docs/api/python/model.md

# MXNet Python Model API

The model API provides a simplified way to train neural networks using common best practices.
It's a thin wrapper built on top of the [ndarray](ndarray.md) and [symbolic](symbol.md)
modules that makes neural network training easy.

Topics:

* [Train a Model](#train-a-model)
* [Save the Model](#save-the-model)
* [Periodic Checkpoint](#periodic-checkpointing)
* [Initializer API Reference](#initializer-api-reference)
* [Evaluation Metric API Reference](#initializer-api-reference)
* [Optimizer API Reference](#optimizer-api-reference)
* [Model API Reference](#model-api-reference)

## Train the Model

To train a model, perform two steps: configure the model  using the symbol parameter,
then call ```model.Feedforward.create``` to create the model.
The following example creates a two-layer neural network.

```python
    # configure a two layer neuralnetwork
    data = mx.symbol.Variable('data')
    fc1 = mx.symbol.FullyConnected(data, name='fc1', num_hidden=128)
    act1 = mx.symbol.Activation(fc1, name='relu1', act_type='relu')
    fc2 = mx.symbol.FullyConnected(act1, name='fc2', num_hidden=64)
    softmax = mx.symbol.SoftmaxOutput(fc2, name='sm')
    # create a model
    model = mx.model.FeedForward.create(
         softmax,
         X=data_set,
         num_epoch=num_epoch,
         learning_rate=0.01)
```
You can also use the scikit-learn-style construct and fit function to create a model.

```python
    # create a model using sklearn-style two step way
    model = mx.model.FeedForward(
         softmax,
         num_epoch=num_epoch,
         learning_rate=0.01)

    model.fit(X=data_set)
```
For more information, see [Model API Reference](#model-api-reference).

## Save the Model

After the job is done, save your work.
To save the model, you can directly pickle it with Python.
We also provide save and load functions.

```python
    # save a model to mymodel-symbol.json and mymodel-0100.params
    prefix = 'mymodel'
    iteration = 100
    model.save(prefix, iteration)

    # load model back
    model_loaded = mx.model.FeedForward.load(prefix, iteration)
```
The advantage of these save and load functions is that they are language agnostic.
You should be able to save and load directly into cloud storage, such as Amazon S3 and HDFS.

##  Periodic Checkpointing

We recommend checkpointing your model after each iteration.
To do this, add a checkpoint callback ```do_checkpoint(path)``` to the function.
The training process automatically checkpoints the specified location after
each iteration.

```python
    prefix='models/chkpt'
    model = mx.model.FeedForward.create(
         softmax,
         X=data_set,
         iter_end_callback=mx.callback.do_checkpoint(prefix),
         ...)
```
You can load the model checkpoint later using ```Feedforward.load```.

## Use Multiple Devices

Set ```ctx``` to the list of devices that you want to train on.

```python
    devices = [mx.gpu(i) for i in range(num_device)]
    model = mx.model.FeedForward.create(
         softmax,
         X=dataset,
         ctx=devices,
         ...)
```
Training occurs in parallel on the GPUs that you specify.

```eval_rst
    .. raw:: html

        <script type="text/javascript" src='../../_static/js/auto_module_index.js'></script>
```


## Initializer API Reference


```eval_rst
    .. automodule:: mxnet.initializer
        :members:

    .. raw:: html

        <script>auto_index("initializer-api-reference");</script>
```

## Evaluation Metric API Reference


```eval_rst
    .. automodule:: mxnet.metric
        :members:

    .. raw:: html

        <script>auto_index("evaluation-metric-api-reference");</script>
```

## Optimizer API Reference


```eval_rst
    .. automodule:: mxnet.optimizer
        :members:

    .. raw:: html

        <script>auto_index("optimizer-api-reference");</script>
```

## Model API Reference


```eval_rst
    .. automodule:: mxnet.model
        :members:

    .. raw:: html

        <script>auto_index("model-api-reference");</script>
```

## Next Steps
* See [Symbolic API](symbol.md) for operations on NDArrays that assemble neural networks from layers.
* See [IO Data Loading API](io.md) for parsing and loading data.
* See [NDArray API](ndarray.md) for vector/matrix/tensor operations.
* See [KVStore API](kvstore.md) for multi-GPU and multi-host distributed training.