tensorflow的日常Demo

Session

Session 是 Tensorflow 为了控制,和输出文件的执行的语句. 运行 session.run() 可以获得你要得知的运算结果, 或者是你所要运算的部分.

01-graph_session.py

# -*- coding: UTF-8 -*-

# 引入tensorflow
import tensorflow as tf

# 创建两个常量 Tensor
const1 = tf.constant([[2, 2]])
const2 = tf.constant([[4],
                      [4]])

multiple = tf.matmul(const1, const2)

# 尝试用print输出multiple的值
print(multiple)

# 创建了 Session（会话）对象
sess = tf.Session()

# 用Session的run方法来实际运行multiple这个矩阵乘法操作
# 并把操作执行的结果赋值给 result
result = sess.run(multiple)

# 用print打印矩阵乘法的结果
print(result)

if const1.graph is tf.get_default_graph():
    print("const1所在的图（Graph）是当前上下文默认的图")

# 关闭已用完的Session（会话）
sess.close()

执行结果：

Tensor("MatMul:0", shape=TensorShape([Dimension(1), Dimension(1)]), dtype=int32)
const1所在的图（Graph）是当前上下文默认的图

 可视化工具tensorboard

构造图Demo

# -*- coding: UTF-8 -*-

# 引入tensorflow
import tensorflow as tf

# 构造图（Graph）的结构
# 用一个线性方程的例子 y = W * x + b
W = tf.Variable(2.0, dtype=tf.float32, name="Weight") # 权重
b = tf.Variable(1.0, dtype=tf.float32, name="Bias") # 偏差
x = tf.placeholder(dtype=tf.float32, name="Input") # 输入
with tf.name_scope("Output"):      # 输出的命名空间
    y = W * x + b    # 输出

#const = tf.constant(2.0) # 不需要初始化

# 定义保存日志的路径
path = "./log"

# 创建用于初始化所有变量（Variable）的操作
init = tf.global_variables_initializer()

# 创建Session（会话）
with tf.Session() as sess:
    sess.run(init) # 初始化变量
    writer = tf.summary.FileWriter(path, sess.graph)
    result = sess.run(y, {x: 3.0})
    print("y = %s" % result) # 打印 y = W * x + b 的值，就是 7

 Matplotlib中的画图

import numpy as np
import matplotlib.pyplot as plt

from matplotlib.ticker import NullFormatter  # useful for `logit` scale

# Fixing random state for reproducibility
np.random.seed(19680801)

# make up some data in the interval ]0, 1[
y = np.random.normal(loc=0.5, scale=0.4, size=1000)
y = y[(y > 0) & (y < 1)]
y.sort()
x = np.arange(len(y))

# plot with various axes scales
plt.figure(1)

# linear
plt.subplot(221)
plt.plot(x, y)
plt.yscale('linear')
plt.title('linear')
plt.grid(True)


# log
plt.subplot(222)
plt.plot(x, y)
plt.yscale('log')
plt.title('log')
plt.grid(True)


# symmetric log
plt.subplot(223)
plt.plot(x, y - y.mean())
plt.yscale('symlog', linthreshy=0.01)
plt.title('symlog')
plt.grid(True)

# logit
plt.subplot(224)
plt.plot(x, y)
plt.yscale('logit')
plt.title('logit')
plt.grid(True)
# Format the minor tick labels of the y-axis into empty strings with
# `NullFormatter`, to avoid cumbering the axis with too many labels.
plt.gca().yaxis.set_minor_formatter(NullFormatter())
# Adjust the subplot layout, because the logit one may take more space
# than usual, due to y-tick labels like "1 - 10^{-3}"
plt.subplots_adjust(top=0.92, bottom=0.08, left=0.10, right=0.95, hspace=0.25,
                    wspace=0.35)

plt.show()

 梯度下降与线性回归