Question

我有一个使用Tensorflow的MNIST数据集的python代码。会议如下：

    with tf.Session() as sess:
        sess.run(tf.initialize_all_variables())
        for epoch in range(hm_epochs):
            epoch_loss = 0
            for _ in range(int(mnist.train.num_examples / batch_size)):
                epoch_x, epoch_y = mnist.train.next_batch(batch_size)
                _, c = sess.run([optimizer, cost], feed_dict={x: epoch_x, y: epoch_y})
                epoch_loss += c
            print('Epoch: ', epoch, ' completed out of: ', hm_epochs, ' loss: ', epoch_loss)
    correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))

    accuracy = tf.reduce_mean(tf.cast(correct, 'float'))

    print('Accuracy:', accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))

该行：

epoch_x, epoch_y = mnist.train.next_batch(batch_size)

每次都会批量生产新的批次。

我的问题是，如果我有自己的CSV文件（列表列表），如何用新行替换这一行，这对我来说是新的批次？我目前的代码如下：

with tf.Session() as sess:
    sess.run(tf.initialize_all_variables())
    for epoch in range(hm_epochs):
        epoch_loss = 0
        for _ in range(len(training_data_list) // batch_size):
            epoch_x, epoch_y = training_data_list.nextbatch(batch_size)
            _, c = sess.run([optimizer, cost], feed_dict={x: epoch_x, y: epoch_y})
            epoch_loss += c
        print('Epoch: ', epoch, ' completed out of: ', hm_epochs, ' loss: ', epoch_loss)
    correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))

    accuracy = tf.reduce_mean(tf.cast(correct, 'float'))

    print('Accuracy:', accuracy.eval({x: inputs, y: targets}))

＆＃34; nextbatch＆＃34;是我定义的函数。但是我收到以下错误：

AttributeError: 'list' object has no attribute 'nextbatch'

我感谢任何建议：D

顺便说一下，＆＃34; training_data_list＆＃34;来自：

stops = open('.../Desktop/stops_train.csv', 'r')
training_data_list  = stops.readlines()
stops.close()

Answer 1

您需要实现一个处理索引的对象。您需要在该对象中实现nextbatch函数。您可以在mnist中查看nextbatch的实现。

使用自己的csv文件python进行批处理

1 个答案: