected_y_samps = np.concatenate(collected_y_samps, axis=0)
collected_z_samps = np.concatenate(collected_z_samps, axis=0)
assert collected_y_samps.shape == (nb_samples * sample_size,
config['K'], data.shape[1])
assert collected_z_samps.shape == (nb_samples * sample_size, )
# use 300 sample points from the dataset
data, lbl, clustering = sess.run(
(data[:300], lbl[:300], clustering[:300]))
# compute PCA if necessary
samples_2d = []
if data.shape[1] > 2:
pca = PCA(n_components=2).fit(data)
data2d = pca.transform(data)
for z_samples in range(config['K']):
chosen = collected_z_samps == z_samples
samps_k = collected_y_samps[chosen, z_samples, :]
if samps_k.size > 0:
samples_2d.append(pca.transform(samps_k))
else:
data2d = data
for z_samples in range(config['K']):
chosen = (collected_z_samps == z_samples)
samps_k = collected_y_samps[chosen, z_samples, :]
if samps_k.size > 0:
samples_2d.append(samps_k)
# plot 2d-histogram (one histogram for each of the K components)
from matplotlib.colors import LogNorm
for z_samples, samples in enumerate(samples_2d):
ax.hist2d(samples[:, 0],
samples[:, 1],
bins=grid_density,
range=window,
cmap=make_colormap(dark_colors[z_samples %
len(dark_colors)]),
normed=True,
norm=LogNorm())
# overlay histogram with sample datapoints (coloured according to their most likely cluster allocation)
labels = np.argmax(lbl, axis=1)
for c in np.unique(labels):
in_class_c = (labels == c)
color = bright_colors[int(c % len(bright_colors))]
marker = markers[int(c % len(markers))]
ax.scatter(data2d[in_class_c, 0],
data2d[in_class_c, 1],
c=color,
marker=marker,
s=data_dot_size,
linewidths=0)