tensorflow 笔记

佚名 6年前 (2019-05-16) 随笔 1135人围观抢沙发百度已收录

1. matMul

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


import tensorflow as tf
w1 = tf.Variable(tf.random_normal([2,3], stddev= 1, seed= 1))
w2 = tf.Variable(tf.random_normal([3,1], stddev= 1, seed= 1))

x = tf.constant([[0.7, 0.9]]) # 1×2
a = tf.matmul(x, w1) # 1×3
y = tf.matmul(a, w2) # 1×1

sess = tf.Session()

# w1 w2
sess.run(w1.initializer)
sess.run(w2.initializer)

sess.run(y)

print(y)

sess.close()

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2. eval 函数作用:

1.eval(): 将字符串string对象转化为有效的表达式参与求值运算返回计算结果
2.eval()也是启动计算的一种方式。基于Tensorflow的基本原理，首先需要定义图，然后计算图，其中计算图的函数常见的有run()函数，如sess.run()。同样eval()也是此类函数，
3.要注意的是，eval()只能用于tf.Tensor类对象，也就是有输出的Operation。对于没有输出的Operation, 可以用.run()或者Session.run()；Session.run()没有这个限制。

import tensorflow as tf

x = tf.Variable(3, name="x")
y = tf.Variable(4, name="y")
z = tf.Variable(4, name="z")
w = tf.Variable(4, name="w")
f = x * y * z + 3 - w

//单个初始化，变量增多，还真是个事
with tf.Session() as sess:
    x.initializer.run()
    y.initializer.run()
    z.initializer.run()
    w.initializer.run()
    print(x)
    print(y)
    print(z)
    print(w)
   //f fuction eval 方式和js的eval一样，作为方法函数执行
    result = f.eval()
 print(result)

3. 矩阵初始化 :

w1 = tf.constant([[1,1,1],[2,2,2]],tf.float32)
w2 = tf.constant([[3],[3],[3]],tf.float32)

4. 使用GPU计算

g = tf.Graph()
with g.device('/gpu:0'):
    a = tf.matmul(x, w1) # 1×3
    y = tf.matmul(a, w2) # 1×1

5.变量

weights = tf.Variable(tf.random_normal([2,3], stddev = 2))// 产生2行3列  标准差为2 的变量
biases = tf.Variable(tf.zeros([3]) //初值为0 , 含有3个元素的变量

//使用w2变量初值来初始化w3
w2 = tf.Variable(weights.initialized_value()) 
w3 = tf.Varibale(weights.initialized_value() * 2.0)

6 tensorflow 训练神经网络

# 定义损失函数
cross_entropy = -tf.reduce_mean(y_ * tf/log(tf.clip_by_value(y, 1e-10, 1.0)))   //  -y_ln(y)
# 定义学习率
learning_rate = 0.01
#定义反响传播算法
train_step = tf.train.AdamOptimizer(learning_rate).minimize(cross_entropy)  //使损失函数最小

7. 完整的神经网络训练程序

# -*- coding: utf-8 -*-
"""
Created on Thu Apr 12 15:58:38 2018

@author: 无尾君
"""

import tensorflow as tf
from numpy.random import RandomState

batch_size = 8

w1 = tf.Variable(tf.random_normal([2,3], stddev= 1, seed= 1))
w2 = tf.Variable(tf.random_normal([3,1], stddev= 1, seed= 1))

x = tf.placeholder(tf.float32, shape= (None,2), name= 'x-input')
y_ = tf.placeholder(tf.float32, shape= (None,1), name= 'y-input')

a = tf.matmul(x, w1)
y = tf.matmul(a, w2)

cross_entropy = -tf.reduce_mean(y_ * tf.log(tf.clip_by_value(y, 1e-10, 1.0)))
train_step = tf.train.AdamOptimizer(0.001).minimize(cross_entropy)

rdm = RandomState(1)
dataset_size = 128
X = rdm.rand(dataset_size, 2)
Y = [[int(x1+ x2 < 1)] for (x1, x2) in X]

with tf.Session() as sess:
    tf.global_variables_initializer().run()
    print(sess.run(w1))
    print(sess.run(w2))

    STEPS = 5000
    for i in range(STEPS):
        start = (i * batch_size) % dataset_size
        end = min(start + batch_size, dataset_size)

        sess.run(train_step, feed_dict = {x: X[start:end], y_: Y[start:end]})
        if i % 1000 ==0:
            total_cross_entropy = sess.run(cross_entropy, feed_dict = {x: X, y_: Y})
            print("After %d training step(s), cross entropy on all data is %g" % (i, total_cross_entropy))