Question

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（约100 mb）。

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 ='，'）

此命令运行时间太长，我需要找到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花费的时间都要长得多。由于我使用的是随机梯度下降，因此可以解释这一点，因为每个批次都需要从文件中读取

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()

Answer 1

解决方案是使用tensorflow转换TFRecords二进制格式的数据。

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

并将CSV文件转换为TFRecords，请查看this代码段：

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())

从本地文件系统中流式传输（非常）大型文件，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，这对 Gensim smart_open python库很有用：

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

Tensorflow读取CSV - 什么是最好的方法

1 个答案: