Yuan Gao
8 years ago
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GitHub
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import paddle.v2 as paddle
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def main():
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paddle.init(use_gpu=False, trainer_count=1)
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# define network topology
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images = paddle.layer.data(
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name='pixel', type=paddle.data_type.dense_vector(784))
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label = paddle.layer.data(
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name='label', type=paddle.data_type.integer_value(10))
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hidden1 = paddle.layer.fc(input=images, size=200)
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hidden2 = paddle.layer.fc(input=hidden1, size=200)
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inference = paddle.layer.fc(input=hidden2,
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size=10,
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act=paddle.activation.Softmax())
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cost = paddle.layer.classification_cost(input=inference, label=label)
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parameters = paddle.parameters.create(cost)
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adam_optimizer = paddle.optimizer.Adam(learning_rate=0.01)
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trainer = paddle.trainer.SGD(cost=cost,
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parameters=parameters,
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update_equation=adam_optimizer)
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def event_handler(event):
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if isinstance(event, paddle.event.EndIteration):
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if event.batch_id % 1000 == 0:
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result = trainer.test(reader=paddle.reader.batched(
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paddle.dataset.mnist.test(), batch_size=256))
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print "Pass %d, Batch %d, Cost %f, %s, Testing metrics %s" % (
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event.pass_id, event.batch_id, event.cost, event.metrics,
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result.metrics)
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else:
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pass
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trainer.train(
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reader=paddle.reader.batched(
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paddle.reader.shuffle(
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paddle.dataset.mnist.train(), buf_size=8192),
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batch_size=32),
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event_handler=event_handler)
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# output is a softmax layer. It returns probabilities.
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# Shape should be (100, 10)
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probs = paddle.infer(
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output=inference,
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parameters=parameters,
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reader=paddle.reader.batched(
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paddle.reader.firstn(
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paddle.reader.map_readers(lambda item: (item[0], ),
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paddle.dataset.mnist.test()),
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n=100),
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batch_size=32))
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print probs.shape
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if __name__ == '__main__':
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main()
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Load Diff
@ -0,0 +1,161 @@
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# Python Data Reader Design Doc
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At training and testing time, PaddlePaddle programs need to read data. To ease the users' work to write data reading code, we define that
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- A *reader* is a function that reads data (from file, network, random number generator, etc) and yields data items.
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- A *reader creator* is a function that returns a reader function.
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- A *reader* decorator is a function, which accepts one or more readers, and returns a reader.
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and provide frequently used reader creators and reader decorators.
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## Data Reader Interface
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Indeed, *data reader* doesn't have to be a function that reads and yields data items. It can be any function with no parameter that creates a iterable (anything can be used in `for x in iterable`):
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```
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iterable = data_reader()
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```
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Element produced from the iterable should be a **single** entry of data, **not** a mini batch. That entry of data could be a single item, or a tuple of items. Item should be of [supported type](http://www.paddlepaddle.org/doc/ui/data_provider/pydataprovider2.html?highlight=dense_vector#input-types) (e.g., numpy 1d array of float32, int, list of int)
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An example implementation for single item data reader creator:
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```python
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def reader_creator_random_image(width, height):
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def reader():
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while True:
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yield numpy.random.uniform(-1, 1, size=width*height)
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return reader
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```
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An example implementation for multiple item data reader creator:
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```python
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def reader_creator_random_imageand_label(widht, height, label):
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def reader():
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while True:
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yield numpy.random.uniform(-1, 1, size=width*height), label
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return reader
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```
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## Usage
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data reader, mapping from item(s) read to data layer, batch size and number of total pass will be passed into `paddle.train`:
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```python
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# two data layer is created:
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image_layer = paddle.layer.data("image", ...)
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label_layer = paddle.layer.data("label", ...)
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# ...
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paddle.train(paddle.dataset.mnist, {"image":0, "label":1}, 128, 10, ...)
