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
# -*- coding: utf-8 -*-
# version：python 3.5
import tensorflow as tf
from numpy.random import RandomState batch_size = 8
x = tf.placeholder(tf.float32, shape=(None, 2), name="x-input")
y_ = tf.placeholder(tf.float32, shape=(None, 1), name='y-input')
w1= tf.Variable(tf.random_normal([2, 1], stddev=1, seed=1))
y = tf.matmul(x, w1)
2、设置自定义的损失函数
# 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。
loss_less = 10
loss_more = 1
loss = tf.reduce_sum(tf.where(tf.greater(y, y_), (y - y_) * loss_more, (y_ - y) * loss_less))
train_step = tf.train.AdamOptimizer(0.001).minimize(loss)
3、生成模拟数据集
rdm = RandomState(1)
X = rdm.rand(128,2)
Y = [[x1+x2+rdm.rand()/10.0-0.05] for (x1, x2) in X]
4、训练模型
with tf.Session() as sess:
init_op = tf.global_variables_initializer()
sess.run(init_op)
STEPS = 5000
for i in range(STEPS):
start = (i*batch_size) % 128
end = (i*batch_size) % 128 + batch_size
sess.run(train_step, feed_dict={x: X[start:end], y_: Y[start:end]})
if i % 1000 == 0:
print("After %d training step(s), w1 is: " % (i))
print sess.run(w1), "\n"
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
loss_less = 1
loss_more = 10
loss = tf.reduce_sum(tf.where(tf.greater(y, y_), (y - y_) * loss_more, (y_ - y) * loss_less))
train_step = tf.train.AdamOptimizer(0.001).minimize(loss) with tf.Session() as sess:
init_op = tf.global_variables_initializer()
sess.run(init_op)
STEPS = 5000
for i in range(STEPS):
start = (i*batch_size) % 128
end = (i*batch_size) % 128 + batch_size
sess.run(train_step, feed_dict={x: X[start:end], y_: Y[start:end]})
if i % 1000 == 0:
print("After %d training step(s), w1 is: " % (i))
print sess.run(w1), "\n"
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 ]]