Tensorflow可视化编程 Tensorflow可视化编程

安装Tensoflow1.0 Linux/ubuntu:

python2.7:

pip install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.0.1-cp27-none-linux_x86_64.whl

python3.5:

pip3 install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.0.1-cp35-cp35m-linux_x86_64.whl

Maxos:

python2:

pip install https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.0.1-py2-none-any.whl

python3:

pip3 install https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.0.1-py3-none-any.whl

Tensorflow完成加法

import tensorflow as tf # 消除警告(使用源码安装可自动消除) import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'a = tf.constant(3.0) b = tf.constant(4.0)with tf.Session() as sess: a_b = tf.add(a, b) print("相加后的类型为") print(a_b) print("真正的结果为:") print(sess.run(a_b))

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tf_add

将加法运算以图形化方式展示

在会话中添加记录文件的语句

import tensorflow as tf # 消除警告(使用源码安装可自动消除) import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'a = tf.constant(3.0) b = tf.constant(4.0)with tf.Session() as sess: a_b = tf.add(a, b) print("相加后的类型为") print(a_b) print("真正的结果为:") print(sess.run(a_b)) # 添加board记录文件 file_write = tf.summary.FileWriter('/Users/lijianzhao/tensorBoard/', graph=sess.graph)

在终端运行tensorboard --logdir="/Users/lijianzhao/tensorBoard/"

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在终端运行tensorboard

根据终端提示,在浏览器键入http://192.168.199.213:6006

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tensorboard主界面

选择GRAPHS

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选择GRAPHS

实现简单的线性回归

import tensorflow as tf # 消除警告(使用源码安装可自动消除) import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'# 回归函数 def my_regression():# 准备10000 条数据x的平均值为5.0 标准差为1.0 x = tf.random_normal([100, 1], mean = 5.0, stddev=1.0, name="x") # 真实的关系为 y = 0.7x + 0.6 y_true = tf.matmul(x, [[0.7]]) + 0.6# 创建权重变量 weight = tf.Variable(tf.random_normal([1, 1], mean=1.0, stddev=0.1), name="weight")# 创建偏置变量,初始值为1 bias = tf.Variable(1.0, name="bias")# 预测结果 y_predict = tf.matmul(x, weight) + bias# 计算损失 loss = tf.reduce_mean(tf.square(y_predict - y_true))# 梯度下降减少损失,每次的学习率为0.1 train_op = tf.train.GradientDescentOptimizer(0.01).minimize(loss)# 收集变量 tf.summary.scalar("losses", loss) tf.summary.histogram("weightes", weight)# 合并变量 merged = tf.summary.merge_all()# 初始化变量 init_op = tf.global_variables_initializer()# 梯度下降优化损失 with tf.Session() as sess: sess.run(init_op)print("初始的权重为{}, 初始的偏置为{}".format(weight.eval(), bias.eval()))# 添加board记录文件 file_write = tf.summary.FileWriter('/Users/lijianzhao/tensorBoard/my_regression', graph=sess.graph)# 循环训练线性回归模型 for i in range(20000): sess.run(train_op) print("训练第{}次的权重为{}, 偏置为{}".format(i,weight.eval(), bias.eval()))# 观察每次值的变化 # 运行merge summery = sess.run(merged) # 每次收集到的值添加到文件中 file_write.add_summary(summery, i)if __name__ == '__main__': my_regression()

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运行结果

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程序流程图

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损失值降低

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权重逐渐接近真实值

为程序添加作用域

import tensorflow as tf # 消除警告(使用源码安装可自动消除) import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'# 回归函数 def my_regression():# 准备数据 with tf.variable_scope("data"): # 准备10000 条数据x的平均值为5.0 标准差为1.0 x = tf.random_normal([100, 1], mean = 5.0, stddev=1.0, name="x") # 真实的关系为 y = 0.7x + 0.6 y_true = tf.matmul(x, [[0.7]]) + 0.6# 创建模型 with tf.variable_scope ("model"): # 创建权重变量 weight = tf.Variable(tf.random_normal([1, 1], mean=1.0, stddev=0.1), name="weight")# 创建偏置变量,初始值为1 bias = tf.Variable(1.0, name="bias")# 预测结果 y_predict = tf.matmul(x, weight) + bias# 计算损失 with tf.variable_scope ("loss"): # 计算损失 loss = tf.reduce_mean(tf.square(y_predict - y_true))# 减少损失 with tf.variable_scope("optimizer"): # 梯度下降减少损失,每次的学习率为0.1 train_op = tf.train.GradientDescentOptimizer(0.01).minimize(loss)# 收集变量 tf.summary.scalar("losses", loss) tf.summary.histogram("weightes", weight)# 合并变量 merged = tf.summary.merge_all()# 初始化变量 init_op = tf.global_variables_initializer()# 梯度下降优化损失 with tf.Session() as sess: sess.run(init_op) print("初始的权重为{}, 初始的偏置为{}".format(weight.eval(), bias.eval())) # 添加board记录文件 file_write = tf.summary.FileWriter('/Users/lijianzhao/tensorBoard/my_regression', graph=sess.graph) # 循环训练线性回归模型 for i in range(20000): sess.run(train_op) print("训练第{}次的权重为{}, 偏置为{}".format(i,weight.eval(), bias.eval())) # 观察每次值的变化 # 运行merge summery = sess.run(merged) # 每次收集到的值添加到文件中 file_write.add_summary(summery, i)if __name__ == '__main__': my_regression()