In [1]:

    

import tensorflow as tf
import numpy as np
import pandas as pd


    

In [17]:

    

logit_list = [0.1, 0.5, 0.4]
label_list = [0]


    

In [18]:

    

logits = tf.Variable(np.array([logit_list]), dtype=tf.float32, name="logits")
labels = tf.Variable(np.array(label_list), dtype=tf.int32, name="labels")


    

In [19]:

    

result = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels, logits=logits, name='result')


    

In [20]:

    

init = tf.global_variables_initializer()


    

In [21]:

    

with tf.Session() as sess:
    init.run()
    ret = sess.run(result)
    print(ret)


    

    




[ 1.34591067]

In [22]:

    

# Next, I am trying to implement softmax cross entropy my self
# reference: https://deepnotes.io/softmax-crossentropy
# reference: https://ml-cheatsheet.readthedocs.io/en/latest/loss_functions.html#cross-entropy


    

In [23]:

    

def softmax(logits):
    exps = np.exp(logits)
    return exps / np.sum(exps)


    

In [24]:

    

print(softmax(logit_list))


    

    




[ 0.26030255  0.38832577  0.35137169]

In [13]:

    

def cross_entropy(label, y_hat):
    return  -np.log(y_hat[label])


    

In [25]:

    

y_hat = np.array(softmax(logit_list))


    

In [26]:

    

print(cross_entropy(label_list[0], y_hat=y_hat))


    

    




1.34591068334