深度学习之tensorflow （一）

一、TensorFlow简介

1.TensorFlow定义：

tensor ：张量，N维数组

Flow : 流，基于数据流图的计算

TensorFlow : 张量从图像的一端流动到另一端的计算过程，是将复杂的数据结构传输至人工智能神经网络中进行分析和处理的过程。

2. 工作模式：

图graphs表示计算任务，图中的节点称之为op(operation) ，一个 op可以获得0个或多个张量（tensor），通过创建会话（session）对象来执行计算，产生0个或多个tensor。

其工作模式分为两步：(1)define the computation graph

(2)run the graph (with data) in session

3. 特点：

（1）异步：一处写、一处读、一处训练

（2）全局：操作添加到全局的graph中，监控添加到全局的summary中，

参数/损失添加到全局的collection中

（3）符号式的：创建时没有具体，运行时才传入

二、代码

1 、定义神经网络的相关参数和变量

 1 # -*- coding: utf-8 -*-
 2 # version：python 3.5
 3 import tensorflow as tf
 4 from numpy.random import RandomState
 5 
 6 batch_size = 8
 7 x = tf.placeholder(tf.float32, shape=(None, 2), name="x-input")
 8 y_ = tf.placeholder(tf.float32, shape=(None, 1), name='y-input')
 9 w1= tf.Variable(tf.random_normal([2, 1], stddev=1, seed=1))
10 y = tf.matmul(x, w1)

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2、设置自定义的损失函数

1 # 定义损失函数使得预测少了的损失大，于是模型应该偏向多的方向预测。
2 loss_less = 10
3 loss_more = 1
4 loss = tf.reduce_sum(tf.where(tf.greater(y, y_), (y - y_) * loss_more, (y_ - y) * loss_less))
5 train_step = tf.train.AdamOptimizer(0.001).minimize(loss)

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3、生成模拟数据集

1 rdm = RandomState(1)
2 X = rdm.rand(128,2)
3 Y = [[x1+x2+rdm.rand()/10.0-0.05] for (x1, x2) in X]

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4、训练模型

 1 with tf.Session() as sess:
 2     init_op = tf.global_variables_initializer()
 3     sess.run(init_op)
 4     STEPS = 5000
 5     for i in range(STEPS):
 6         start = (i*batch_size) % 128
 7         end = (i*batch_size) % 128 + batch_size
 8         sess.run(train_step, feed_dict={x: X[start:end], y_: Y[start:end]})
 9         if i % 1000 == 0:
10             print("After %d training step(s), w1 is: " % (i))
11             print sess.run(w1), "
"
12     print "Final w1 is: 
", sess.run(w1)

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结果：

After 0 training step(s), w1 is: 
[[-0.81031823]
 [ 1.4855988 ]] 

After 1000 training step(s), w1 is: 
[[ 0.01247112]
 [ 2.1385448 ]] 

After 2000 training step(s), w1 is: 
[[ 0.45567414]
 [ 2.17060661]] 

After 3000 training step(s), w1 is: 
[[ 0.69968724]
 [ 1.8465308 ]] 

After 4000 training step(s), w1 is: 
[[ 0.89886665]
 [ 1.29736018]] 

Final w1 is: 
[[ 1.01934695]
 [ 1.04280889]]

5、重新定义损失函数，使得预测多了的损失大，于是模型应该偏向少的方向预测

 1 loss_less = 1
 2 loss_more = 10
 3 loss = tf.reduce_sum(tf.where(tf.greater(y, y_), (y - y_) * loss_more, (y_ - y) * loss_less))
 4 train_step = tf.train.AdamOptimizer(0.001).minimize(loss)
 5 
 6 with tf.Session() as sess:
 7     init_op = tf.global_variables_initializer()
 8     sess.run(init_op)
 9     STEPS = 5000
10     for i in range(STEPS):
11         start = (i*batch_size) % 128
12         end = (i*batch_size) % 128 + batch_size
13         sess.run(train_step, feed_dict={x: X[start:end], y_: Y[start:end]})
14         if i % 1000 == 0:
15             print("After %d training step(s), w1 is: " % (i))
16             print sess.run(w1), "
"
17     print "Final w1 is: 
", sess.run(w1)

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结果：

After 0 training step(s), w1 is: 
[[-0.81231821]
 [ 1.48359871]] 

After 1000 training step(s), w1 is: 
[[ 0.18643527]
 [ 1.07393336]] 

After 2000 training step(s), w1 is: 
[[ 0.95444274]
 [ 0.98088616]] 

After 3000 training step(s), w1 is: 
[[ 0.95574027]
 [ 0.9806633 ]] 

After 4000 training step(s), w1 is: 
[[ 0.95466018]
 [ 0.98135227]] 

Final w1 is: 
[[ 0.95525807]
 [ 0.9813394 ]]