Assume your data examples are already read to a python's variable and you would like to read it n times, in batches of given size:

```
import numpy as np
import tensorflow as tf
data = np.array([1, 2, 3, 4, 5])
n = 4
```

To merge data in batches, possibly with random shuffling, you can use `tf.train.batch`

or `tf.train.batch_shuffle`

, but you need to pass to it the tensor that would produce whole data n times:

```
limited_tensor = tf.train.limit_epochs(data, n)
batch = tf.train.shuffle_batch([limited_tensor], batch_size=3, enqueue_many=True, capacity=4)
```

The `limit_epochs`

converts the numpy array to tensor under the hood and returns a tensor producing it n times and throwing an OutOfRangeError afterwards.
The `enqueue_many=True`

argument passed to `shuffle_batch`

denotes that each tensor in the tensor list `[limited_tensor]`

should be interpreted as containing a number of examples. Note that capacity of the batching queue can be smaller than the number of examples in the tensor.

One can process the data as usual:

```
with tf.Session() as sess:
sess.run(tf.initialize_local_variables())
tf.train.start_queue_runners()
try:
while True:
data_batch = sess.run(batch)
# process data
except tf.errors.OutOfRangeError:
pass
```

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