Tensorflow 中TFRecord格式轉換與讀取
阿新 • • 發佈:2019-01-02
把csv格式檔案轉化為TFRecord
Tensorflow提供了TFRecord格式來儲存資料,以下是將csv格式轉化為TFRecord格式的程式碼
import tensorflow as tf
import pandas as pd
import numpy as np
train = pd.read_csv('train.csv')
label = train['label'].values
y_train = train.iloc[:,:-1].values
writer = tf.python_io.TFRecordWriter('train_csv.tfrecords' )
print(y_train[1].shape)
for i in range(y_train.shape[0]):
image_raw = y_train[i].tostring()
example = tf.train.Example(
# 需要主要此處是tf.train.Features,下面的是tf.train.Feature,差別在於一個's'
features=tf.train.Features(
feature = {
'image_raw':tf.train.Feature(bytes_list=tf.train.BytesList(value=[image_raw])),
'label' :tf.train.Feature(int64_list=tf.train.Int64List(value=[label[i]])),
}
)
)
writer.write(record=example.SerializeToString())
writer.close()
將圖片存為TFRecord格式檔案
import tensorflow as tf
from scipy import misc
img = misc.imread('im.jpg')
img_raw = img.tostring()
# 此處假定圖片標籤為1,實際中標籤可能在圖片名,檔案中
label = 1
writer = tf.python_io.TFRecordWriter('img_to_TFRcord.tfrecords')
example = tf.train.Example(
features = tf.train.Features(
feature = {
'img_raw':tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw])),
'label':tf.train.Feature(int64_list=tf.train.Int64List(value=[label]))
}
)
)
writer.write(record=example.SerializeToString())
writer.close()
TFRecord格式檔案的讀取
import tensorflow as tf
import numpy as np
filename_tfrecord = tf.train.string_input_producer(['img_to_TFRcord.tfrecords'])
reader = tf.TFRecordReader()
_,serialized_record = reader.read(filename_tfrecord)
features = tf.parse_single_example(
serialized=serialized_record,
features={
'img_raw':tf.FixedLenFeature([],tf.string),
'label':tf.FixedLenFeature([],tf.int64),
}
)
img = tf.decode_raw(features['img_raw'],tf.uint8)
label = tf.cast(features['label'],tf.int32)
with tf.Session() as sess:
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess,coord=coord)
image, label = sess.run([img, label])
print(image.shape)
print(label.shape)