TensorBoard使用

先跑一个小例程

'''Graph and Loss visualization using Tensorboard.This example is using the MNIST database of handwritten digits(http://yann.lecun.com/exdb/mnist/)Author: Aymeric DamienProject: https://github.com/aymericdamien/TensorFlow-Examples/'''from __future__ import print_functionimport tensorflow as tf# Import MNIST datafrom tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets("/tmp/data/", one_hot=True)# Parameterslearning_rate = 0.01training_epochs = 25batch_size = 100display_step = 1logs_path = '/tmp/tensorflow_logs/example'# Network Parametersn_hidden_1 = 256 # 1st layer number of featuresn_hidden_2 = 256 # 2nd layer number of featuresn_input = 784 # MNIST data input (img shape: 28*28)n_classes = 10 # MNIST total classes (0-9 digits)# tf Graph Input# mnist data image of shape 28*28=784x = tf.placeholder(tf.float32, [None, 784], name='InputData')# 0-9 digits recognition => 10 classesy = tf.placeholder(tf.float32, [None, 10], name='LabelData')# Create modeldef multilayer_perceptron(x, weights, biases):    # Hidden layer with RELU activation    layer_1 = tf.add(tf.matmul(x, weights['w1']), biases['b1'])    layer_1 = tf.nn.relu(layer_1)    # Create a summary to visualize the first layer ReLU activation    #tf.summary.histogram("relu1", layer_1)    tf.histogram_summary("relu1", layer_1)    # Hidden layer with RELU activation    layer_2 = tf.add(tf.matmul(layer_1, weights['w2']), biases['b2'])    layer_2 = tf.nn.relu(layer_2)    # Create another summary to visualize the second layer ReLU activation    #tf.summary.histogram("relu2", layer_2)    tf.histogram_summary("relu2", layer_2)    # Output layer    out_layer = tf.add(tf.matmul(layer_2, weights['w3']), biases['b3'])    return out_layer# Store layers weight & biasweights = {    'w1': tf.Variable(tf.random_normal([n_input, n_hidden_1]), name='W1'),    'w2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2]), name='W2'),    'w3': tf.Variable(tf.random_normal([n_hidden_2, n_classes]), name='W3')}biases = {    'b1': tf.Variable(tf.random_normal([n_hidden_1]), name='b1'),    'b2': tf.Variable(tf.random_normal([n_hidden_2]), name='b2'),    'b3': tf.Variable(tf.random_normal([n_classes]), name='b3')}# Encapsulating all ops into scopes, making Tensorboard's Graph# Visualization more convenientwith tf.name_scope('Model'):    # Build model    pred = multilayer_perceptron(x, weights, biases)with tf.name_scope('Loss'):    # Softmax Cross entropy (cost function)    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))with tf.name_scope('SGD'):    # Gradient Descent    optimizer = tf.train.GradientDescentOptimizer(learning_rate)    # Op to calculate every variable gradient    grads = tf.gradients(loss, tf.trainable_variables())    grads = list(zip(grads, tf.trainable_variables()))    # Op to update all variables according to their gradient    apply_grads = optimizer.apply_gradients(grads_and_vars=grads)with tf.name_scope('Accuracy'):    # Accuracy    acc = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))    acc = tf.reduce_mean(tf.cast(acc, tf.float32))# Initializing the variablesinit = tf.initialize_all_variables()# Create a summary to monitor cost tensortf.scalar_summary("loss", loss)# Create a summary to monitor accuracy tensortf.scalar_summary("accuracy", acc)# Create summaries to visualize weightsfor var in tf.trainable_variables():    tf.histogram_summary(var.name, var)# Summarize all gradientsfor grad, var in grads:    tf.histogram_summary(var.name + '/gradient', grad)# Merge all summaries into a single opmerged_summary_op = tf.merge_all_summaries()# Launch the graphwith tf.Session() as sess:    sess.run(init)    # op to write logs to Tensorboard    summary_writer = tf.train.SummaryWriter(logs_path,                                            graph=tf.get_default_graph())    # Training cycle    for epoch in range(training_epochs):        avg_cost = 0.        total_batch = int(mnist.train.num_examples/batch_size)        # Loop over all batches        for i in range(total_batch):            batch_xs, batch_ys = mnist.train.next_batch(batch_size)            # Run optimization op (backprop), cost op (to get loss value)            # and summary nodes            _, c, summary = sess.run([apply_grads, loss, merged_summary_op],                                     feed_dict={x: batch_xs, y: batch_ys})            # Write logs at every iteration            summary_writer.add_summary(summary, epoch * total_batch + i)            # Compute average loss            avg_cost += c / total_batch        # Display logs per epoch step        if (epoch+1) % display_step == 0:            print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost))    print("Optimization Finished!")    # Test model    # Calculate accuracy    print("Accuracy:", acc.eval({x: mnist.test.images, y: mnist.test.labels}))    print("Run the command line:\n" \          "--> tensorboard --logdir=/tmp/tensorflow_logs " \"\nThen open http://0.0.0.0:6006/ into your web browser")

运行该程序。

运行完了之后，可以在"/tmp/tensorflow_logs/example"目录下找到事件记录文件"events.out.tfevents.1490276692.inspur.datanode7.com"。

输入"tensorboard --logdir=/tmp/tensorflow_logs"

这个时候就遇到问题啦~看下面呢：

[root@inspur example]# tensorboard --logdir=/tmp/tensorflow_logsERROR:tensorflow:Tried to connect to port 6006, but address is in use.

6006端口被占用了，把它干掉好啦。

[root@inspur example]# lsof -i:6006COMMAND     PID USER   FD   TYPE   DEVICE SIZE/OFF NODE NAMEtensorboa 28508 root    4u  IPv4 18373697      0t0  TCP *:6006 (LISTEN)[root@inspur example]# kill -9 28508[root@inspur example]# tensorboard --logdir=/tmp/tensorflow_logsStarting TensorBoard b'23' on port 6006(You can navigate to http://0.0.0.0:6006)

进程号为28508的进程占用了端口6006，用kill命令干掉。

另外程序是在服务器上跑得，所以在本机上查看的时候，网址要输 ‘’服务器IP:6006"