tensorflow and tensorboard gradient search

gradient-board.py

import tensorflow as tf
import numpy as np
import os
import uuid

TENSORBOARD_PATH = "/tmp/tensorboard-switchpoint"
# tensorboard --logdir=/tmp/tensorboard-switchpoint

x1 = np.random.randn(35)-1
x2 = np.random.randn(35)*2 + 5
x_all = np.hstack([x1, x2])
len_x = len(x_all)
time_all = np.arange(1, len_x + 1)

mu1 = tf.Variable(0, name="mu1", dtype=tf.float32)
mu2 = tf.Variable(0, name = "mu2", dtype=tf.float32)
sigma1 = tf.Variable(2, name = "sigma1", dtype=tf.float32)
sigma2 = tf.Variable(2, name = "sigma2", dtype=tf.float32)
tau = tf.Variable(15, name = "tau", dtype=tf.float32)
switch = 1./(1+tf.exp(tf.pow(time_all - tau, 1)))

mu = switch*mu1 + (1-switch)*mu2
sigma = switch*sigma1 + (1-switch)*sigma2

likelihood_arr = tf.log(tf.sqrt(1/(2*np.pi*tf.pow(sigma, 2)))) - tf.pow(x_all - mu, 2)/(2*tf.pow(sigma, 2))
total_likelihood = tf.reduce_sum(likelihood_arr, name="total_likelihood")

optimizer = tf.train.AdamOptimizer()
opt_task = optimizer.minimize(-total_likelihood)
init = tf.global_variables_initializer()

tf.summary.scalar("mu1", mu1)
tf.summary.scalar("mu2", mu2)
tf.summary.scalar("sigma1", sigma1)
tf.summary.scalar("sigma2", sigma2)
tf.summary.scalar("tau", tau)
tf.summary.scalar("likelihood", total_likelihood)
merged_summary_op = tf.summary.merge_all()

with tf.Session() as sess:
    sess.run(init)
    print("these variables should be trainable: {}".format([_.name for _ in tf.trainable_variables()]))
    uniq_id = os.path.join(TENSORBOARD_PATH, "switchpoint-adam-" + uuid.uuid1().__str__()[:4])
    summary_writer = tf.summary.FileWriter(uniq_id, graph=tf.get_default_graph())
    for step in range(15000):
        lik, opt, summary = sess.run([total_likelihood, opt_task, merged_summary_op])
        if step % 100 == 0:
            variables = {_.name:_.eval() for _ in [total_likelihood]}
            summary_writer.add_summary(summary, step)
            print("i{}: {}".format(str(step).zfill(5), variables))