def p_net(observed=None, n_y=None, n_z=None, n_samples=None):
if n_samples is not None:
warnings.warn('`n_samples` is deprecated, use `n_y` instead.')
n_y = n_samples
net = spt.BayesianNet(observed=observed)
# sample y
y = net.add('y',
spt.Categorical(tf.zeros([1, config.n_clusters])),
n_samples=n_y)
# sample z ~ p(z|y)
z = net.add('z',
gaussian_mixture_prior(y, config.z_dim, config.n_clusters),
group_ndims=1,
n_samples=n_z,
is_reparameterized=False)
# compute the hidden features for x
with arg_scope([spt.layers.dense],
activation_fn=tf.nn.leaky_relu,
kernel_regularizer=spt.layers.l2_regularizer(
config.l2_reg)):
h_x = z
h_x = spt.layers.dense(h_x, 500)
h_x = spt.layers.dense(h_x, 500)
# sample x ~ p(x|z)
x_logits = spt.layers.dense(h_x, config.x_dim, name='x_logits')
x = net.add('x', spt.Bernoulli(logits=x_logits), group_ndims=1)
return net
def q_net(x, observed=None, n_samples=None):
net = spt.BayesianNet(observed=observed)
# compute the hidden features
with arg_scope([spt.layers.dense],
activation_fn=tf.nn.leaky_relu,
kernel_regularizer=spt.layers.l2_regularizer(
config.l2_reg)):
h_x = tf.to_float(x)
h_x = spt.layers.dense(h_x, 500)
h_x = spt.layers.dense(h_x, 500)
# sample y ~ q(y|x)
y_logits = spt.layers.dense(h_x, config.n_clusters, name='y_logits')
y = net.add('y', spt.Categorical(y_logits), n_samples=n_samples)
y_one_hot = tf.one_hot(y, config.n_clusters, dtype=tf.float32)
# sample z ~ q(z|y,x)
with arg_scope([spt.layers.dense],
activation_fn=tf.nn.leaky_relu,
kernel_regularizer=spt.layers.l2_regularizer(
config.l2_reg)):
if config.mean_field_assumption_for_q:
# by mean-field-assumption we let q(z|y,x) = q(z|x)
h_z = h_x
z_n_samples = n_samples
else:
if n_samples is not None:
h_z = tf.concat([
tf.tile(tf.reshape(h_x, [1, -1, 500]),
tf.stack([n_samples, 1, 1])), y_one_hot
],
axis=-1)
else:
h_z = tf.concat([h_x, y_one_hot], axis=-1)
h_z = spt.layers.dense(h_z, 500)
z_n_samples = None
z_mean = spt.layers.dense(h_z, config.z_dim, name='z_mean')
z_logstd = spt.layers.dense(h_z, config.z_dim, name='z_logstd')
z = net.add('z',
spt.Normal(mean=z_mean,
logstd=z_logstd,
is_reparameterized=False),
n_samples=z_n_samples,
group_ndims=1)
return net
def p_net(observed=None, n_z=None, is_initializing=False):
net = spt.BayesianNet(observed=observed)
normalizer_fn = functools.partial(spt.layers.act_norm,
initializing=is_initializing)
# sample z ~ p(z)
def make_component(i):
normal = spt.Normal(mean=tf.get_variable('mean_{}'.format(i),
shape=[1, config.z_dim],
dtype=tf.float32,
trainable=True),
logstd=tf.maximum(
tf.get_variable('logstd_{}'.format(i),
shape=[1, config.z_dim],
dtype=tf.float32,
trainable=True),
config.z_logstd_min))
return normal.expand_value_ndims(1)
components = [
make_component(i) for i in range(config.n_mixture_components)
]
mixture = spt.Mixture(categorical=spt.Categorical(
logits=tf.zeros([1, config.n_mixture_components])),
components=components,
is_reparameterized=True)
z = net.add('z', mixture, n_samples=n_z)
# compute the hidden features
with arg_scope([spt.layers.dense],
activation_fn=tf.nn.leaky_relu,
normalizer_fn=normalizer_fn,
weight_norm=True,
kernel_regularizer=spt.layers.l2_regularizer(
