Ishitori commented on a change in pull request #12542: [MXNET-949] Module API to Gluon API tutorial URL: https://github.com/apache/incubator-mxnet/pull/12542#discussion_r217570777
########## File path: docs/tutorials/python/module_to_gluon.md ########## @@ -0,0 +1,297 @@ + +# Converting Module API code to the Gluon API + +Sometimes, you find yourself in the situation where the model you want to use has been written using the symbolic Module API rather than the simpler, easier-to-debug, more flexible, imperative Gluon API. In this tutorial, we will give you a comprehensive guide you can use in order to see how you can transform your Module code, to work with the Gluon API. + +The different steps to take into consideration are: + +I) Data loading + +II) Model definition + +III) Loss + +IV) Training Loop + +V) Exporting Models + +In the following section we will look at 1:1 mappings between the Module and the Gluon ways of training a neural networks. + +## I - Data Loading + + +```python +import logging +logging.basicConfig(level=logging.INFO) + +import numpy as np +import mxnet as mx +from mxnet.gluon.data import ArrayDataset, DataLoader +from mxnet.gluon import nn +from mxnet import gluon + +batch_size = 5 +dataset_length = 200 +``` + +#### Module + +When using the Module API we use a [`DataIter`](https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=dataiter#mxnet.io.DataIter), in addition to the data itself, the [`DataIter`](https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=dataiter#mxnet.io.DataIter) contains information about the name of the input symbols. + +Let's create some random data, following the same format as grayscale 28x28 images. + + +```python +train_data = np.random.rand(dataset_length, 28,28).astype('float32') +train_label = np.random.randint(0, 10, (dataset_length,)).astype('float32') +``` + + +```python +data_iter = mx.io.NDArrayIter(data=train_data, label=train_label, batch_size=batch_size, shuffle=False, data_name='data', label_name='softmax_label') +for batch in data_iter: + print(batch.data[0].shape, batch.label[0]) + break; +``` + + (5, 28, 28) + [5. 0. 3. 4. 9.] + <NDArray 5 @cpu(0)> + + +#### Gluon + +With Gluon, the preferred method is to use a [`DataLoader`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader) that make use of a [`Dataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset) to prefetch asynchronously the data. + + +```python +dataset = ArrayDataset(train_data, train_label) +dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False, num_workers=0) +for data, label in dataloader: + print(data.shape, label) + break +``` + + (5, 28, 28) + [5. 0. 3. 4. 9.] + <NDArray 5 @cpu(0)> + + +#### Notable differences + +- Gluon keeps a strict separation between data holding, and data loading / fetching. The `Dataset` role is to hold onto some data, in or out of memory, and the `DataLoader` role is to request certain indices of the dataset, in the main thread or through multi-processing workers. This flexible API allows to efficiently pre-fetch data and separate the concerns. +- In the module API, `DataIter`s are responsible for both holding the data and iterating through it. Some `DataIter` support multi-threading like the [`ImageRecordIter`](https://mxnet.incubator.apache.org/api/python/io/io.html#mxnet.io.ImageRecordIter), while other don't like the `NDArrayIter`. Review comment: Make `NDArrayIter` link as well ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected] With regards, Apache Git Services
