#-*- coding:utf-8 -*-

import tensorflow as tf

sess=tf.Session()

#整流水线单元relu

print sess.run(tf.nn.relu([-3.,3,10.]))

#抵消relu激励函数的线性增长部分

print sess.run(tf.nn.relu6([-3.,3.,10.]))

#segmoid函数：1/(1+exp(-x))

print sess.run(tf.nn.sigmoid([-1.,0.,1.]))

#双面正切segmoid：(exp(x)-exp(-x))/(exp(x)+exp(-x))

print sess.run(tf.nn.tanh([-1.,0.,1.]))

#softsign:x/(abs(x)+1)

print sess.run(tf.nn.softsign([-1.,0.,1.]))

#softplus:log(exp(x)+1)

print sess.run(tf.nn.softplus([-1.,0.,1.]))

#ELU激励函数：(exp(-x)+1) if x<0 else x

print sess.run(tf.nn.elu([-1.,0.,1.]))

#-*- coding:utf-8 -*-

from sklearn import datasets

#加载鸢尾花卉数据集:

#                  150数据集

#                  每个数据集50个样本

iris=datasets.load_iris()

print len(iris.data)

print len(iris.target)

print iris.target[0]

print set(iris.target)

# #加载波士顿房价，506个放假样本，14个特征值

import requests

housing_url='https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data'

housing_header=['CRIM','ZN','INDUS','CHAS','NOX','RM','AGE','DIS','RAD','TAX','PTRATIO','B','LSTAT','MEDV0']

housing_file=requests.get(housing_url)

print housing_file.text

housing_data=[[float(x) for x in y.split(' ') if len(x)>=1] for y in housing_file.text.split('\n') if len(y)>=1]

print len(housing_data)

print len(housing_data[0])

#MNIST手写字体库

from tensorflow.examples.tutorials.mnist import input_data

mnist=input_data.read_data_sets("MNIST_data",one_hot=True)

print len(mnist.train.images)

print mnist.train.images

print len(mnist.test.images)

print mnist.test.images

print len(mnist.validation.images)

print mnist.validation.images

print mnist.train.labels[1,:]