TensorFlowのtfrecordsファイルとqueueキューを組み合わせてデータを読み込む方法
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TensorFlowのtfrecordsファイルとqueueキューを組み合わせてデータを読み込む方法
自分でデータセットを作ったのですが、読み方がなんとなく読めないので、以下の記事を参考にしてみました.https://blog.csdn.net/julialove102123/article/details/80085871
私のテストファイル:
import os
import tensorflow as tf
from PIL import Image
def read_tfRecord(file_tfRecord): # .tfrecords
queue = tf.train.string_input_producer([file_tfRecord])
reader = tf.TFRecordReader()
_, serialized_example = reader.read(queue)
features = tf.parse_single_example(
serialized_example,
features={
'img_raw': tf.FixedLenFeature([], tf.string),
'label': tf.FixedLenFeature([], tf.int64)
}
)
image = tf.decode_raw(features['img_raw'], tf.uint8)
image = tf.reshape(image, [50, 50, 1])
image = tf.cast(image, tf.float32)
image = tf.image.per_image_standardization(image)
label = tf.cast(features['label'], tf.int64) #
return image, label
traindata, trainlabel = read_tfRecord("./tmp_train.tfrecords")
image_batch, label_batch = tf.train.shuffle_batch([traindata, trainlabel],
batch_size=100, capacity=2000, min_after_dequeue=1000)
with tf.Session() as sess:
sess.run(tf.local_variables_initializer())
sess.run(tf.global_variables_initializer())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
train_steps = 10
try:
while not coord.should_stop(): # True
example, label = sess.run([image_batch, label_batch])
print(example.shape, label)
train_steps -= 1
print(train_steps)
if train_steps <= 0:
coord.request_stop() #
except tf.errors.OutOfRangeError:
print('Done training -- epoch limit reached')
finally:
# When done, ask the threads to stop.
coord.request_stop()
# And wait for them to actually do it.
coord.join(threads)