tensorflow 激活函数 elu exponential

#!/usr/bin/env python3   
# -*- coding:UTF-8 -*-  
import tensorflow as tf
import numpy as np
from tensorflow.python import debug as tf_debug

#tf.keras.backend.set_session(tf_debug.LocalCLIDebugWrapperSession(tf.Session()))
import scipy.misc

x_train = np.array([[1,1],[0,0],[0,1],[1,0]]);
y_train = np.array([0,0,1,1])
model = tf.keras.models.Sequential([
  tf.keras.layers.Dense(2, input_shape=(2,),activation='sigmoid',use_bias =True),
 tf.keras.layers.Dense(3, activation='relu'),
 tf.keras.layers.Dense(3, activation='elu'),
 tf.keras.layers.Dense(3, activation='softplus'),
 tf.keras.layers.Dense(3, activation='linear'),
 tf.keras.layers.Dense(3, activation='exponential'),
 tf.keras.layers.Dense(3, activation='selu'),
  tf.keras.layers.Dense(1, activation='tanh',use_bias =True)
])
model.compile(optimizer='adam',
              loss='mean_squared_error',
              metrics=['binary_accuracy'])

model.fit(x_train, y_train, epochs=5000)
model.save('/home/a/my_minis.h5')
new_model = tf.keras.models.load_model('/home/a/my_minis.h5')
print(new_model.predict(x_train))
print(new_model.predict_classes(x_train))

运行结果：

 loss: 3.1563e-05 - binary_accuracy: 1.0000
Epoch 5000/5000
4/4 [==============================] - 0s 808us/sample - loss: 3.1545e-05 - binary_accuracy: 1.0000
[[2.9830632e-04]
 [1.9936262e-04]
 [9.9207312e-01]
 [9.9205357e-01]]
[[0]
 [0]
 [1]
 [1]]
--------------------------------------------------------------

https://www.tensorflow.org/api_docs/python/tf/keras/activations

tf.keras.activations.elu
tf.keras.activations.elu(
    x,
    alpha=1.0
)

import tensorflow as tf
import os
from math import *
print('tf versions',tf.__version__)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

e1=tf.keras.activations.elu(-1.3)
e2=tf.keras.activations.elu(1.3)
with tf.Session():
  re1 = e1.eval()
  re2 = e2.eval()

print(re1,re2, exp(-1.3)-1 )

import tensorflow as tf
import os
from math import *
print('tf versions',tf.__version__)#tf versions 1.13.1
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

e1=tf.keras.activations.exponential(1.3)
e2=tf.keras.activations.exponential(0.1)
with tf.Session():
  re1 = e1.eval()
  re2 = e2.eval()

print(re1,re2, exp(1.3),exp(0.1) )#3.6692965 1.105171 3.6692966676192444 1.1051709180756477

import tensorflow as tf
import os
from math import *
print('tf versions',tf.__version__)#tf versions 1.13.1
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

e1=tf.keras.activations.linear(1.3)
e2=tf.keras.activations.linear(0.1)
print(e1,e2)#1.3 0.1

relu

 

代码托管在github

https://github.com/sofiathefirst/AIcode