I can run this example from here:
mu = [1, 2, 3.]
diag_stdev = [4, 5, 6.]
dist = tf.contrib.distributions.MultivariateNormalDiag(mu, diag_stdev)
dist.pdf([-1., 0, 1])
but when I substitute the last line for dist.cdf([-1., 0, 1]) I get a not implemented error:
NotImplementedError: log_cdf is not implemented
Can anybody suggest a workaround for the time being at least?
Based on the solutions in here and here, I've implemented the following solution:
import tensorflow as tf
import numpy as np
from scipy.stats import mvn
def py_func(func, inp, Tout, stateful=True, name=None, grad=None):
# Need to generate a unique name to avoid duplicates:
rnd_name = 'PyFuncGrad' + str(np.random.randint(0, 1E+10))
tf.RegisterGradient(rnd_name)(grad)
g = tf.get_default_graph()
with g.gradient_override_map({"PyFunc": rnd_name}):
return tf.py_func(func, inp, Tout, stateful=stateful, name=name)
def np_cdf(mean, diag_sigma, value, name=None):
low = np.array([-30, -30])
cdf = list()
for variables in zip(value, mean, diag_sigma):
S = np.diag(variables[2])
p, _ = mvn.mvnun(low,variables[0],variables[1],S)
cdf.append(p)
cdfs = np.asarray(cdf, dtype=np.float32).reshape([-1,1])
return cdfs
def cdf_gradient(op, grad):
mu = op.inputs[0]
diag_sigma = op.inputs[1]
value = op.inputs[2]
dist = tf.contrib.distributions.MultivariateNormalDiag(mu, diag_sigma)
pdf = dist.pdf(value)
dc_dv = tf.inv(diag_sigma) * pdf
dc_dm = -1 * dc_dv
dc_ds = tf.div(value-mu,tf.square(diag_sigma)+1e-6) * pdf
return grad * dc_dm, grad * dc_ds, grad * dc_dv
def tf_cdf(mean, diag_sigma, value, name=None):
with tf.name_scope(name, "MyCDF", [mean, diag_sigma, value]) as name:
cdf = py_func(np_cdf,
[mean, diag_sigma, value],
[tf.float32],
name=name,
grad=cdf_gradient) # <-- here's the call to the gradient
return cdf[0]