def call(self, inputs):
net = self.layer(inputs)
logits, loc, unconstrained_scale = tf.split(net, 3, axis=-1)
scale = tf.nn.softplus(unconstrained_scale) + tf.keras.backend.epsilon()
return ed.MixtureSameFamily(
mixture_distribution=ed.Categorical(logits=logits).distribution,
components_distribution=ed.Logistic(loc=loc, scale=scale).distribution)
def model(NHIDDEN, x):
W = tf.Variable(
tf.random_normal([NPixels * NPixels, NHIDDEN],
0.0,
0.1,
dtype=tf.float32,
seed=1))
b = tf.Variable(
tf.random_normal([1, NHIDDEN], 0.0, 0.1, dtype=tf.float32, seed=1))
W_out = tf.Variable(
tf.random_normal([NHIDDEN, NLabels],
0.0,
0.1,
dtype=tf.float32,
seed=1))
b_out = tf.Variable(
tf.random_normal([1, NLabels], 0.0, 0.1, dtype=tf.float32, seed=1))
hidden_layer = tf.nn.tanh(tf.matmul(x, W) + b)
out = tf.matmul(hidden_layer, W_out) + b_out
y = ed.Categorical(logits=out, name="y")
###Prior
normal = tf.distributions.Normal(0., 1.)
logpiror = tf.math.reduce_sum(normal.log_prob(W)) + \
tf.math.reduce_sum(normal.log_prob(b)) + \
tf.math.reduce_sum(normal.log_prob(W_out)) + \
tf.math.reduce_sum(normal.log_prob(b_out))
return x, y, logpiror
def model(NHIDDEN, x):
W = ed.Normal(loc=tf.zeros([NPixels * NPixels, NHIDDEN]),
scale=1.,
name="W")
b = ed.Normal(loc=tf.zeros([1, NHIDDEN]), scale=1., name="b")
W_out = ed.Normal(loc=tf.zeros([NHIDDEN, NLabels]),
scale=1.,
name="W_out")
b_out = ed.Normal(loc=tf.zeros([1, NLabels]), scale=1., name="b_out")
hidden_layer = tf.nn.relu(tf.matmul(x, W) + b)
out = tf.matmul(hidden_layer, W_out) + b_out
y = ed.Categorical(logits=out, name="y")
return W, b, W_out, b_out, x, y
def model(NHIDDEN, x):
W = ed.Normal(loc=tf.zeros([NPixels, NHIDDEN]), scale=1., name="W")
b = ed.Normal(loc=tf.zeros([1, NHIDDEN]), scale=1., name="b")
W_out = ed.Normal(loc=tf.zeros([NHIDDEN, 2 * NPixels]),
scale=1.,
name="W_out")
b_out = ed.Normal(loc=tf.zeros([1, 2 * NPixels]),
scale=1.,
name="b_out")
hidden_layer = tf.nn.relu(tf.matmul(x, W) + b)
out = tf.matmul(hidden_layer, W_out) + b_out
y = ed.Categorical(logits=tf.reshape(
out, [tf.shape(x_batch)[0], NPixels, 2]),
name="y")
return W, b, W_out, b_out, x, y
import tensorflow as tf
from tensorflow_probability import edward2 as ed
N = 10000
car_door = ed.Categorical(probs=tf.constant([1. / 3., 1. / 3., 1. / 3.]),
sample_shape=N,
name='car_door')
picked_door = ed.Categorical(probs=tf.constant([1. / 3., 1. / 3., 1. / 3.]),
sample_shape=N,
name='picked_door')
preference = ed.Bernoulli(probs=tf.constant(0.5),
sample_shape=N,
name='preference')
host_choice = tf.where(tf.not_equal(car_door, picked_door),
3 - car_door - picked_door,
tf.where(
tf.equal(car_door, 2 * tf.ones(N, dtype=tf.int32)),
preference,
tf.where(
tf.equal(car_door, tf.ones(N, dtype=tf.int32)),
2 * preference, 1 + preference)),
name='host_choice')
#changed_door = 3 - host_choice - picked_door
changed_door = tf.subtract(tf.subtract(3, host_choice),
picked_door,
name='changed_door')
writer = tf.summary.FileWriter('./graphs_tfp', tf.get_default_graph())
