data = build_toy_dataset()
# Set up figure
fig = plt.figure(figsize=(8, 8), facecolor='white')
ax = fig.add_subplot(111, frameon=False)
plt.ion()
plt.show(block=False)
inference = ed.MFVI(model, variational, data)
inference.initialize(n_print=5)
sess = ed.get_session()
for t in range(600):
loss = inference.update()
if t % inference.n_print == 0:
print("iter {:d} loss {:.2f}".format(t, loss))
variational.print_params()
# Sample functions from variational model
mean, std = sess.run(
[variational.layers[0].m, variational.layers[0].s])
rs = np.random.RandomState(0)
zs = rs.randn(10, variational.num_vars) * std + mean
zs = tf.constant(zs, dtype=tf.float32)
inputs = np.linspace(-3, 3, num=400, dtype=np.float32)
x = tf.expand_dims(tf.constant(inputs), 1)
mus = tf.pack([model.mapping(x, z) for z in tf.unpack(zs)])
outputs = mus.eval()
# Get data
y, x = sess.run([data.data[:, 0], data.data[:, 1]])
data = build_toy_dataset()
# Set up figure
fig = plt.figure(figsize=(8, 8), facecolor='white')
ax = fig.add_subplot(111, frameon=False)
plt.ion()
plt.show(block=False)
inference = ed.MFVI(model, variational, data)
sess = inference.initialize(n_print=5)
# TODO it gets NaN's at iteration 608 and beyond
for t in range(600):
loss = inference.update(sess)
if t % inference.n_print == 0:
print("iter {:d} loss {:.2f}".format(t, loss))
variational.print_params(sess)
# Sample functions from variational model
mean, std = sess.run(
[variational.layers[0].m, variational.layers[0].s])
rs = np.random.RandomState(0)
zs = rs.randn(10, variational.num_vars) * std + mean
zs = tf.constant(zs, dtype=tf.float32)
inputs = np.linspace(-3, 3, num=400, dtype=np.float32)
x = tf.expand_dims(tf.constant(inputs), 1)
mus = tf.pack([model.mapping(x, z) for z in tf.unpack(zs)])
outputs = sess.run(mus)
# Get data
y, x = sess.run([data.data[:, 0], data.data[:, 1]])
data = build_toy_dataset()
# Set up figure
fig = plt.figure(figsize=(8,8), facecolor='white')
ax = fig.add_subplot(111, frameon=False)
plt.ion()
plt.show(block=False)
inference = ed.MFVI(model, variational, data)
inference.initialize(n_print=5)
sess = ed.get_session()
for t in range(600):
loss = inference.update()
if t % inference.n_print == 0:
print("iter {:d} loss {:.2f}".format(t, loss))
variational.print_params()
# Sample functions from variational model
mean, std = sess.run([variational.layers[0].m,
variational.layers[0].s])
rs = np.random.RandomState(0)
zs = rs.randn(10, variational.num_vars) * std + mean
zs = tf.constant(zs, dtype=tf.float32)
inputs = np.linspace(-3, 3, num=400, dtype=np.float32)
x = tf.expand_dims(tf.constant(inputs), 1)
mus = tf.pack([model.mapping(x, z) for z in tf.unpack(zs)])
outputs = mus.eval()
# Get data
y, x = sess.run([data.data[:, 0], data.data[:, 1]])
data = build_toy_dataset()
# Set up figure
fig = plt.figure(figsize=(8,8), facecolor='white')
ax = fig.add_subplot(111, frameon=False)
plt.ion()
plt.show(block=False)
inference = ed.MFVI(model, variational, data)
sess = inference.initialize(n_print=5)
# TODO it gets NaN's at iteration 608 and beyond
for t in range(600):
loss = inference.update(sess)
if t % inference.n_print == 0:
print("iter {:d} loss {:.2f}".format(t, loss))
variational.print_params(sess)
# Sample functions from variational model
mean, std = sess.run([variational.layers[0].m,
variational.layers[0].s])
rs = np.random.RandomState(0)
zs = rs.randn(10, variational.num_vars) * std + mean
zs = tf.constant(zs, dtype=tf.float32)
inputs = np.linspace(-3, 3, num=400, dtype=np.float32)
x = tf.expand_dims(tf.constant(inputs), 1)
mus = tf.pack([model.mapping(x, z) for z in tf.unpack(zs)])
outputs = sess.run(mus)
# Get data
y, x = sess.run([data.data[:, 0], data.data[:, 1]])
