TensorFlow学习笔记--mnist可视化版本

主要代码tensorflow的官网上都有，这个版本主要是增加了一些可视化的东西。方便观察一些变量。

注：以下代码为1.0版本下

from tensorflow.examples.tutorials.mnist import input_dataimport tensorflow as tfsess = tf.InteractiveSession()mnist = input_data.read_data_sets('MNIST_data',one_hot=True)#set parameterlearning_rate=0.001#set placeholderwith tf.name_scope('inputs'):    x_input = tf.placeholder('float',[None,784],name='x_input')    y_input = tf.placeholder('float',[None,10],name='y_input')#set variablewith tf.name_scope('weights'):    W = tf.Variable(tf.random_normal([784,10]), name='W')    tf.summary.histogram('weights', W)with tf.name_scope('b'):    b = tf.Variable(tf.random_normal([10]),name='b')    tf.summary.histogram('b', b)#set graphwith tf.name_scope('layer'):    pred_y = tf.nn.softmax(tf.matmul(x_input,W)+b)    tf.summary.histogram('out',pred_y)#set costwith tf.name_scope('loss'):    loss =-tf.reduce_sum(y_input*tf.log(pred_y))    tf.summary.scalar('loss', loss)with tf.name_scope('train'):    train = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)        sess = tf.Session()merged = tf.summary.merge_all()# save the logstrain_writer = tf.summary.FileWriter("mnist_logs/train", sess.graph)test_writer = tf.summary.FileWriter("mnist_logs/test", sess.graph)#runwith tf.Session() as sess:    sess.run(tf.global_variables_initializer())    for i in range(1000):        batch_xs,batch_ys = mnist.train.next_batch(100)        sess.run(train,feed_dict={x_input:batch_xs,y_input:batch_ys})        if i % 50 == 0:            correct_prediction = tf.equal(tf.argmax(pred_y,1),tf.argmax(y_input,1))            correct_rate = tf.reduce_mean(tf.cast(correct_prediction,'float32'))            print sess.run(loss,feed_dict={x_input:mnist.test.images,y_input:mnist.test.labels})            print sess.run(correct_rate,feed_dict={x_input:batch_xs,y_input:batch_ys})            train_result = sess.run(merged,feed_dict={x_input:batch_xs,y_input:batch_ys})            train_writer.add_summary(train_result, i)            test_result = sess.run(merged,feed_dict={x_input:mnist.test.images,y_input:mnist.test.labels})            test_writer.add_summary(test_result, i)

可以看到一个logs的文件，然后在命令窗口中打以下命令

$ tensorboard --logdir=mnist_logs

打开链接 http://0.0.0.0:6006