tensorflow layer example

tf.py

import tensorflow as tf
import numpy as np
import uuid

x = tf.placeholder(shape=[None, 3], dtype=tf.float32)
nn = tf.layers.dense(x, 3, activation=tf.nn.sigmoid)
nn = tf.layers.dense(nn, 5, activation=tf.nn.sigmoid)
encoded = tf.layers.dense(nn, 2, activation=tf.nn.sigmoid)
nn = tf.layers.dense(encoded, 5, activation=tf.nn.sigmoid)
nn = tf.layers.dense(nn, 3, activation=tf.nn.sigmoid)

cost = tf.reduce_mean((nn - x)**2)
optimizer = tf.train.RMSPropOptimizer(0.01).minimize(cost)
init = tf.global_variables_initializer()

tf.summary.scalar("cost", cost)
merged_summary_op = tf.summary.merge_all()

with tf.Session() as sess:
    sess.run(init)
    uniq_id = "/tmp/tensorboard-layers-api/" + uuid.uuid1().__str__()[:6]
    summary_writer = tf.summary.FileWriter(uniq_id, graph=tf.get_default_graph())
    x_vals = np.random.normal(0, 1, (10000, 3))
    for step in range(10000):
        _, val, summary = sess.run([optimizer, cost, merged_summary_op],
                                   feed_dict={x: x_vals})
        if step % 5 == 0:
            print("step: {}, value: {}".format(step, val))
            summary_writer.add_summary(summary, step)

Hey there! This gist is great!
Could you please tell me how to use the intializer and the regularizer parameters of the tf.layers.dense functional interface?
I am mostly interested in passing my own functions as the initializer and regularizer

Brilliant gist, really helped me understand how to train tensorflow models without the use of the Estimator API or Keras.

It was very helpful for me, thanks!

Simpliest tutorial ive seen. Thanks

Elegant, simple & succint code. Thanks

Thanks 👍

Awesome!!!! Really so unique - didn't find anything this good anywhere! Thank you so much and congratulations on such a great post.

Those are kind words ... but you should realise this code is ... quite old by now.