def prior(name, y_onehot, hps):
with tf.variable_scope(name):
n_z = hps.top_shape[-1]
h = tf.zeros([tf.shape(y_onehot)[0]]+hps.top_shape[:2]+[2*n_z])
if hps.learntop:
h = Z.conv2d_zeros('p', h, 2*n_z)
if hps.ycond:
h += tf.reshape(Z.linear_zeros("y_emb", y_onehot,
2*n_z), [-1, 1, 1, 2 * n_z])
pz = Z.gaussian_diag(h[:, :, :, :n_z], h[:, :, :, n_z:])
def logp(z1):
objective = pz.logp(z1)
return objective
def sample(eps=None, eps_std=None):
if eps is not None:
# Already sampled eps. Don't use eps_std
z = pz.sample2(eps)
elif eps_std is not None:
# Sample with given eps_std
z = pz.sample2(pz.eps * tf.reshape(eps_std, [-1, 1, 1, 1]))
else:
# Sample normally
z = pz.sample
return z
def eps(z1):
return pz.get_eps(z1)
return logp, sample, eps
def prior(name, y_onehot, hps):
with tf.variable_scope(name):
n_z = hps.top_shape[-1]
h = tf.zeros([tf.shape(y_onehot)[0]]+hps.top_shape[:2]+[2*n_z])
if hps.learntop:
h = Z.conv2d_zeros('p', h, 2*n_z)
if hps.ycond:
h += tf.reshape(Z.linear_zeros("y_emb", y_onehot,
2*n_z), [-1, 1, 1, 2 * n_z])
pz = Z.gaussian_diag(h[:, :, :, :n_z], h[:, :, :, n_z:])
def logp(z1):
objective = pz.logp(z1)
return objective
def sample(eps=None, eps_std=None):
if eps is not None:
# Already sampled eps. Don't use eps_std
z = pz.sample2(eps)
elif eps_std is not None:
# Sample with given eps_std
z = pz.sample2(pz.eps * tf.reshape(eps_std, [-1, 1, 1, 1]))
else:
# Sample normally
z = pz.sample
return z
def eps(z1):
return pz.get_eps(z1)
return logp, sample, eps
def split2d_prior(z):
n_z2 = int(z.get_shape()[3])
n_z1 = n_z2
h = Z.conv2d_zeros("conv", z, 2 * n_z1)
mean = h[:, :, :, 0::2]
logs = h[:, :, :, 1::2]
return Z.gaussian_diag(mean, logs)
def split2d_prior(z):
n_z2 = int(z.get_shape()[3])
n_z1 = n_z2
h = Z.conv2d_zeros("conv", z, 2 * n_z1)
mean = h[:, :, :, 0::2]
logs = h[:, :, :, 1::2]
return Z.gaussian_diag(mean, logs)
def split2d_prior(z, hps):
shape = Z.int_shape(z)
n_z2 = int(z.get_shape()[3])
n_z1 = n_z2
h = tf.zeros([tf.shape(z)[0]] + shape[1:3] + [2 * n_z1])
if hps.learnprior:
h = Z.conv2d_zeros("conv", z, 2 * n_z1)
mean = h[:, :, :, 0::2]
logs = h[:, :, :, 1::2]
return Z.gaussian_diag(mean, logs)
def split3d_prior(y, shape, z_prior, level):
n_z = shape[-1]
h = tf.zeros([shape[0]] + shape[1:4] + [2 * n_z])
mean = h[:, :, :, :, :n_z]
logsd = h[:, :, :, :, n_z:]
if y is not None:
temp_v = Z.linear_zeros("y_emb", y, n_z)
mean += tf.reshape(temp_v, [-1, 1, 1, 1, n_z])
if z_prior is not None:
mean, logsd = Z.condFun(mean, logsd, z_prior, level)
# n_z2 = int(z.get_shape()[3])
# n_z1 = n_z2
# h = Z.conv2d_zeros("conv", z, 2 * n_z1)
#
# mean = h[:, :, :, 0::2]
# logs = h[:, :, :, 1::2]
return Z.gaussian_diag(mean, logsd)
def _create_prior(self, z):
'''Create a unit normal Gaussian object with same shape as z.'''
mu = tf.zeros_like(z, dtype='float32')
logs = tf.zeros_like(z, dtype='float32')
return Z.gaussian_diag(mu, logs)
