重要知识点总结
1. 命名空间的应用
with tf.name_scope('input'):
2. scalar、histogram、image 等的使用
tf.summary.scalar('stddev', stddev)#标准差
tf.summary.scalar('max', tf.reduce_max(var))#最大值
tf.summary.scalar('min', tf.reduce_min(var))#最小值
tf.summary.histogram('histogram', var)#直方图
3. 产生 metadata 文件
if tf.gfile.Exists(DIR + 'projecto\\projector\\metadata.tsv'):
tf.gfile.DeleteRecursively(DIR + 'projector\\projector\\metadata.tsv')
with open(DIR + 'projector\\projector\\metadata.tsv', 'w') as f:
labels = sess.run(tf.argmax(mnist.test.labels[:],1))
for i in range(image_num):
f.write(str(labels[i])+'\n')
4. 通过 projector.ProjectorConfig 生成日志文件
config = projector.ProjectorConfig()
embed = config.embeddings.add()
embed.tensor_name = embedding.name
embed.metadata_path = DIR + 'projector\\projector\\metadata.tsv'
embed.sprite.image_path = DIR + 'projector\\data\\img.png'
embed.sprite.single_image_dim.extend([28,28])
projector.visualize_embeddings(projector_writer,config)
5. 保存日志文件
save.save(sess,DIR + 'projector\\projector\\a_module.ckpt',global_step=max_step)
整体代码如下图：
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from tensorflow.contrib.tensorboard.plugins import projector
#载入数据集
mnist = input_data.read_data_sets("F:/AI/AItest/MNIST_data",one_hot=True)
#运行次数
max_step = 100
#图片数量
image_num = 3000
#文件路径
DIR = 'F:\\AI/AItest\\'
sess = tf.Session()
#载入图片
embedding = tf.Variable(tf.stack(mnist.test.images[:image_num]),trainable=False,name='embedding')
#参数概要
def variable_summaries(var):
with tf.name_scope('summaries'):
mean = tf.reduce_mean(var)
tf.summary.scalar('mean', mean)#平均值
with tf.name_scope('stddev'):
stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
tf.summary.scalar('stddev', stddev)#标准差
tf.summary.scalar('max', tf.reduce_max(var))#最大值
tf.summary.scalar('min', tf.reduce_min(var))#最小值
tf.summary.histogram('histogram', var)#直方图
#命名空间
with tf.name_scope('input'):
#定义两个placeholder
x = tf.placeholder(tf.float32,[None,784],name='x-input')
y = tf.placeholder(tf.float32,[None,10],name='y-input')
#显示图片
with tf.name_scope('input_reshape'):
image_shaped_input = tf.reshape(x,[-1,28,28,1])
tf.summary.image('input',image_shaped_input,10)
with tf.name_scope('layer'):
#创建一个简单的神经网络
with tf.name_scope('wights'):
W = tf.Variable(tf.zeros([784,10]),name='W')
variable_summaries(W)
with tf.name_scope('biases'):
b = tf.Variable(tf.zeros([10]),name='b')
variable_summaries(b)
with tf.name_scope('wx_plus_b'):
wx_plus_b = tf.matmul(x,W) + b
with tf.name_scope('softmax'):
prediction = tf.nn.softmax(wx_plus_b)
#二次代价函数
# loss = tf.reduce_mean(tf.square(y-prediction))
with tf.name_scope('loss'):
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction))
tf.summary.scalar('loss',loss)
with tf.name_scope('train'):
#使用梯度下降法
train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)
#初始化变量
sess.run(tf.global_variables_initializer())
with tf.name_scope('accuracy'):
with tf.name_scope('correct_prediction'):
#结果存放在一个布尔型列表中
correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))#argmax返回一维张量中最大的值所在的位置
with tf.name_scope('accuracy'):
#求准确率
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
tf.summary.scalar('accuracy',accuracy)
#产生metadata文件
if tf.gfile.Exists(DIR + 'projecto\\projector\\metadata.tsv'):
tf.gfile.DeleteRecursively(DIR + 'projector\\projector\\metadata.tsv')
with open(DIR + 'projector\\projector\\metadata.tsv', 'w') as f:
labels = sess.run(tf.argmax(mnist.test.labels[:],1))
for i in range(image_num):
f.write(str(labels[i])+'\n')
#合并所有的summary
merged = tf.summary.merge_all()
projector_writer = tf.summary.FileWriter(DIR + 'projector/projector',sess.graph)
save = tf.train.Saver()
config = projector.ProjectorConfig()
embed = config.embeddings.add()
embed.tensor_name = embedding.name
embed.metadata_path = DIR + 'projector\\projector\\metadata.tsv'
embed.sprite.image_path = DIR + 'projector\\data\\img.png'
embed.sprite.single_image_dim.extend([28,28])
projector.visualize_embeddings(projector_writer,config)
for i in range(max_step):
batch_xs,batch_ys = mnist.train.next_batch(100)
run_options = tf.RunOptions(trace_level = tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
summary,_ = sess.run([merged,train_step],feed_dict={x:batch_xs,y:batch_ys},options=run_options,run_metadata=run_metadata)
projector_writer.add_run_metadata(run_metadata,'stepd' % i)
projector_writer.add_summary(summary,i)
if i0 == 0:
acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})
print("Iter " + str(i) + ",Testing Accuracy " + str(acc))
save.save(sess,DIR + 'projector\\projector\\a_module.ckpt',global_step=max_step)
projector_writer.close()
sess.close()
使用命令行运行tensorboard的结果：
tip：如果日志文件在其他的盘，需要先切换到该盘，然后使用tensorboard --logdir=（路径名称），会出现一个地址，直接用火狐或者google浏览器打开就好
结果如图所示：
