Cross Entropy in TensorFlow
As with the softmax function, TensorFlow has a function to do the cross entropy calculations for us.
tf.reduce_sum()tf.log()Reduce Sum
x = tf.reduce_sum([1, 2, 3, 4, 5]) # 15
The tf.reduce_sum() function takes an array of numbers and sums them together.
Natural Log
x = tf.log(100.0) # 4.60517
This function does exactly what you would expect it to do. tf.log() takes the natural log of a number.
Quiz
Print the cross entropy using softmax_data and one_hot_encod_label.
(Alternative link for users in China.)
quiz.py
# Solution is available in the other "solution.py" tab import tensorflow as tf softmax_data = [0.7, 0.2, 0.1] one_hot_data = [1.0, 0.0, 0.0] softmax = tf.placeholder(tf.float32) one_hot = tf.placeholder(tf.float32) # TODO: Print cross entropy from session
solution.py
# Quiz Solution
# Note: You can't run code in this tab
import tensorflow as tf
softmax_data = [0.7, 0.2, 0.1]
one_hot_data = [1.0, 0.0, 0.0]
softmax = tf.placeholder(tf.float32)
one_hot = tf.placeholder(tf.float32)
# ToDo: Print cross entropy from session
cross_entropy = -tf.reduce_sum(tf.multiply(one_hot, tf.log(softmax)))
with tf.Session() as sess:
print(sess.run(cross_entropy, feed_dict={softmax: softmax_data, one_hot: one_hot_data}))
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