python – Tensorflow读取CSV – 什么是最好的方法

2023年10月22日 263次阅读

所以我一直在试用不同的方法来读取97K行的CSV文件,每行有500个功能(大约100mb).

我的第一种方法是使用numpy将所有数据读入内存：

raw_data = genfromtxt(filename,dtype = numpy.int32,delimiter =’,’)

这个命令运行了很长时间,我需要找到一个更好的方法来读取我的文件.

第二种方法是遵循本指南：
https://www.tensorflow.org/programmers_guide/reading_data

我注意到的第一件事是,每个时代都需要更长的时间来运行.由于我使用的是随机梯度下降,因此可以解释这一点,因为需要从文件中读取每个批次

有没有办法优化第二种方法？

我的代码(第二种方法)：

reader = tf.TextLineReader()
filename_queue = tf.train.string_input_producer([filename])
_, csv_row = reader.read(filename_queue) # read one line
data = tf.decode_csv(csv_row, record_defaults=rDefaults) # use defaults for this line (in case of missing data)

labels = data[0]
features = data[labelsSize:labelsSize+featuresSize]

# minimum number elements in the queue after a dequeue, used to ensure 
# that the samples are sufficiently mixed
# I think 10 times the BATCH_SIZE is sufficient
min_after_dequeue = 10 * batch_size

# the maximum number of elements in the queue
capacity = 20 * batch_size

# shuffle the data to generate BATCH_SIZE sample pairs
features_batch, labels_batch = tf.train.shuffle_batch([features, labels], batch_size=batch_size, num_threads=10, capacity=capacity, min_after_dequeue=min_after_dequeue)

*
*
*
*

coordinator = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coordinator)

try:
 # And then after everything is built, start the training loop.
 for step in xrange(max_steps):
  global_step = step + offset_step
  start_time = time.time()

  # Run one step of the model.  The return values are the activations
  # from the `train_op` (which is discarded) and the `loss` Op.  To
  # inspect the values of your Ops or variables, you may include them
  # in the list passed to sess.run() and the value tensors will be
  # returned in the tuple from the call.
  _, __, loss_value, summary_str = sess.run([eval_op_train, train_op, loss_op, summary_op])

except tf.errors.OutOfRangeError:
  print('Done training -- epoch limit reached')
finally:
  coordinator.request_stop()

# Wait for threads to finish.
coordinator.join(threads)
sess.close()

最佳答案 解决方案可以是使用TFRecords以tensorflow二进制格式转换数据.

见TensorFlow Data Input (Part 1): Placeholders, Protobufs & Queues

并将CSV文件转换为TFRecords,请看this片段：

csv = pandas.read_csv("your.csv").values
with tf.python_io.TFRecordWriter("csv.tfrecords") as writer:
    for row in csv:
        features, label = row[:-1], row[-1]
        example = tf.train.Example()
        example.features.feature["features"].float_list.value.extend(features)
        example.features.feature["label"].int64_list.value.append(label)
        writer.write(example.SerializeToString())

虽然要从本地文件系统中流式传输(非常)大型文件,但是在一个更实际的用例中,从AWS S3,HDFS等远程存储器中流式传输,这可能对 Gensim smart_open python库有所帮助：

    # stream lines from an S3 object
    for line in smart_open.smart_open('s3://mybucket/mykey.txt'):
           print line
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