Tensorflow 学习

1.Tensorflow linux环境下安装以及问题处理

 http://blog.csdn.net/levy_cui/article/details/51251095

2.Tensorflow基础:构造部分+执行部分

demo

import tensorflow as tf
# Create a variable.
w = tf.Variable([[0.5,1.0]])
x = tf.Variable([[2.0],[1.0]]) 
y = tf.matmul(w, x)  
#variables have to be explicitly initialized before you can run Ops
init_op = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init_op)
    print (y.eval())
# [[ 2.]]


# float32
tf.zeros([3, 4], 'int32') ==> [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
# 'tensor' is [[1, 2, 3], [4, 5, 6]]
tf.zeros_like(tensor) ==> [[0, 0, 0], [0, 0, 0]]
tf.ones([2, 3], int32) ==> [[1, 1, 1], [1, 1, 1]]
# 'tensor' is [[1, 2, 3], [4, 5, 6]]
tf.ones_like(tensor) ==> [[1, 1, 1], [1, 1, 1]]
# Constant 1-D Tensor populated with value list.
tensor = tf.constant([1, 2, 3, 4, 5, 6, 7]) => [1 2 3 4 5 6 7]
# Constant 2-D tensor populated with scalar value -1.
tensor = tf.constant(-1.0, shape=[2, 3]) => [[-1. -1. -1.]
                                              [-1. -1. -1.]]
tf.linspace(10.0, 12.0, 3, name="linspace") => [ 10.0  11.0  12.0]
# 'start' is 3
# 'limit' is 18
# 'delta' is 3
tf.range(start, limit, delta) ==> [3, 6, 9, 12, 15]



norm = tf.random_normal([2, 3], mean=-1, stddev=4)



# Shuffle the first dimension of a tensor
c = tf.constant([[1, 2], [3, 4], [5, 6]])
shuff = tf.random_shuffle(c)



# Each time we run these ops, different results are generated
sess = tf.Session()
print (sess.run(norm))
print (sess.run(shuff))

state = tf.Variable(0)
new_value = tf.add(state, tf.constant(1))
update = tf.assign(state, new_value)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    print(sess.run(state))    
    for _ in range(3):
        sess.run(update)


#tf.train.Saver
w = tf.Variable([[0.5,1.0]])
x = tf.Variable([[2.0],[1.0]])
y = tf.matmul(w, x)
init_op = tf.global_variables_initializer()
saver = tf.train.Saver()
with tf.Session() as sess:
    sess.run(init_op)
# Do some work with the model.
# Save the variables to disk.
    save_path = saver.save(sess, "C://tensorflow//model//test")
    print ("Model saved in file: ", save_path)

#numpy转tensor
import numpy as np
a = np.zeros((3,3))
ta = tf.convert_to_tensor(a)
with tf.Session() as sess:
     print(sess.run(ta))


#占位符
input1 = tf.placeholder(tf.float32)
input2 = tf.placeholder(tf.float32)
output = tf.multiply(input1, input2)
with tf.Session() as sess:
    print(sess.run([output], feed_dict={input1:[7.], input2:[2.]}))

View Code

 LinR demo

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt

# 随机生成1000个点，围绕在y=0.1x+0.3的直线周围
num_points = 1000
vectors_set = []
for i in range(num_points):
    x1 = np.random.normal(0.0, 0.55)
    y1 = x1 * 0.1 + 0.3 + np.random.normal(0.0, 0.03)
    vectors_set.append([x1, y1])

# 生成一些样本
x_data = [v[0] for v in vectors_set]
y_data = [v[1] for v in vectors_set]

plt.scatter(x_data,y_data,c='r')
plt.show()


# 生成1维的W矩阵，取值是[-1,1]之间的随机数
with tf.name_scope('weight'):
    W = tf.Variable(tf.random_uniform([1], -1.0, 1.0), name='W')
# 生成1维的b矩阵，初始值是0
with tf.name_scope('bias'):
    b = tf.Variable(tf.zeros([1]), name='b')
# 经过计算得出预估值y
with tf.name_scope('y'):
    y = W * x_data + b

# 以预估值y和实际值y_data之间的均方误差作为损失
with tf.name_scope('loss'):
    loss = tf.reduce_mean(tf.square(y - y_data), name='loss')
# 采用梯度下降法来优化参数
optimizer = tf.train.GradientDescentOptimizer(0.5)
# 训练的过程就是最小化这个误差值
train = optimizer.minimize(loss, name='train')

sess = tf.Session()

init = tf.global_variables_initializer()
sess.run(init)

# 初始化的W和b是多少
print ("W =", sess.run(W), "b =", sess.run(b), "loss =", sess.run(loss))
# 执行20次训练
for step in range(20):
    sess.run(train)
    # 输出训练好的W和b
    print ("W =", sess.run(W), "b =", sess.run(b), "loss =", sess.run(loss))
writer = tf.train.SummaryWriter("logs/", sess.graph)


plt.scatter(x_data,y_data,c='r')
plt.plot(x_data,sess.run(W)*x_data+sess.run(b))
plt.show()

View Code

 3.tensor. Tensorflow的核心单元是tensor.

tensorflow编程可以分为两部分：构建计算图和运行计算图