def encode(self, x, n_iters=100):
y_ = dgm._forward_pass_Cat(x, self.Qx_y, self.NUM_HIDDEN,
self.NONLINEARITY, self.batchnorm,
self.phase)
y_ = tf.one_hot(tf.argmax(y_, axis=1), self.NUM_CLASSES)
_, _, a = self._sample_a(x, y_, self.Z_SAMPLES)
a_samps = tf.expand_dims(a, axis=2)
_, _, z = self._sample_Z(x, y_, a, self.Z_SAMPLES)
z_samps = tf.expand_dims(z, axis=2)
for i in range(n_iters):
h = tf.concat([x, z], axis=1)
y_ = dgm._forward_pass_Cat(h, self.Pzx_y, self.NUM_HIDDEN,
self.NONLINEARITY, self.batchnorm,
self.phase)
y_ = tf.one_hot(tf.argmax(y_, axis=1), self.NUM_CLASSES)
_, _, a = self._sample_a(x, y_, self.Z_SAMPLES)
_, _, z = self._sample_Z(x, y_, a, self.Z_SAMPLES)
a_samps = tf.concat([a_samps, tf.expand_dims(a, axis=2)], axis=2)
z_samps = tf.concat([z_samps, tf.expand_dims(z, axis=2)], axis=2)
return tf.reduce_mean(z_samps, axis=2)
def _unlabeled_loss(self, x):
""" Compute necessary terms for unlabeled loss (per data point) """
weights = dgm._forward_pass_Cat(x, self.Qx_y, self.NUM_HIDDEN,
self.NONLINEARITY, self.batchnorm,
self.phase)
EL_l = 0
for i in range(self.NUM_CLASSES):
y = self._generate_class(i, x.get_shape()[0])
EL_l += tf.multiply(weights[:, i], self._labeled_loss(x, y))
ent_qy = -tf.reduce_sum(tf.multiply(weights, tf.log(1e-10 + weights)),
axis=1)
return EL_l + ent_qy, EL_l, ent_qy
def _sample_xy(self, n_samples=int(1e3)):
saver = tf.train.Saver()
with tf.Session() as session:
ckpt = tf.train.get_checkpoint_state(self.ckpt_dir)
saver.restore(session, ckpt.model_checkpoint_path)
self.phase=False
z_ = np.random.normal(size=(n_samples, self.Z_DIM)).astype('float32')
if self.TYPE_PX=='Gaussian':
x_ = dgm._forward_pass_Gauss(z_, self.Pz_x, self.NUM_HIDDEN, self.NONLINEARITY, self.batchnorm, self.phase)[0]
else:
x_ = dgm._forward_pass_Bernoulli(z_, self.Pz_x, self.NUM_HIDDEN, self.NONLINEARITY, self.batchnorm, self.phase)
h = tf.concat([x_,z_], axis=1)
y_ = dgm._forward_pass_Cat(h, self.Pzx_y, self.NUM_HIDDEN, self.NONLINEARITY, self.batchnorm, self.phase)
x,y = session.run([x_,y_])
return x,y
def _predict_condition_W(self, x, n_iters=20):
y_ = dgm._forward_pass_Cat(x, self.Qx_y, self.NUM_HIDDEN,
self.NONLINEARITY, self.batchnorm,
self.phase)
yq = y_
y_ = tf.one_hot(tf.argmax(y_, axis=1), self.NUM_CLASSES)
y_samps = tf.expand_dims(y_, axis=2)
for i in range(n_iters):
_, _, z = self._sample_Z(x, y_, self.Z_SAMPLES)
h = tf.concat([x, z], axis=1)
y_ = dgm._forward_pass_Cat_bnn(h, self.Wtilde, self.Pzx_y,
self.NUM_HIDDEN, self.NONLINEARITY,
self.batchnorm, self.phase)
y_samps = tf.concat([y_samps, tf.expand_dims(y_, axis=2)], axis=2)
y_ = tf.one_hot(tf.argmax(y_, axis=1), self.NUM_CLASSES)
return tf.reduce_mean(y_samps, axis=2), yq
def predict(self, x):
return (dgm._forward_pass_Cat(x, self.Qx_y, self.NUM_HIDDEN,
self.NONLINEARITY, self.batchnorm,
self.phase), )