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 _f_loss(x, y, is_training, reuse=False):
with tf.variable_scope('model', reuse=reuse):
y_onehot = tf.cast(tf.one_hot(y, hps.n_y, 1, 0), 'float32')
objective = tf.zeros_like(x, dtype='float32')[:, 0, 0, 0]
z = preprocess(x)
z = z + tf.random_uniform(tf.shape(z), 0, 1. / hps.n_bins)
objective += -np.log(hps.n_bins) * np.prod(Z.int_shape(z)[1:])
# Encode
z = Z.squeeze2d(z, 2) # > 16x16x12
z, objective = encoder(z, objective)
hps.top_shape = Z.int_shape(z)[1:]
# Prior
logp, _ = prior("prior", y_onehot, hps)
objective += logp(z)
# Generative loss
nobj = -objective
bits_x = nobj / (np.log(2.) * int(x.get_shape()[1]) * int(
x.get_shape()[2]) * int(x.get_shape()[3])) # bits per subpixel
# Predictive loss
if hps.weight_y > 0 and hps.ycond:
# Classification loss
h_y = tf.reduce_mean(z, axis=[1, 2])
y_logits = Z.linear_zeros("classifier", h_y, hps.n_y)
bits_y = tf.nn.softmax_cross_entropy_with_logits_v2(
labels=y_onehot, logits=y_logits) / np.log(2.)
# Classification accuracy
y_predicted = tf.argmax(y_logits, 1, output_type=tf.int32)
classification_error = 1 - \
tf.cast(tf.equal(y_predicted, y), tf.float32)
else:
bits_y = tf.zeros_like(bits_x)
classification_error = tf.ones_like(bits_x)
return bits_x, bits_y, classification_error
def _f_loss(x, y, is_training, reuse=False):
with tf.variable_scope('model', reuse=reuse):
y_onehot = tf.cast(tf.one_hot(y, hps.n_y, 1, 0), 'float32')
# Discrete -> Continuous
objective = tf.zeros_like(x, dtype='float32')[:, 0, 0, 0]
z = preprocess(x)
z = z + tf.random_uniform(tf.shape(z), 0, 1./hps.n_bins)
objective += - np.log(hps.n_bins) * np.prod(Z.int_shape(z)[1:])
# Encode
z = Z.squeeze2d(z, 2) # > 16x16x12
z, objective, _ = encoder(z, objective)
# Prior
hps.top_shape = Z.int_shape(z)[1:]
logp, _, _ = prior("prior", y_onehot, hps)
objective += logp(z)
# Generative loss
nobj = - objective
bits_x = nobj / (np.log(2.) * int(x.get_shape()[1]) * int(
x.get_shape()[2]) * int(x.get_shape()[3])) # bits per subpixel
# Predictive loss
if hps.weight_y > 0 and hps.ycond:
# Classification loss
h_y = tf.reduce_mean(z, axis=[1, 2])
y_logits = Z.linear_zeros("classifier", h_y, hps.n_y)
bits_y = tf.nn.softmax_cross_entropy_with_logits_v2(
labels=y_onehot, logits=y_logits) / np.log(2.)
# Classification accuracy
y_predicted = tf.argmax(y_logits, 1, output_type=tf.int32)
classification_error = 1 - \
tf.cast(tf.equal(y_predicted, y), tf.float32)
else:
bits_y = tf.zeros_like(bits_x)
classification_error = tf.ones_like(bits_x)
return bits_x, bits_y, classification_error
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)