config.l2_reg)):
h_z = z
h_z = spt.layers.dense(h_z, 500)
h_z = spt.layers.dense(h_z, 500)
# sample x ~ p(x|z)
x_logits = spt.layers.dense(h_z, config.x_dim, name='x_logits')
x = net.add('x', spt.Bernoulli(logits=x_logits), group_ndims=1)
return net
def q_net(x, observed=None, n_z=None, is_initializing=False):
net = spt.BayesianNet(observed=observed)
normalizer_fn = functools.partial(spt.layers.act_norm,
initializing=is_initializing)
# compute the hidden features
with arg_scope([spt.layers.dense],
activation_fn=tf.nn.leaky_relu,
normalizer_fn=normalizer_fn,
weight_norm=True,
kernel_regularizer=spt.layers.l2_regularizer(
config.l2_reg)):
h_x = tf.to_float(x)
h_x = spt.layers.dense(h_x, 500)
h_x = spt.layers.dense(h_x, 500)
# sample z ~ q(z|x)
components = [
spt.Normal(mean=spt.layers.dense(h_x,
config.z_dim,
name='z_mean_{}'.format(i)),
logstd=spt.layers.dense(h_x,
config.z_dim,
name='z_logstd_{}'.format(i)))
for i in range(config.n_mixture_components)
]
mixture_param_shape = spt.utils.concat_shapes([
spt.utils.get_shape(components[0].mean), [config.n_mixture_components]
])
mixture = spt.Mixture(
categorical=spt.Categorical(logits=tf.zeros(mixture_param_shape)),
components=components,
is_reparameterized=True)
z = net.add('z', mixture, n_samples=n_z, group_ndims=1)
return net
def p_net(observed=None, n_z=None, is_training=False, is_initializing=False):
"""
Generative net
return p net structure.
"""
net = spt.BayesianNet(observed=observed)
normalizer_fn = None if not config.act_norm else functools.partial(
spt.layers.act_norm,
axis=-1 if config.channels_last else -3,
initializing=is_initializing,
value_ndims=3,
)
def make_component(i):
normal = spt.Normal(mean=tf.get_variable('mean_{}'.format(i),
shape=[1, config.z_dim],
dtype=tf.float32,
trainable=True),
logstd=tf.maximum(
tf.get_variable('logstd_{}'.format(i),
shape=[1, config.z_dim],
dtype=tf.float32,
trainable=True), -1.))
return normal.expand_value_ndims(1)
components = [make_component(i) for i in range(config.n_c)]
mixture = spt.Mixture(
categorical=spt.Categorical(logits=tf.zeros([1, config.n_c])),
components=components,
is_reparameterized=True)
z = net.add('z', mixture, n_samples=n_z)
print("=" * 10 + "pnet" + "=" * 10)
# compute the hidden features
with arg_scope([spt.layers.resnet_deconv2d_block],
kernel_size=config.kernel_size2,
shortcut_kernel_size=config.shortcut_kernel_size,
activation_fn=tf.nn.elu,
normalizer_fn=normalizer_fn,
kernel_regularizer=spt.layers.l2_regularizer(config.l2_reg),
channels_last=config.channels_last):
print("px:%s" % z.get_shape())
h_z = spt.layers.dense(
z,
int(config.timeLength / (config.strides1**2) /
(config.strides2**2) * int(config.metricNumber)))
h_z = spt.ops.reshape_tail(
h_z,
ndims=1,
shape=(int(config.timeLength /
(config.strides1**2) / (config.strides2**2)),
int(config.metricNumber), 1) if config.channels_last else
(1,
int(config.timeLength / (config.strides1**2) /
(config.strides2**2)), int(config.metricNumber)))
print("p1:%s" % h_z.get_shape())
h_z = spt.layers.resnet_deconv2d_block(
h_z,
1,
kernel_size=(config.kernel_size2, 1),
strides=(config.strides2, 1))
print("p2:%s" % h_z.get_shape())
h_z = spt.layers.resnet_deconv2d_block(
