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<img src="https://img.shields.io/badge/SAS%20Viya-4.0-blue.svg?&colorA=0b5788&style=for-the-badge&logoWidth=30&logo=data:image/png;base64,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"
alt="SAS Viya Version"/>
</a>

<a href="https://www.sas.com/en_us/software/visual-data-mining-machine-learning.html">
<img src="https://img.shields.io/badge/pip_install_sas_dlpy-blue.svg?&style=for-the-badge&colorA=254f73" alt="Python Version">

Expand All @@ -19,30 +19,34 @@
</div>

### Overview
DLPy is a high-level Python library for the SAS Deep learning features
available in SAS Viya. DLPy is designed to provide an efficient way to
apply deep learning methods to image, text, and audio data. DLPy APIs
are created following the [Keras](https://keras.io/) APIs with a touch

DLPy is a high-level Python library for the SAS Deep learning features
available in SAS Viya. DLPy is designed to provide an efficient way to
apply deep learning methods to image, text, and audio data. DLPy APIs
are created following the [Keras](https://keras.io/) APIs with a touch
of [PyTorch](https://pytorch.org/) flavor.

### What's Recently Added
* DLModelzoo action support
* Real-time plot for hyper-parameter tuning with DLModelzoo
* New examples for APIs with DLModelzoo
* PNG/base64 output format for segmentation models
* Additional pre-defined network architectures such as ENet and Efficient-Net
### Recently Added Features

* DLPy now supports the DLModelzoo action through the use of `MZModel`.
* Real-time plots for hyper-parameter tuning are available with DLModelzoo.
* New examples are available for APIs with DLModelzoo.
* Segmentation models can produce PNG/base64 output.
* Additional pre-defined network architectures are available. Examples include ENet and Efficient-Net.

### Prerequisites
- Python version 3.3 or greater is required
- Install SAS [Scripting Wrapper for Analytics Transfer (SWAT)](https://github.com/sassoftware/python-swat) for Python using `pip install swat` or `conda install -c sas-institute swat`
- Access to a SAS Viya 4.0 environment with [Visual Data Mining and Machine Learning](https://www.sas.com/en_us/software/visual-data-mining-machine-learning.html) (VDMML) is required
- To use timeseries APIs, access to either [SAS Econometrics](https://www.sas.com/en_us/software/econometrics.html) or [SAS Visual Forecasting](https://www.sas.com/en_us/software/visual-forecasting.html) is required
- A user login to your SAS Viya back-end is required. See your system administrator for details if you do not have a SAS Viya account.
- It is recommended that you install the open source graph visualization software called [Graphviz](https://www.graphviz.org/download/) to enable graphic visualizations of the DLPy deep learning models
- Install DLPy using `pip install sas-dlpy` or `conda install -c sas-institute sas-dlpy`

#### SAS Viya and VDMML versions vs. DLPY versions
DLPy versions are aligned with the SAS Viya and VDMML versions.

* You must use Python version 3.3 or greater.
* You must install SAS [Scripting Wrapper for Analytics Transfer (SWAT)](https://github.com/sassoftware/python-swat) for Python. You can install the package from PyPI by using the command `pip install swat` or from the SAS conda repository by using `conda install -c sas-institute swat`.
* You must have access to a SAS Viya 4.0 environment that has [Machine Learning](https://www.sas.com/en_us/software/machine-learning-deep-learning.html) licensed.
* To use time series APIs, you must have access to either [SAS Econometrics](https://www.sas.com/en_us/software/econometrics.html) or [SAS Visual Forecasting](https://www.sas.com/en_us/software/visual-forecasting.html).
* You must have a user login to your SAS Viya back-end. See your system administrator for details if you do not have a SAS Viya account.
* It is recommended that you install the open source graph visualization software called [Graphviz](https://www.graphviz.org/download/) to enable graphic visualizations of the DLPy deep learning models.
* Install DLPy using `pip install sas-dlpy` or `conda install -c sas-institute sas-dlpy`.

#### SAS Viya and DLPY Versions

DLPy versions are aligned with SAS Viya versions.
Below is the versions matrix.

<table>
Expand Down Expand Up @@ -74,11 +78,12 @@ Below is the versions matrix.
The table above can be read as follows: DLPy versions between 1.0 (inclusive)
to 1.1 (exclusive) are designed to work with the SAS Viya 3.4.

#### External Libraries ####
#### External Libraries

The following versions of external libraries are supported:
- ONNX: versions >= 1.5.0
- Keras: versions >= 2.1.3

* ONNX: versions >= 1.5.0
* Keras: versions >= 2.1.3

### Getting Started

Expand All @@ -90,7 +95,7 @@ Note: The default CAS port is 5570.
>>> import swat
>>> sess = swat.CAS('mycloud.example.com', 5570)

Next, import the DLPy package, and then build a simple convolutional
Next, import the DLPy package, and then build a simple convolutional
neural network (CNN) model.

Import DLPy model functions:
Expand All @@ -111,7 +116,7 @@ Define an input layer to add to `model1`:

NOTE: Input layer added.

