tensorflow 练习

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import tensorflow as tf   from tensorflow.examples.tutorials.mnist import input_datamnist=input_data.read_data_sets("MNIST_data/",one_hot=True)sess=tf.InteractiveSession()x=tf.placeholder(tf.float32,[None,784])W=tf.Variable(tf.zeros([784,10]))b=tf.Variable(tf.zeros([10]))y=tf.nn.softmax(tf.matmul(x,W)+b)y_=tf.placeholder(tf.float32,[None,10])cross_entropy=tf.reduce_mean(-tf.reduce_sum(y_*tf.log(y),reduction_indices=[1]))train_step=tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)tf.global_variables_initializer().run()for i in range(1000):    batch_xs,batch_ys=mnist.train.next_batch(100)    train_step.run({x:batch_xs,y_:batch_ys})correct_prediction=tf.equal(tf.argmax(y,1),tf.argmax(y_,1))accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))print(accuracy.eval({x:mnist.test.images,y_:mnist.test.labels}))

此处可能报找不到global_variables_initializer的错误，这个是由于你的tensorflow的版本过于低的问题导致的。

可以到https://github.com/tensorflow/tensorflow下载相应的.whl 用命令 sudo pip install XXXX.whl进行安装
这里写图片描述

import tensorflow as tffrom numpy.random import RandomStatebatch_size = 8x = 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)# 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 = 10loss_more = 1loss = 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)rdm = RandomState(1)X = rdm.rand(128,2)Y = [[x1+x2+(rdm.rand()/10.0-0.05)] for (x1, x2) in X]with tf.Session() as sess:    tf.global_variables_initializer().run()    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)

简单的保存文件

import tensorflow as tfv1=tf.Variable(tf.constant(1.0,shape=[1]),name="v1")v2=tf.Variable(tf.constant(1.0,shape=[1]),name="v2")result=v1+v2init_op=tf.global_variables_initializer()saver=tf.train.Saver()with tf.Session()  as sess:    sess.run(init_op)    saver.save(sess,"model.ckpt")

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