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_function
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("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 tf
from tensorflow.examples.tutorials.mnist import input_data

SUMMARY_DIR = "/log/supervisor.log"
BATCH_SIZE = 100
TRAIN_STEPS = 3000

def 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 activations
def 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;