projector_writer tf.summary.FileWriter(DIR projector/projector ,sess.graph) #定义路径 图结构
saver tf.train.Saver()
config projector.ProjectorConfig() #定义配置项
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]) #按照28*28像素进行切分
projector.visualize_embeddings(projector_writer,config)
for i in range(max_steps):
#每个批次100个样本
batch_xs,batch_ys mnist.train.next_batch(100)
run_options tf.RunOptions(trace_level tf.RunOption.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) ,Testing Accuracy str(acc))
saver.save(sess,DIR projector/projector/a_model.ckpt ,global_step max_steps)
projector_writer.close()
sess.close()
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