tensorboard可视化高级使用

1、Scalar

运行程序时，出错，AttributeError: 'SummaryMetadata' object has no attribute 'display_name'
只有graph图像。
后来，发现这是TensorFlow版本问题。

由于，之前装的GPU版本是tensorflow (1.3.0rc0),但是运行tensorboard的时候，没有出现scalar，然后试了升级TensorFlow版本，成功解决问题。
在anaconda prompt运行

pip install --ignore-installed --upgrade tensorflow-gpu

附上运行的代码：

from __future__ import print_functionimport 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.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 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.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("logs/", sess.graph)init = tf.global_variables_initializer()sess.run(init)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)

2、监控指标可视化

监控指标包括，变量标准差（scalar显示）、激活函数分布（histogram）、交叉熵（scalar监控）、计算图（graph）

#监控指标可视化import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_dataSUMMARY_DIR = "/log/supervisor.log"BATCH_SIZE = 100TRAIN_STEPS = 3000def variable_summaries(var, name):    with tf.name_scope('summaries'):        tf.summary.histogram(name, var)        mean = tf.reduce_mean(var)        tf.summary.scalar('mean/' + name, mean)        stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))        tf.summary.scalar('stddev/' + name, stddev)  def nn_layer(input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu):    with tf.name_scope(layer_name):        with tf.name_scope('weights'):            weights = tf.Variable(tf.truncated_normal([input_dim, output_dim], stddev=0.1))            variable_summaries(weights, layer_name + '/weights')        with tf.name_scope('biases'):            biases = tf.Variable(tf.constant(0.0, shape=[output_dim]))            variable_summaries(biases, layer_name + '/biases')        with tf.name_scope('Wx_plus_b'):            preactivate = tf.matmul(input_tensor, weights) + biases            tf.summary.histogram(layer_name + '/pre_activations', preactivate)        activations = act(preactivate, name='activation')                # 记录神经网络节点输出在经过激活函数之后的分布。        tf.summary.histogram(layer_name + '/activations', activations)        return activationsdef main():    mnist = input_data.read_data_sets("../../datasets/MNIST_data", one_hot=True)    with tf.name_scope('input'):        x = tf.placeholder(tf.float32, [None, 784], name='x-input')        y_ = tf.placeholder(tf.float32, [None, 10], name='y-input')    with tf.name_scope('input_reshape'):        image_shaped_input = tf.reshape(x, [-1, 28, 28, 1])        tf.summary.image('input', image_shaped_input, 10)    hidden1 = nn_layer(x, 784, 500, 'layer1')    y = nn_layer(hidden1, 500, 10, 'layer2', act=tf.identity)    with tf.name_scope('cross_entropy'):        cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=y, labels=y_))        tf.summary.scalar('cross_entropy', cross_entropy)    with tf.name_scope('train'):        train_step = tf.train.AdamOptimizer(0.001).minimize(cross_entropy)    with tf.name_scope('accuracy'):        with tf.name_scope('correct_prediction'):            correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))        with tf.name_scope('accuracy'):            accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))        tf.summary.scalar('accuracy', accuracy)    merged = tf.summary.merge_all()    with tf.Session() as sess:        summary_writer = tf.summary.FileWriter(SUMMARY_DIR, sess.graph)        tf.global_variables_initializer().run()        for i in range(TRAIN_STEPS):            xs, ys = mnist.train.next_batch(BATCH_SIZE)            # 运行训练步骤以及所有的日志生成操作，得到这次运行的日志。            summary, _ = sess.run([merged, train_step], feed_dict={x: xs, y_: ys})            # 将得到的所有日志写入日志文件，这样TensorBoard程序就可以拿到这次运行所对应的            # 运行信息。            summary_writer.add_summary(summary, i)    summary_writer.close()if __name__ == '__main__':    main()

---

参考：
1. GitHub_tensorboard_test.py;
2. ‘SummaryMetadata’ object has no attribute ‘display_name’;
3. 莫烦GitHub_tensorboard_test;