Tensorflow讀取CSV - 什么是最好的方法

Question

所以我一直在試用不同的方法來讀取97K行的CSV文件，每行有500個功能（大約100mb）。

我的第一種方法是使用numpy將所有數據讀入內存：

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

這個命令運行了很長時間，我需要找到一個更好的方法來讀取我的文件。

第二種方法是遵循本指南： https ： //www.tensorflow.org/programmers_guide/reading_data

我注意到的第一件事是，每個時代都需要更長的時間來運行。 由於我使用的是隨機梯度下降，因此可以解釋這一點，因為需要從文件中讀取每個批次

有沒有辦法優化第二種方法？

我的代碼（第二種方法）：

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

解決方案可以是使用TFRecords以tensorflow二進制格式轉換數據。

請參閱TensorFlow數據輸入（第1部分）：占位符，Protobufs和隊列

要將CSV文件轉換為TFRecords查看以下代碼段：

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())

雖然要從本地文件系統中流式傳輸（非常）大型文件，在更實際的用例中，從AWS S3，HDFS等遠程存儲器中流式傳輸，但Gensim smart_open python庫可能會有所幫助：

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