1、使用tensorflow的SoftMax函数，对手写数字进行识别
Administrator@SuperComputer MINGW64 ~ $ docker run -it -p 8888:8888 registry.cn-hangzhou.aliyuncs.com/denverdino/tens orflow bash root@b3e200093da9:/notebooks# python Python 2.7.6 (default, Oct 26 2016, 20:30:19) [GCC 4.8.4] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>> from tensorflow.examples.tutorials.mnist import input_data
-----------------------------------------------------------对于中间这个数据是怎么来的，我只能说是从网上下的，具体存放在哪个文件间中，我至今都没有找到 >>> mnist = input_data.read_data_sets("/MNIST_data/",one_hot = True) Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes. Extracting /MNIST_data/train-images-idx3-ubyte.gz Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes. Extracting /MNIST_data/train-labels-idx1-ubyte.gz Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes. Extracting /MNIST_data/t10k-images-idx3-ubyte.gz Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes. Extracting /MNIST_data/t10k-labels-idx1-ubyte.gz >>> import tensorflow as tf >>> 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)
>>> init = tf.initialize_all_variables()//这个函数现在已经不用了，应该使用下边的那一行函数 WARNING:tensorflow:From <stdin>:1 in <module>.: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02. Instructions for updating: Use `tf.global_variables_initializer` instead. >>> init = tf.global_variables_initializer() >>> sess = tf.Session() >>> sess.run(init) >>> for i in range(1000): ... batch_xs,batch_ys = mnist.train.next_batch(100) ... sess.run(train_step,feed_dict = {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(sess.run(accuracy,feed_dict={x:mnist.test.images,y_:mnist.test.labels} )) 0.9167 >>>
最后，训练后得到的模型在测试数据上的正确率是0.9167