2018.01.07 17:59* 字数 173
- pip install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.0.1-cp27-none-linux_x86_64.whl
- pip3 install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.0.1-cp35-cp35m-linux_x86_64.whl
- pip install https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.0.1-py2-none-any.whl
- pip3 install https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.0.1-py3-none-any.whl
- 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))
- 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)
- tensorboard--logdir = "/Users/lijianzhao/tensorBoard/"
- http://192.168.199.213:6006
- 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()
- 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()
- saver = tf.train.Saver()
- saver.save(sess, "./tmp/ckpt/test")
程序员
- save.restore(sess, "./tmp/ckpt/test")
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来源: http://www.jianshu.com/p/475793442f53