You are viewing a plain text version of this content. The canonical link for it is here.
Posted to commits@mxnet.apache.org by gi...@git.apache.org on 2017/08/02 17:59:58 UTC
[GitHub] szha commented on a change in pull request #7284: add doc for gluon, sym/nd contrib
szha commented on a change in pull request #7284: add doc for gluon, sym/nd contrib
URL: https://github.com/apache/incubator-mxnet/pull/7284#discussion_r130950566
##########
File path: docs/api/python/gluon.md
##########
@@ -21,58 +21,379 @@ in Python and then deploy with symbolic graph in C++ and Scala.
## Parameter
```eval_rst
-.. currentmodule:: mxnet.gluon
+.. autosummary::
+ :nosignatures:
+
+ Parameter
+ ParameterDict
```
+## Containers
+
```eval_rst
-.. currentmodule:: mxnet.gluon
-.. autoclass:: mxnet.gluon.Parameter
- :members:
-.. autoclass:: mxnet.gluon.ParameterDict
- :members:
+.. autosummary::
+ :nosignatures:
+
+ Block
+ HybridBlock
+ SymbolBlock
```
+## Neural Network Layers
+
+```eval_rst
+.. currentmodule:: mxnet.gluon.nn
+```
+
+### Containers
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ Sequential
+ HybridSequential
+```
+
+
+### Basic Layers
+
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ Dense
+ Activation
+ Dropout
+ BatchNorm
+ LeakyReLU
+ Embedding
+```
+
+
+### Convolutional Layers
+
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ Conv1D
+ Conv2D
+ Conv3D
+ Conv1DTranspose
+ Conv2DTranspose
+ Conv3DTranspose
+```
+
+
+
+### Pooling Layers
-## Containers
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ MaxPool1D
+ MaxPool2D
+ MaxPool3D
+ AvgPool1D
+ AvgPool2D
+ AvgPool3D
+ GlobalMaxPool1D
+ GlobalMaxPool2D
+ GlobalMaxPool3D
+ GlobalAvgPool1D
+ GlobalAvgPool2D
+ GlobalAvgPool3D
+```
+
+
+
+## Recurrent Layers
+
+```eval_rst
+.. currentmodule:: mxnet.gluon.rnn
+```
+
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ RecurrentCell
+ RNN
+ LSTM
+ GRU
+ RNNCell
+ LSTMCell
+ GRUCell
+ SequentialRNNCell
+ BidirectionalCell
+ DropoutCell
+ ZoneoutCell
+ ResidualCell
+```
+
+
+## Trainer
```eval_rst
.. currentmodule:: mxnet.gluon
-.. autoclass:: mxnet.gluon.Block
- :members:
- .. automethod:: forward
-.. autoclass:: mxnet.gluon.HybridBlock
- :members:
+.. autosummary::
+ :nosignatures:
- .. automethod:: hybrid_forward
+ Trainer
```
-## Neural Network Layers
+
+## Loss functions
```eval_rst
-.. currentmodule:: mxnet.gluon.nn
+.. currentmodule:: mxnet.gluon.loss
```
-### Containers
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ L2Loss
+ L1Loss
+ SoftmaxCrossEntropyLoss
+ KLDivLoss
+```
+
+## Utilities
```eval_rst
-.. currentmodule:: mxnet.gluon.nn
+.. currentmodule:: mxnet.gluon.utils
+```
- .. automethod:: __call__
-.. autoclass:: mxnet.gluon.nn.Sequential
- :members:
-.. autoclass:: mxnet.gluon.nn.HybridSequential
- :members:
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ split_data
+ split_and_load
+ clip_global_norm
```
+## Data
-### Basic Layers
+```eval_rst
+.. currentmodule:: mxnet.gluon.data
+```
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ Dataset
+ ArrayDataset
+ RecordFileDataset
+ ImageRecordDataset
+```
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ Sampler
+ SequentialSampler
+ RandomSampler
+ BatchSampler
+```
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ DataLoader
+```
+
+### Vision
+
+```eval_rst
+.. currentmodule:: mxnet.gluon.data.vision
+```
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ MNIST
+ CIFAR10
+```
+
+## Model Zoo
+
+Model zoo provides pre-defined and pre-trained models to help bootstrap machine learning applications.
+
+### Vision
+
+```eval_rst
+.. currentmodule:: mxnet.gluon.model_zoo.vision
+```
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ get_model
+```
+
+#### ResNet
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ resnet18_v1
+ resnet34_v1
+ resnet50_v1
+ resnet101_v1
+ resnet152_v1
+ resnet18_v2
+ resnet34_v2
+ resnet50_v2
+ resnet101_v2
+ resnet152_v2
+```
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ ResNetV1
+ ResNetV2
+ BasicBlockV1
Review comment:
Users can use the blocks to construct their own ResNet outside of predefined models (resnet1001 e.g.)
----------------------------------------------------------------
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:
users@infra.apache.org
With regards,
Apache Git Services