我一直在尝试不同的方法来读取一个包含97K行,每行500个特征(大约100 MB)的CSV文件。
我的第一种方法是使用numpy将所有数据都读入内存:
raw_data = genfromtxt(filename, dtype=numpy.int32, delimiter=',')
这个命令运行时间太长了,所以我需要找到更好的读取文件的方法。
第二种方法是参照以下指南:https://www.tensorflow.org/programmers_guide/reading_data
我注意到的第一件事是每个epoch的运行时间都要长得多。由于我正在使用随机梯度下降,这可以解释为每个批次都需要从文件中读取。
是否有一种方法来优化这第二种方法呢?
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()