將資料集轉化為tfrecord並讀取tfrecord
阿新 • • 發佈:2018-12-13
**//將資料集轉化為tfrecord** import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data import numpy as np def _int64_feature(value): return tf.train.Feature(int64_list=tf.train.Int64List(value=[value])); def _bytes_feature(value): return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value])); mnist=input_data.read_data_sets("../mnist",dtype=tf.uint8,one_hot=True); images=mnist.train.images; labels=mnist.train.labels; pixels=images.shape[1]; num_examples=mnist.train.num_examples; filename="log/output.tfrecords"; writer=tf.python_io.TFRecordWriter(filename); for index in range(num_examples): image_raw=images[index].tostring(); example=tf.train.Example(features=tf.train.Features(feature={ 'pixels':_int64_feature(pixels), 'labels':_int64_feature(np.argmax(labels[index])), 'image_raw':_bytes_feature(image_raw) })); writer.write(example.SerializeToString()); writer.close(); **//讀取tfrecord** import tensorflow as tf reader=tf.TFRecordReader(); filename_queue=tf.train.string_input_producer(["log/output.tfrecords"]); _,serialized_example=reader.read(filename_queue); features=tf.parse_single_example( serialized_example, features={ 'image_raw':tf.FixedLenFeature([],tf.string), 'pixels':tf.FixedLenFeature([],tf.int64), 'labels':tf.FixedLenFeature([],tf.int64), }); images=tf.decode_raw(features['image_raw'],tf.uint8); labels=tf.cast(features['labels'],tf.int32); pixels=tf.cast(features['pixels'],tf.int32); with tf.Session() as sess: # coord=tf.train.Coordinator(); # threads=tf.train.start_queue_runners(sess=sess,coord=coord); for i in range(10): image,label,pixel=sess.run([images,labels,pixels]); print(image);