先说一下遇到的一些问题，在实现了tensorboard可以显示graphs以后发现了一个问题，就是tensorboard无法显示scalars、distributions、histograms
其实就是tensorflow的版本问题，升级到1.8发现用不了，报错，和之前的错误一样
ImportError:DLL load failed with error code -1073741795我之前的一篇文章里提到过这个问题import tensorflow error然后我从1.8一直降低版本到1.5
C:\Users\Administrator>python Python 3.5.2 |Anaconda 4.2.0 (64-bit)| (default, Jul 5 2016, 11:41:13) [MSC v. 900 64 bit (AMD64)] on win32 Type "help", "copyright", "credits" or "license" for more information. >>> import tensorflow as tf >>> tf.__version__ '1.5.0-rc0'再去运行
import tensorflow as tf import 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('weights'): Weights = tf.Variable(tf.random_normal([in_size, out_size]), name='W') tf.summary.histogram(layer_name + '/weights', Weights) with tf.name_scope('biases'): biases = tf.Variable(tf.zeros([1, out_size]) + 0.1, name='b') tf.summary.histogram(layer_name + '/biases', biases) with tf.name_scope('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 data x_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 network with tf.name_scope('inputs'): xs = tf.placeholder(tf.float32, [None, 1], name='x_input') ys = tf.placeholder(tf.float32, [None, 1], name='y_input') # add hidden layer l1 = add_layer(xs, 1, 10, n_layer=1, activation_function=tf.nn.relu) # add output layer prediction = add_layer(l1, 10, 1, n_layer=2, activation_function=None) # the error between prediciton and real data with tf.name_scope('loss'): loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction), reduction_indices=[1])) tf.summary.scalar('loss', loss) with tf.name_scope('train'): train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss) sess = tf.Session() merged = tf.summary.merge_all() writer = tf.summary.FileWriter("G://my_python_pro", sess.graph) # important step sess.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)在路径G://my_python_pro中打开终端，输入
tensorboard --logdir=G://my_python_pro在你的ip地址后加上：6006
即可看到
