一、还是以mnist的例程,来演示tensorboard的可视化
1、先上代码:
from tensorflow.examples.tutorials.mnist import input_data import tensorflow as tf dir = './MNIST_data' # 最好填绝对路径 # 1.Import data mnist = input_data.read_data_sets(dir, one_hot=True) # print data information print(mnist.train.images.shape, mnist.train.labels.shape) print(mnist.test.images.shape, mnist.train.labels.shape) print(mnist.validation.images.shape, mnist.validation.labels.shape) def weight_variable(shape): initial = tf.truncated_normal(shape, stddev=0.1) return tf.Variable(initial) def bias_variable(shape): initial = tf.constant(0.1, shape=shape) return tf.Variable(initial) def conv2d(x, W): return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') def max_pool_2x2(x): return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') #sess = tf.InteractiveSession() myGraph = tf.Graph() with myGraph.as_default(): with tf.name_scope('inputsAndLabels'): #输入数据 x = tf.placeholder(tf.float32, [None, 784]) y_ = tf.placeholder(tf.float32, [None, 10]) with tf.name_scope('hidden1'): #第一层卷积 W_conv1 = weight_variable([5, 5, 1, 32]) b_conv1 = bias_variable([32]) x_image = tf.reshape(x, [-1, 28, 28, 1]) h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) h_pool1 = max_pool_2x2(h_conv1) tf.summary.image('x_input',x_image,max_outputs=10) tf.summary.histogram('W_con1',W_conv1) tf.summary.histogram('b_con1',b_conv1) with tf.name_scope('hidden2'):#第二层卷积 W_conv2 = weight_variable([5, 5, 32, 64]) b_conv2 = bias_variable([64]) h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) h_pool2 = max_pool_2x2(h_conv2) tf.summary.histogram('W_con2', W_conv2) tf.summary.histogram('b_con2', b_conv2) with tf.name_scope('fc1'):#密集连接层 W_fc1 = weight_variable([7 * 7 * 64, 1024]) b_fc1 = bias_variable([1024]) h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64]) h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) # dropput keep_prob = tf.placeholder(tf.float32) h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) tf.summary.histogram('W_fc1', W_fc1) tf.summary.histogram('b_fc1', b_fc1) with tf.name_scope('fc2'):#输出层 W_fc2 = weight_variable([1024, 10]) b_fc2 = bias_variable([10]) y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2 tf.summary.histogram('W_fc1', W_fc1) tf.summary.histogram('b_fc1', b_fc1) with tf.name_scope('train'):#训练和评估模型 cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv)) train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) tf.summary.scalar('loss', cross_entropy) tf.summary.scalar('accuracy', accuracy) with tf.Session(graph=myGraph) as sess: sess.run(tf.global_variables_initializer()) saver = tf.train.Saver() merged = tf.summary.merge_all() summary_writer = tf.summary.FileWriter('./mnistEven/', graph=sess.graph) for i in range(2000): batch = mnist.train.next_batch(50) train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}) if i % 100 == 0: train_accuracy = accuracy.eval(feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0}) print("step %d, training accuracy %g" % (i, train_accuracy)) summary = sess.run(merged, feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0}) summary_writer.add_summary(summary, i) saver.save(sess, save_path='./res/mnistmodel', global_step=1)
2、运行完成后,会在mnistEven目录下,生成 events.out.tfevents.1517282424.DESKTOP-527AKJ 这样的文件。
二、启动tensorboard
1、tensorboard不需要额外的安装,在tf安装完成时,tensorboard就会被自动安装。
2、回到刚刚保存的mnistEven文件夹所在目录,在文件资源管理器的路径栏中直接输入cmd启动dos对话框。
3、输入命令:tensorboard --logdir=mnistEven,不出意外的话,会打印出下面所示的信息
TensorBoard 0.4.0rc3 at http://DESKTOP-527AKJ8:6006 (Press CTRL+C to quit)
4、打开谷歌浏览器,输入:http://DESKTOP-527AKJ8:6006,即可看到tensorboard的界面