# -*- coding: utf-8 -*-
"""
Created on Thu Jun  7 16:25:24 2018

@author: 吴八八
"""

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

# fake data
x = np.linspace(-5, 5, 200)     # x data, shape=(100, 1)

# following are popular activation functions
y_relu = tf.nn.relu(x)
y_sigmoid = tf.nn.sigmoid(x)
y_tanh = tf.nn.tanh(x)
y_softplus = tf.nn.softplus(x)
# y_softmax = tf.nn.softmax(x)  softmax is a special kind of activation function, it is about probability

sess = tf.Session()
y_relu, y_sigmoid, y_tanh, y_softplus = sess.run([y_relu, y_sigmoid, y_tanh, y_softplus])

# plt to visualize these activation function
plt.figure(1, figsize=(8, 6))
plt.subplot(221)
plt.plot(x, y_relu, c='red', label='relu')
plt.ylim((-1, 5))
plt.legend(loc='best')

plt.subplot(222)
plt.plot(x, y_sigmoid, c='red', label='sigmoid')
plt.ylim((-0.2, 1.2))
plt.legend(loc='best')

plt.subplot(223)
plt.plot(x, y_tanh, c='red', label='tanh')
plt.ylim((-1.2, 1.2))
plt.legend(loc='best')

plt.subplot(224)
plt.plot(x, y_softplus, c='red', label='softplus')
plt.ylim((-0.2, 6))
plt.legend(loc='best')

plt.show()


#plt 函数应用
#使用import导入模块matplotlib.pyplot，并简写成plt 使用import导入模块numpy，并简写成np
#使用np.linspace定义x：范围是(-1,1);个数是50. 仿真一维数据组(x ,y)表示曲线1
#使用plt.figure定义一个图像窗口. 使用plt.plot画(x ,y)曲线. 使用plt.show显示图像.






