Created
March 9, 2017 16:13
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tensorflow layer example
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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) |
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.
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It was very helpful for me, thanks!