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。

    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

（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

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)

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)

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]

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), "\n"
12     print "Final w1 is: \n", sess.run(w1)

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), "\n"
17     print "Final w1 is: \n", sess.run(w1)

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 ]]