TensorFlow将数据加载到tf.Dataset需要太长时间

我的GPU有3gb的内存,RAM有32gb的内存。每个半数据集的大小为20GB。我的硬盘有足够的可用空间(超过1 TB)。

我的尝试如下。我创建了一个可初始化的 tf.Dataset

但是,这非常慢,因为从硬盘加载数据需要很长时间,而且每次用这些数据初始化数据集也需要很长时间。

有没有更有效的方法来做到这一点?

在加载数据集的另一半之前,我已经尝试过对数据集的每一半进行多个阶段的培训,这要快得多,但这会使验证数据的性能差得多。这大概是因为模型在每一半上都过拟合,然后没有推广到另一半的数据。

import tensorflow as tf
import numpy as np
import time

# Create and save 2 datasets of test NumPy data
dataset_num_elements = 100000
element_dim = 10000
batch_size = 50
test_data = np.zeros([2, int(dataset_num_elements * 0.5), element_dim], dtype=np.float32)
np.savez('test_data_1.npz', x=test_data[0])
np.savez('test_data_2.npz', x=test_data[1])

# Create the TensorFlow dataset
data_placeholder = tf.placeholder(tf.float32, [int(dataset_num_elements * 0.5), element_dim])
dataset = tf.data.Dataset.from_tensor_slices(data_placeholder)
dataset = dataset.shuffle(buffer_size=dataset_num_elements)
dataset = dataset.repeat()
dataset = dataset.batch(batch_size=batch_size)
dataset = dataset.prefetch(1)
iterator = dataset.make_initializable_iterator()
next_element = iterator.get_next()
init_op = iterator.initializer

num_batches = int(dataset_num_elements / batch_size)

with tf.Session() as sess:
    while True:
        for dataset_section in range(2):
            # Load the data from the hard drive
            t1 = time.time()
            print('Loading')
            loaded_data = np.load('test_data_' + str(dataset_section + 1) + '.npz')
            x = loaded_data['x']
            print('Loaded')
            t2 = time.time()
            loading_time = t2 - t1
            print('Loading time = ' + str(loading_time))
            # Initialize the dataset with this loaded data
            t1 = time.time()
            sess.run(init_op, feed_dict={data_placeholder: x})
            t2 = time.time()
            initialization_time = t2 - t1
            print('Initialization time = ' + str(initialization_time))
            # Read the data in batches
            for i in range(num_batches):
                x = sess.run(next_element)