【tensorflow学习笔记】（7）可视化助手

申明：暂且为初稿：

"""Please note, this code is only for python 3+. If you are using python 2+, please modify the code accordingly."""import tensorflow as tfimport numpy as npdef 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.histogram_summary(layer_name + '/weights', Weights)        with tf.name_scope('biases'):            biases = tf.Variable(tf.zeros([1, out_size]) + 0.1, name='b')            tf.histogram_summary(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.histogram_summary(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('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('loss'):    loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),                                        reduction_indices=[1]))    tf.scalar_summary('loss', loss)with tf.name_scope('train'):    train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)sess = tf.session()merged = tf.merge_all_summaries()writer = tf.train.SummaryWriter("logs/", sess.graph)# important stepsess.run(tf.initialize_all_variables())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)
接下来，程序开始运行以后，跑到shell里运行，打开终端，输入如下语句：
cd到指定的文件下，
tensorboard --logdir  = ‘logs/’
tensorboard --logdir  = ‘logs/’
开始运行tensorboard.接下来打开浏览器，进入127.0.0.1:6006 就能够看到loss值在训练中的变化值了。