def __init__(self,
vis_dim,
hid_dim,
w=None,
l=None,
vis_b=None,
hid_b=None):
self.vis_dim = vis_dim
self.hid_dim = hid_dim
if w is not None:
self.w = w
else:
self.w = tfe.Variable(tf_xavier_init(self.vis_dim,
self.hid_dim,
const=4.0),
name='semibm.w')
if l is not None:
self.l = l
else:
self.l = tfe.Variable(tf_xavier_init(self.vis_dim,
self.vis_dim,
const=4.0),
name='semibm.l')
if hid_b is not None:
self.hid_b = hid_b
else:
self.hid_b = tfe.Variable(tf.zeros([self.hid_dim]),
dtype=tf.float32,
name='semibm.hid_b')
if vis_b is not None:
self.vis_b = vis_b
else:
self.vis_b = tfe.Variable(tf.zeros([self.vis_dim]),
dtype=tf.float32,
name='semibm.vis_b')
def __init__(self,
dims,
name,
activation_fn=tf.nn.relu,
temp=.1,
hard=False):
self.dims = dims
self.ws, self.bs = [], []
if activation_fn == tf.nn.relu:
const = 1.0
else:
const = 4.0
assert len(self.dims) > 1
for d1, d2, i in zip(dims[:-1], dims[1:], xrange(1, len(self.dims))):
self.ws.append(
tfe.Variable(tf_xavier_init(d1, d2, const=const),
name='enc.' + name + '.w.' + str(i)))
self.bs.append(
tfe.Variable(tf.zeros([d2]),
dtype=tf.float32,
name='enc.' + name + '.b.' + str(i)))
self.activation_fn = activation_fn
self.temp = temp
self.hard = hard
def __init__(self,
dim_z2h,
dim_h2v,
activation_fn=tf.nn.relu,
temp=.1,
hard=False,
train_pi=True):
self.prior_h = NADE(dim_z2h[1], dim_z2h[0], temp)
self.dim_h2v = dim_h2v
self.w_h2v, self.b_h2v = [], []
if activation_fn == tf.nn.relu:
const = 1.0
else:
const = 4.0
assert len(self.dim_h2v) > 1
for d1, d2, i in zip(dim_h2v[:-1], dim_h2v[1:],
xrange(1, len(self.dim_h2v))):
self.w_h2v.append(
tfe.Variable(tf_xavier_init(d1, d2, const=const),
name='sbn.w_h2v.' + str(i)))
self.b_h2v.append(
tfe.Variable(tf.zeros([d2]),
dtype=tf.float32,
name='sbn.b_h2v.' + str(i)))
self.activation_fn = activation_fn
self.temp = temp
self.hard = hard
def __init__(self,
vis_dim,
hid_dim1,
hid_dim2,
w1=None,
w2=None,
vis_b=None,
hid_b1=None,
hid_b2=None):
self.vis_dim = vis_dim
self.hid_dim1 = hid_dim1
self.hid_dim2 = hid_dim2
if w1 is not None:
self.w1 = w1
else:
self.w1 = tfe.Variable(tf_xavier_init(self.vis_dim,
self.hid_dim1,
const=4.0),
name='dbm.w1')
if w2 is not None:
self.w2 = w2
else:
self.w2 = tfe.Variable(tf_xavier_init(self.hid_dim1,
self.hid_dim2,
const=4.0),
name='dbm.w2')
if hid_b1 is not None:
self.hid_b1 = hid_b1
else:
self.hid_b1 = tfe.Variable(tf.zeros([self.hid_dim1]),
dtype=tf.float32,
name='dbm.hid_b1')
if hid_b2 is not None:
self.hid_b2 = hid_b2
else:
self.hid_b2 = tfe.Variable(tf.zeros([self.hid_dim2]),
dtype=tf.float32,
name='dbm.hid_b2')
if vis_b is not None:
self.vis_b = vis_b
else:
self.vis_b = tfe.Variable(tf.zeros([self.vis_dim]),
dtype=tf.float32,
name='dbm.vis_b')
def __init__(self,
dim_z2h,
dim_h2v,
activation_fn=tf.nn.relu,
temp=.1,
hard=False,
has_h=True):
self.dim_z2h = dim_z2h
self.w_z2h, self.b_z2h = [], []
if activation_fn == tf.nn.relu:
const = 1.0
else:
const = 4.0
if has_h:
assert len(self.dim_z2h) > 1
for d1, d2, i in zip(dim_z2h[:-1], dim_z2h[1:],
xrange(1, len(self.dim_z2h))):
self.w_z2h.append(
tfe.Variable(tf_xavier_init(d1, d2, const=const),
name='sbn.w_z2h.' + str(i)))
self.b_z2h.append(
tfe.Variable(tf.zeros([d2]),
dtype=tf.float32,
name='sbn.b_z2h.' + str(i)))
self.dim_h2v = dim_h2v
self.w_h2v, self.b_h2v = [], []
assert len(self.dim_h2v) > 1
for d1, d2, i in zip(dim_h2v[:-1], dim_h2v[1:],
xrange(1, len(self.dim_h2v))):
self.w_h2v.append(
tfe.Variable(tf_xavier_init(d1, d2, const=const),
name='sbn.w_h2v.' + str(i)))
self.b_h2v.append(
tfe.Variable(tf.zeros([d2]),
dtype=tf.float32,
name='sbn.b_h2v.' + str(i)))
self.activation_fn = activation_fn
self.temp = temp
self.hard = hard
self.has_h = has_h # Turning it false to train a 2 layer sbn for debugging, otherwise always true
def __init__(self, dims, name, activation_fn=tf.nn.tanh):
self.dims = dims
self.ws, self.bs = [], []
self.activation_fn = activation_fn
if activation_fn == tf.nn.relu:
const = 1.0
else:
const = 4.0
assert len(self.dims) > 1
for d1, d2, i in zip(dims[:-2], dims[1:-1],
xrange(1,
len(self.dims) - 1)):
self.ws.append(
tfe.Variable(tf_xavier_init(d1, d2, const=const),
name='enc.' + name + '.w.' + str(i)))
self.bs.append(
tfe.Variable(tf.zeros([d2]),
dtype=tf.float32,
name='enc.' + name + '.b.' + str(i)))
self.ws.append(
tfe.Variable(tf_xavier_init(dims[-2], dims[-1], const=const),
name='enc.' + name + '.w.out.mu'))
self.bs.append(
tfe.Variable(tf.zeros([dims[-1]]),
dtype=tf.float32,
name='enc.' + name + '.b.out.mu'))
self.ws.append(
tfe.Variable(tf_xavier_init(dims[-2], dims[-1], const=const),
name='enc.' + name + '.w.out.logvar'))
self.bs.append(
tfe.Variable(tf.zeros([dims[-1]]),
dtype=tf.float32,
name='enc.' + name + '.b.out.logvar'))