**//将数据集转化为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();
**//读取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);
将数据集转化为tfrecord并读取tfrecord
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转载自blog.csdn.net/qq_38588316/article/details/82956134
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