python实现softmax函数、sigmoid函数、 softmax 交叉熵loss函数、sigmoid 交叉熵loss函数

编辑器：jupyter notebook
Tensorflow2.0
计算用了两种方法numpy&tensorflow，建议tensorflow

import tensorflow as tf
import numpy as np

实现softmax函数

#np
def softmax(x):x_exp = np.exp(x)x_sum = np.sum(x_exp, axis=1, keepdims=True)prob_x = x_exp / x_sumreturn prob_xtest_data = np.random.normal(size=[10, 5])
(softmax(test_data) - tf.nn.softmax(test_data, axis=-1).numpy())**2

#tf
def softmaxF(x):x_exp = tf.math.exp(x)x_sum = tf.reduce_sum(x_exp, axis=1, keepdims=True)prob_x = x_exp / x_sumreturn prob_x
(softmaxF(test_data).numpy() - tf.nn.softmax(test_data, axis=-1).numpy())**2 <0.0001

实现sigmoid函数

def sigmoid(x):x =1 + (1 / np.exp(x))prob_x = 1 / xreturn prob_xtest_data = np.random.normal(size=[10, 5])
(sigmoid(test_data) - tf.nn.sigmoid(test_data).numpy())**2 < 0.0001

#tf
#tf
def sigmoidF(x):prob_x =1/(1+ (1/tf.math.exp(x)))return prob_xtest_data = np.random.normal(size=[10, 5])
# print(tf.nn.softmax(test_data, axis=-1).numpy(),'T')
(sigmoidF(test_data).numpy() - tf.nn.sigmoid(test_data).numpy())**2 < 0.0001

实现softmax交叉熵loss函数

def softmax_ce(x, label):loss = -np.sum(np.nan_to_num(label*np.log(x)),axis=1) return losstest_data = np.random.normal(size=[10, 5])
prob = tf.nn.softmax(test_data)
label = np.zeros_like(test_data)
label[np.arange(10), np.random.randint(0, 5, size=10)]=1.
((tf.nn.softmax_cross_entropy_with_logits(label, test_data)- softmax_ce(prob, label))**2 < 0.0001).numpy()

#tf
def softmax_ceF(x, label):
# epsilon = 1e-12 losses = -tf.reduce_sum(label*tf.math.log(x),axis=1)
# losses = -tf.reduce_mean(label*tf.math.log(x+1e-12),axis=1)loss = tf.reduce_mean(losses)print(loss,'F')return losstest_data = np.random.normal(size=[10, 5])
prob = tf.nn.softmax(test_data)
label = np.zeros_like(test_data)
label[np.arange(10), np.random.randint(0, 5, size=10)]=1.
print(tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(label, test_data)),'T')((tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(label, test_data))- softmax_ceF(prob, label))**2 < 0.0001).numpy()

实现sigmoid交叉熵loss函数

def sigmoid_ce(x, label):loss = -np.sum(np.nan_to_num(label*np.log(x)+(1-label)*np.log(1-x)))return losstest_data = np.random.normal(size=[10])
prob = tf.nn.sigmoid(test_data)
label = np.random.randint(0, 2, 10).astype(test_data.dtype)
a = tf.nn.sigmoid_cross_entropy_with_logits(label, test_data).numpy()
((np.sum(a)- sigmoid_ce(prob, label))**2 < 0.0001)

#tf
def sigmoid_ceF(x, label):losses = -tf.reduce_sum(label*tf.math.log(x)+(1.-label)*tf.math.log(1-x))/len(x)loss = tf.reduce_mean(losses)print(loss,'F')return losstest_data = np.random.normal(size=[10])
prob = tf.nn.sigmoid(test_data)
label = np.random.randint(0, 2, 10).astype(test_data.dtype)
print(tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(label, test_data)),'T')((tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(label, test_data))- sigmoid_ceF(prob, label))**2 < 0.0001).numpy()