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```
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## Data Reader Decorator
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*Data reader decorator* takes a single or multiple data reader, returns a new data reader. It is similar to a [python decorator](https://wiki.python.org/moin/PythonDecorators), but it does not use `@` syntax.
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Since we have a strict interface for data readers (no parameter, return a single data item). Data reader can be used flexiable via data reader decorators. Following are a few examples:
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### Prefetch Data
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Since reading data may take time and training can not proceed without data. It is generally a good idea to prefetch data.
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Use `paddle.reader.buffered` to prefetch data:
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```python
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buffered_reader = paddle.reader.buffered(paddle.dataset.mnist, 100)
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```
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`buffered_reader` will try to buffer (prefetch) `100` data entries.
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### Compose Multiple Data Readers
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For example, we want to use a source of real images (reusing mnist dataset), and a source of random images as input for [Generative Adversarial Networks](https://arxiv.org/abs/1406.2661).
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We can do:
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```python
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def reader_creator_random_image(width, height):
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def reader():
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while True:
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yield numpy.random.uniform(-1, 1, size=width*height)
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return reader
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def reader_creator_bool(t):
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def reader:
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while True:
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yield t
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return reader
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true_reader = reader_creator_bool(True)
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false_reader = reader_creator_bool(False)
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reader = paddle.reader.compose(paddle.dataset.mnist, data_reader_creator_random_image(20, 20), true_reader, false_reader)
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# Skipped 1 because paddle.dataset.mnist produces two items per data entry.
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# And we don't care second item at this time.
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paddle.train(reader, {"true_image":0, "fake_image": 2, "true_label": 3, "false_label": 4}, ...)
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```
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### Shuffle
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Given shuffle buffer size `n`, `paddle.reader.shuffle` will return a data reader that buffers `n` data entries and shuffle them before a data entry is read.
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Example:
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```python
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reader = paddle.reader.shuffle(paddle.dataset.mnist, 512)
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```
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## Q & A
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### Why return only a single entry, but not a mini batch?
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If a mini batch is returned, data reader need to take care of batch size. But batch size is a concept for training, it makes more sense for user to specify batch size as a parameter for `train`.
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Practically, always return a single entry make reusing existing data readers much easier (e.g., if existing reader return not a single entry but 3 entries, training code will be more complex because it need to handle cases like batch size 2).
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### Why use a dictionary but not a list to provide mapping?
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We decided to use dictionary (`{"image":0, "label":1}`) instead of list (`["image", "label"]`) is because that user can easily resue item (e.g., using `{"image_a":0, "image_b":0, "label":1}`) or skip item (e.g., using `{"image_a":0, "label":2}`).
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### How to create custom data reader creator
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```python
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def image_reader_creator(image_path, label_path, n):
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def reader():
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f = open(image_path)
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l = open(label_path)
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images = numpy.fromfile(
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f, 'ubyte', count=n * 28 * 28).reshape((n, 28 * 28)).astype('float32')
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images = images / 255.0 * 2.0 - 1.0
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labels = numpy.fromfile(l, 'ubyte', count=n).astype("int")
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for i in xrange(n):
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yield images[i, :], labels[i] # a single entry of data is created each time
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f.close()
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l.close()
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return reader
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# images_reader_creator creates a reader
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reader = image_reader_creator("/path/to/image_file", "/path/to/label_file", 1024)
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paddle.train(reader, {"image":0, "label":1}, ...)
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```
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### How is `paddle.train` implemented
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An example implementation of paddle.train could be:
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```python
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def make_minibatch(reader, minibatch_size):
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def ret():
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r = reader()
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buf = [r.next() for x in xrange(minibatch_size)]
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while len(buf) > 0:
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yield buf
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buf = [r.next() for x in xrange(minibatch_size)]
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return ret
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def train(reader, mapping, batch_size, total_pass):
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for pass_idx in range(total_pass):
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for mini_batch in make_minibatch(reader): # this loop will never end in online learning.
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do_forward_backward(mini_batch, mapping)
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```
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