px_logit = Dense(h2, 784, 'logit', reuse=reuse)
return zm, zv, px_logit
tf.reset_default_graph()
x = Placeholder((None, 784), 'x')
# binarize data and create a y "placeholder"
with tf.name_scope('x_binarized'):
xb = tf.cast(tf.greater(x, tf.random_uniform(tf.shape(x), 0, 1)),
tf.float32)
with tf.name_scope('y_'):
y_ = tf.fill(tf.pack([tf.shape(x)[0], k]), 0.0)
# propose distribution over y
qy_logit, qy = qy_graph(xb, k)
# for each proposed y, infer z and reconstruct x
z, zm, zv, zm_prior, zv_prior, px_logit = [[None] * k for i in xrange(6)]
for i in xrange(k):
with tf.name_scope('graphs/hot_at{:d}'.format(i)):
y = tf.add(y_, Constant(np.eye(k)[i], name='hot_at_{:d}'.format(i)))
z[i], zm[i], zv[i] = qz_graph(xb, y)
zm_prior[i], zv_prior[i], px_logit[i] = px_graph(z[i], y)
# Aggressive name scoping for pretty graph visualization :P
with tf.name_scope('loss'):
with tf.name_scope('neg_entropy'):
nent = -cross_entropy_with_logits(qy_logit, qy)
losses = [None] * k
for i in xrange(k):
px_logit = Dense(h2, 784, 'logit', reuse=reuse)
return px_logit
tf.reset_default_graph()
x = Placeholder((None, 784), name='x')
# binarize data and create a y "placeholder"
with tf.name_scope('x_binarized'):
xb = tf.cast(tf.greater(x, tf.random_uniform(tf.shape(x), 0, 1)),
tf.float32)
with tf.name_scope('y_'):
y_ = tf.fill(tf.stack([tf.shape(x)[0], 10]), 0.0)
# propose distribution over y
qy_logit, qy = qy_graph(xb)
# for each proposed y, infer z and reconstruct x
z, zm, zv, px_logit = [[None] * 10 for i in xrange(4)]
for i in xrange(10):
with tf.name_scope('graphs/hot_at{:d}'.format(i)):
y = tf.add(y_, Constant(np.eye(10)[i], name='hot_at_{:d}'.format(i)))
z[i], zm[i], zv[i] = qz_graph(xb, y)
px_logit[i] = px_graph(z[i], y)
# Aggressive name scoping for pretty graph visualization :P
with tf.name_scope('loss'):
with tf.name_scope('neg_entropy'):
nent = -cross_entropy_with_logits(qy_logit, qy)
losses = [None] * 10
for i in xrange(10):