h_z,
1,
kernel_size=(config.kernel_size2, 1),
strides=(config.strides2, 1))
print("p3:%s" % h_z.get_shape())
h_z = spt.layers.resnet_deconv2d_block(
h_z,
1,
kernel_size=(config.kernel_size1, 1),
strides=(config.strides1, 1))
print("p4:%s" % h_z.get_shape())
h_z = spt.layers.resnet_deconv2d_block(
h_z,
1,
kernel_size=(config.kernel_size1, 1),
strides=(config.strides1, 1))
print("p5:%s" % h_z.get_shape())
# sample x ~ p(x|z)
x_mean = spt.layers.conv2d(h_z,
1, (1, 1),
padding='same',
name='x_mean',
channels_last=config.channels_last)
x_logstd = spt.layers.conv2d(
h_z,
1,
(1, 1),
padding='same',
name='x_logstd',
channels_last=config.channels_last,
activation_fn=tf.nn.elu,
) + config.std_epsilon
x = net.add('x',
spt.Normal(mean=x_mean, logstd=x_logstd),
n_samples=n_z,
group_ndims=3)
print("p6:%s, %s, %s" %
(x_mean.get_shape(), x_logstd.get_shape(), x.get_shape()))
return net
def q_net(x,
observed=None,
n_z=None,
is_training=False,
is_initializing=False):
"""
Inference net
param x: input X, multivariate time series data.
return q net structure.
"""
net = spt.BayesianNet(observed=observed)
normalizer_fn = None if not config.act_norm else functools.partial(
spt.layers.act_norm,
axis=-1 if config.channels_last else -3,
initializing=is_initializing,
value_ndims=3,
)
print("=" * 10 + "qnet" + "=" * 10)
# compute the hidden features
with arg_scope([spt.layers.resnet_conv2d_block],
kernel_size=config.kernel_size2,
shortcut_kernel_size=config.shortcut_kernel_size,
activation_fn=tf.nn.elu,
normalizer_fn=normalizer_fn,
kernel_regularizer=spt.layers.l2_regularizer(config.l2_reg),
channels_last=config.channels_last):
print("qx:%s" % x.get_shape())
h_x = tf.reshape(tf.to_float(x),
[-1, config.timeLength, config.metricNumber, 1]
if config.channels_last else
[-1, 1, config.timeLength, config.metricNumber])
print("q1:%s" % h_x.get_shape())
h_x = spt.layers.resnet_conv2d_block(h_x,
1,
kernel_size=(config.kernel_size1,
1),
strides=(config.strides1, 1))
print("q2:%s" % h_x.get_shape())
h_x = spt.layers.resnet_conv2d_block(h_x,
1,
kernel_size=(config.kernel_size1,
1),
strides=(config.strides1, 1))
print("q3:%s" % h_x.get_shape())
h_x = spt.layers.resnet_conv2d_block(h_x,
1,
kernel_size=(config.kernel_size2,
1),
strides=(config.strides2, 1))
print("q4:%s" % h_x.get_shape())
h_x = spt.layers.resnet_conv2d_block(h_x,
1,
kernel_size=(config.kernel_size2,
1),
strides=(config.strides2, 1))
print("q5:%s" % h_x.get_shape())
h_x = spt.ops.reshape_tail(h_x, ndims=3, shape=[-1])
print("q6:%s" % h_x.get_shape())
# sample y ~ q(y|x)
c_logits = spt.layers.dense(h_x, config.n_c, name='c_logits')
c = net.add('c', spt.Categorical(c_logits))
c_one_hot = tf.one_hot(c, config.n_c, dtype=tf.float32)
print("qc:%s, %s, %s" % (c_logits.shape, c.shape, c_one_hot.shape))
h_z = h_x
# sample z ~ q(z|x)
z_mean = spt.layers.dense(h_z, config.z_dim, name='z_mean')
z_logstd = spt.layers.dense(
h_z, config.z_dim, name='z_logstd',
activation_fn=tf.nn.elu) + config.std_epsilon
z = net.add('z',
spt.Normal(mean=z_mean, logstd=z_logstd),
n_samples=n_z,
group_ndims=1)
print("q7:%s, %s, %s" %
(z_mean.get_shape(), z_logstd.get_shape(), z.get_shape()))
return net