Question

可能有人问这个问题，但我没找到。
从数据集中不断获取批量数据的最简单方法是什么？是否有内置张量流功能？
例如：

for i in num_trains: x_batch, y_batch = get_batch(x_train, y_train, batch_size) sess.run(train_step, feed_dict={x:x_batch,y:y_batch})

如果没有这样的内置功能，你会如何实现它？我试过自己，但我无法弄清楚每次调用函数时如何获得与之前版本不同的新批次。

谢谢！

Answer 1

您可以尝试：

# Feed batch data
def get_batch(inputX, inputY, batch_size):
   duration = len(inputX)
   for i in range(0,duration//batch_size):
     idx = i*batch_size
     yield inputX[idx:idx+batch_size], inputY[idx:idx+batch_size]

你也可以使用tensorflow＆＃39; dataset API：

dataset = tf.data.Dataset.from_tensor_slices((train_x, train_y))
dataset = dataset.batch(batch_size)

获取批次：

  X = np.arange(100)
  Y = X
 batch = get_batch(X, Y, 5)
 batch_x, batch_y = next(batch)
 print(batch_x, batch_y)
 #[0 1 2 3 4] [0 1 2 3 4]

 batch_x, batch_y = next(batch)
 print(batch_x, batch_y)
 #[5 6 7 8 9] [5 6 7 8 9]

通常，为了遍历多个epochs的数据集，您可以执行以下操作：

 for epoch in range(number of epoch):
    for step in range(size_of_dataset//batch_size):
        for x_batch, y_batch in get_batch(x_train, y_train, batch_size):
           sess.run(train_step, feed_dict={x:x_batch,y:y_batch})

使用dataset API：

  dataset = tf.data.Dataset.from_tensor_slices((X, Y))
  dataset = dataset.batch(5)
  iterator = dataset.make_initializable_iterator()
  train_x, train_y = iterator.get_next()
  with tf.Session() as sess:
    sess.run(iterator.initializer)
   for i in range(2):
       print(sess.run([train_x, train_y]))
   #[array([0, 1, 2, 3, 4]), array([0, 1, 2, 3, 4])]
   #[array([5, 6, 7, 8, 9]), array([5, 6, 7, 8, 9])]

在tensorflow中获取批次

1 个答案: