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Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

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# Model API
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The model API provides a simplified way to train neural networks using common best practices.
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It's a thin wrapper built on top of the [ndarray](../python/ndarray/ndarray.md) and [symbolic](../python/symbol/symbol.md)
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modules that make neural network training easy.
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Topics:
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* [Train a Model](#train-a-model)
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* [Save the Model](#save-the-model)
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* [Periodic Checkpoint](#periodic-checkpointing)
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* [Initializer API Reference](#initializer-api-reference)
Bugfix in PandasLogger. Fixing broken link in Docs (#6403) * Fixing copy/paste bug in PandasLogger callback. Importing notebook by default. * Fixing bug in docs table-of-contents for eval metrics.
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* [Evaluation Metric API Reference](#evaluation-metric-api-reference)
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* [Optimizer API Reference](#optimizer-api-reference)
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* [Model API Reference](#model-api-reference)
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## Train the Model
Revamped tutorials and API reference pages. Adding examples from mxnet repo to tutorials sections (#3545) * Fixing broken links and format issues in getting started and architecture pages * Fixing all broken links. Change all references from mxnet.readthedocs.org to mxnet.io. * Re-organized tutorials and api reference documents. Added tutorials from mxnet repo examples folder.
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To train a model, perform two steps: configure the model using the symbol parameter,
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then call ```model.Feedforward.create``` to create the model.
The following example creates a two-layer neural network.
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```python
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# configure a two layer neuralnetwork
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data = mx.symbol.Variable('data')
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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)
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```
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You can also use the `scikit-learn-style` construct and `fit` function to create a model.
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```python
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# create a model using sklearn-style two-step way
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model = mx.model.FeedForward(
softmax,
num_epoch=num_epoch,
learning_rate=0.01)
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model.fit(X=data_set)
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```
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For more information, see [Model API Reference](#model-api-reference).
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## Save the Model
Revamped tutorials and API reference pages. Adding examples from mxnet repo to tutorials sections (#3545) * Fixing broken links and format issues in getting started and architecture pages * Fixing all broken links. Change all references from mxnet.readthedocs.org to mxnet.io. * Re-organized tutorials and api reference documents. Added tutorials from mxnet repo examples folder.
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After the job is done, save your work.
To save the model, you can directly pickle it with Python.
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We also provide `save` and `load` functions.
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```python
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# save a model to mymodel-symbol.json and mymodel-0100.params
prefix = 'mymodel'
iteration = 100
model.save(prefix, iteration)
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# load model back
model_loaded = mx.model.FeedForward.load(prefix, iteration)
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```
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The advantage of these two `save` and `load` functions are that they are language agnostic.
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You should be able to save and load directly into cloud storage, such as Amazon S3 and HDFS.
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## Periodic Checkpointing
Revamped tutorials and API reference pages. Adding examples from mxnet repo to tutorials sections (#3545) * Fixing broken links and format issues in getting started and architecture pages * Fixing all broken links. Change all references from mxnet.readthedocs.org to mxnet.io. * Re-organized tutorials and api reference documents. Added tutorials from mxnet repo examples folder.
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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
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each iteration.
```python
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prefix='models/chkpt'
model = mx.model.FeedForward.create(
softmax,
X=data_set,
iter_end_callback=mx.callback.do_checkpoint(prefix),
...)
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```
You can load the model checkpoint later using ```Feedforward.load```.
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## Use Multiple Devices
Revamped tutorials and API reference pages. Adding examples from mxnet repo to tutorials sections (#3545) * Fixing broken links and format issues in getting started and architecture pages * Fixing all broken links. Change all references from mxnet.readthedocs.org to mxnet.io. * Re-organized tutorials and api reference documents. Added tutorials from mxnet repo examples folder.
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Set ```ctx``` to the list of devices that you want to train on.
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```python
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devices = [mx.gpu(i) for i in range(num_device)]
model = mx.model.FeedForward.create(
softmax,
X=dataset,
ctx=devices,
...)
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```
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Training occurs in parallel on the GPUs that you specify.
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```eval_rst
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.. raw:: html
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<script type="text/javascript" src='../../_static/js/auto_module_index.js'></script>
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```
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## Initializer API Reference
Revamped tutorials and API reference pages. Adding examples from mxnet repo to tutorials sections (#3545) * Fixing broken links and format issues in getting started and architecture pages * Fixing all broken links. Change all references from mxnet.readthedocs.org to mxnet.io. * Re-organized tutorials and api reference documents. Added tutorials from mxnet repo examples folder.
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```eval_rst
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.. automodule:: mxnet.initializer
:members:
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.. raw:: html
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<script>auto_index("initializer-api-reference");</script>
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```
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## Evaluation Metric API Reference
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```eval_rst
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.. automodule:: mxnet.metric
:members:
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.. raw:: html
add autoindexing
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<script>auto_index("evaluation-metric-api-reference");</script>
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```
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## Optimizer API Reference
Revamped tutorials and API reference pages. Adding examples from mxnet repo to tutorials sections (#3545) * Fixing broken links and format issues in getting started and architecture pages * Fixing all broken links. Change all references from mxnet.readthedocs.org to mxnet.io. * Re-organized tutorials and api reference documents. Added tutorials from mxnet repo examples folder.
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```eval_rst
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.. automodule:: mxnet.optimizer
:members:
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.. raw:: html
add autoindexing
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<script>auto_index("optimizer-api-reference");</script>
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```
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## Model API Reference
Revamped tutorials and API reference pages. Adding examples from mxnet repo to tutorials sections (#3545) * Fixing broken links and format issues in getting started and architecture pages * Fixing all broken links. Change all references from mxnet.readthedocs.org to mxnet.io. * Re-organized tutorials and api reference documents. Added tutorials from mxnet repo examples folder.
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```eval_rst
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.. automodule:: mxnet.model
:members:
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.. raw:: html
add autoindexing
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<script>auto_index("model-api-reference");</script>
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```
Revamped tutorials and API reference pages. Adding examples from mxnet repo to tutorials sections (#3545) * Fixing broken links and format issues in getting started and architecture pages * Fixing all broken links. Change all references from mxnet.readthedocs.org to mxnet.io. * Re-organized tutorials and api reference documents. Added tutorials from mxnet repo examples folder.
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## Next Steps
Feature branch gluon (#8209) * added Gluon Menu, changed Community menu and changed some texts in the documentation * fixed the broken link in Glue index page * fixed the typo in the link * updated Gluon index page and re-positioned Community menu bar * Update navbar.html
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* See [Symbolic API](../python/symbol/symbol.md) for operations on NDArrays that assemble neural networks from layers.
* See [IO Data Loading API](../python/io/io.md) for parsing and loading data.
* See [NDArray API](../python/ndarray/ndarray.md) for vector/matrix/tensor operations.
* See [KVStore API](../python/kvstore/kvstore.md) for multi-GPU and multi-host distributed training.
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