利用Tensorboard Embedding可视化训练过程

用Embedding可视化MNIST训练过程及结果

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from tensorflow.contrib.tensorboard.plugins import projector

mnist=input_data.read_data_sets('MNIST_data',one_hot=True)
max_steps=1001
image_num=3000
DIR='/Users/yuewang/Desktop/bilibili-tensorflow'

sess=tf.Session()

#载入图片
embedding=tf.Variable(tf.stack(mnist.test.images[:image_num]),trainable=False,name='embedding')

def variable_summaries(var):
    with tf.name_scope('summaries'):
        mean=tf.reduce_mean(var)
        tf.summary.scalar('mean',mean)
        with tf.name_scope('stddev'):
            stddev=tf.sqrt(tf.reduce_mean(tf.square(var-mean)))
            tf.summary.scalar('stddev',stddev)
        tf.summary.scalar('max',tf.reduce_max(var))
        tf.summary.scalar('min',tf.reduce_min(var))
        tf.summary.histogram('histogram',var)

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)

with tf.name_scope('layer'):
    with tf.name_scope('weights'):
        w=tf.Variable(tf.random_normal([784,10]),name='W')
        variable_summaries(w)
    with tf.name_scope('biases'):
        b=tf.Variable(tf.zeros([10]),'b')
        variable_summaries(b)
    with tf.name_scope('Wx_plus_b'):
        Wx_plus_b=tf.matmul(x,w)+b
    with tf.name_scope('softmax'):
        y_pred=tf.nn.softmax(Wx_plus_b)

with tf.name_scope('loss'):
    loss=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=y_pred))
    tf.summary.scalar('loss',loss)
with tf.name_scope('accuracy'):
    accuracy=tf.reduce_mean(tf.cast(tf.equal(tf.argmax(y,1),tf.argmax(y_pred,1)),tf.float32))
    tf.summary.scalar('accuracy',accuracy)

sess.run(tf.global_variables_initializer())
with tf.name_scope('train'):
    train_step=tf.train.GradientDescentOptimizer(0.2).minimize(loss)

#产生metadata文件
if tf.gfile.Exists(DIR+'/projector/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')

merged=tf.summary.merge_all()
projector_writer=tf.summary.FileWriter(DIR+'/projector/projector',sess.graph)
# writer=tf.summary.FileWriter(DIR+'/projector/projector/logs',sess.graph)
saver=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/mnist_10k_sprite.png'
embed.sprite.single_image_dim.extend([28,28])
projector.visualize_embeddings(projector_writer,config)

for i in range(max_steps):
    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,'step%03d'%i)
    
    projector_writer.add_summary(summary,i)
    
    
    if i%100==0:
        acc=sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})
        print('Iter '+str(i)+', accuracy '+str(acc))

saver.save(sess,DIR+'/projector/projector/a_model.ckpt',global_step=max_steps)
projector_writer.close()
sess.close()