TF：TF之Tensorboard实践：将神经网络Tensorboard形式得到events.out.tfevents文件+dos内运行该文件本地服务器输出到网页可视化—Jason niu

import tensorflow as tfimport numpy as np ????def add_layer(inputs, in_size, out_size, n_layer, activation_function=None): ???# add one more layer and return the output of this layer ???layer_name = ‘layer%s‘ % n_layer ???with tf.name_scope(layer_name): ???????with tf.name_scope(‘Jason_niu_weights‘): ???????????Weights = tf.Variable(tf.random_normal([in_size, out_size]), name=‘W‘) ???????????tf.summary.histogram(layer_name + ‘/weights‘, Weights) ???????with tf.name_scope(‘Jason_niu_biases‘): ???????????biases = tf.Variable(tf.zeros([1, out_size]) + 0.1, name=‘b‘) ???????????tf.summary.histogram(layer_name + ‘/biases‘, biases) ????????with tf.name_scope(‘Jason_niu_Wx_plus_b‘): ???????????Wx_plus_b = tf.add(tf.matmul(inputs, Weights), biases) ???????if activation_function is None: ???????????outputs = Wx_plus_b ???????else: ???????????outputs = activation_function(Wx_plus_b, ) ???????tf.summary.histogram(layer_name + ‘/outputs‘, outputs) ????????return outputs# Make up some real datax_data = np.linspace(-1, 1, 300)[:, np.newaxis]noise = np.random.normal(0, 0.05, x_data.shape)y_data = np.square(x_data) - 0.5 + noise# define placeholder for inputs to networkwith tf.name_scope(‘Jason_niu_inputs‘): ???xs = tf.placeholder(tf.float32, [None, 1], name=‘x_input‘) ???ys = tf.placeholder(tf.float32, [None, 1], name=‘y_input‘)# add hidden layerl1 = add_layer(xs, 1, 10, n_layer=1, activation_function=tf.nn.relu)# add output layerprediction = add_layer(l1, 10, 1, n_layer=2, activation_function=None)# the error between prediciton and real datawith tf.name_scope(‘Jason_niu_loss‘): ???loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction), ???????????????????????????????????????reduction_indices=[1])) ???tf.summary.scalar(‘Jason_niu_loss‘, loss) ?with tf.name_scope(‘Jason_niu_train‘): ???train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)sess = tf.Session()merged = ?tf.summary.merge_all() ?writer = tf.summary.FileWriter("logs3/", sess.graph)# important stepsess.run(tf.global_variables_initializer())for i in range(1000): ?????sess.run(train_step, feed_dict={xs: x_data, ys: y_data}) ???if i % 50 == 0: ??????????????????????????????????????????????????result = sess.run(merged,feed_dict={xs: x_data, ys: y_data}) ???????writer.add_summary(result, i) ????????????????????????