Add a 2D convolution layer and a pooling layer:
Add a 2-D convolution layer and a pooling layer:

# Add 2-Dimensional Convolution Layer to model1
# that has 8 filters and a kernel size of 7.
Expand All @@ -125,8 +130,8 @@ Add a 2D convolution layer and a pooling layer:
>>> model1.add(Pooling(2))

NOTE: Pooling layer added.
Add an additional pair of 2D convolution and pooling layers:

Add an additional pair of 2-D convolution and pooling layers:

# Add another 2D convolution Layer that has 8 filters and a kernel size of 7

Expand All @@ -139,15 +144,15 @@ Add an additional pair of 2D convolution and pooling layers:
>>> model1.add(Pooling(2))

NOTE: Pooling layer added.

Add a fully connected layer:

# Add Fully-Connected Layer with 16 units

>>> model1.add(Dense(16))

NOTE: Fully-connected layer added.

Finally, add the output layer:

# Add an output layer that has 2 nodes and uses
Expand All @@ -158,38 +163,35 @@ Finally, add the output layer:
NOTE: Output layer added.
NOTE: Model compiled successfully


### Additional Resources
- DLPy examples: https://github.com/sassoftware/python-dlpy/tree/master/examples
- DLPy API documentation [sassoftware.github.io/python-dlpy](https://sassoftware.github.io/python-dlpy/).
- [SAS SWAT for Python](http://github.com/sassoftware/python-swat/)
- [SAS ESPPy](https://github.com/sassoftware/python-esppy)
- Watch: DLPy videos:

* DLPy examples: <https://github.com/sassoftware/python-dlpy/tree/master/examples>
* DLPy API documentation [sassoftware.github.io/python-dlpy](https://sassoftware.github.io/python-dlpy/).
* [SAS SWAT for Python](http://github.com/sassoftware/python-swat/)
* [SAS ESPPy](https://github.com/sassoftware/python-esppy)
* Watch: DLPy videos:
* DLPy v1.0 examples:
* [Image classification using CNNs](https://www.youtube.com/watch?v=RJ0gbsB7d_8&start=125)
* [Object detection using TinyYOLOv2](https://www.youtube.com/watch?v=RJ0gbsB7d_8&start=390)
* [Import and export deep learning models with ONNX](https://www.youtube.com/watch?v=RJ0gbsB7d_8&start=627)
* [Text classification and text generation using RNNs](https://www.youtube.com/watch?v=RJ0gbsB7d_8&start=943)
* [Time series forecasting using RNNs](https://www.youtube.com/watch?v=RJ0gbsB7d_8&start=1185)
* [Image classification using CNNs](https://www.youtube.com/watch?v=RJ0gbsB7d_8&start=125)
* [Object detection using TinyYOLOv2](https://www.youtube.com/watch?v=RJ0gbsB7d_8&start=390)
* [Import and export deep learning models with ONNX](https://www.youtube.com/watch?v=RJ0gbsB7d_8&start=627)
* [Text classification and text generation using RNNs](https://www.youtube.com/watch?v=RJ0gbsB7d_8&start=943)
* [Time series forecasting using RNNs](https://www.youtube.com/watch?v=RJ0gbsB7d_8&start=1185)
* DLPy v1.1 examples:
* [Leverage Functional APIs to Build Complex Models](https://www.youtube.com/watch?v=guCDi2C-mNQ&t=115s)
* [Image Segmentation with U-Net](https://www.youtube.com/watch?v=guCDi2C-mNQ&t=399s)
* [Object Detection with Faster-RCNN](https://www.youtube.com/watch?v=guCDi2C-mNQ&t=688s)
* [Image Classification with ShuffleNet and MobileNet](https://www.youtube.com/watch?v=guCDi2C-mNQ&t=1158s)
* [Multi-class Deep learning](https://www.youtube.com/watch?v=guCDi2C-mNQ&t=1648s)
- [SAS Deep Learning with Python made easy using DLPy](https://blogs.sas.com/content/subconsciousmusings/2019/03/13/sas-deep-learning-with-python-made-easy-using-dlpy/)
* [Leverage Functional APIs to Build Complex Models](https://www.youtube.com/watch?v=guCDi2C-mNQ&t=115s)
* [Image Segmentation with U-Net](https://www.youtube.com/watch?v=guCDi2C-mNQ&t=399s)
* [Object Detection with Faster-RCNN](https://www.youtube.com/watch?v=guCDi2C-mNQ&t=688s)
* [Image Classification with ShuffleNet and MobileNet](https://www.youtube.com/watch?v=guCDi2C-mNQ&t=1158s)
* [Multi-class Deep learning](https://www.youtube.com/watch?v=guCDi2C-mNQ&t=1648s)
* [SAS Deep Learning with Python made easy using DLPy](https://blogs.sas.com/content/subconsciousmusings/2019/03/13/sas-deep-learning-with-python-made-easy-using-dlpy/)

### Contributing
Have something cool to share? SAS gladly accepts pull requests on GitHub! See the [Contributor Agreement](https://github.com/sassoftware/python-dlpy/blob/master/ContributorAgreement.txt) for details.

### Licensing
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License.
You may obtain a copy of the License at [LICENSE.txt](https://github.com/sassoftware/python-dlpy/blob/master/LICENSE.txt)

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

Have something cool to share? We gladly accept pull requests on GitHub! See the [Contributor Agreement](https://github.com/sassoftware/python-dlpy/blob/master/ContributorAgreement.txt) for details.

### Licensing

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License.
You can obtain a copy of the License at [LICENSE.txt](https://github.com/sassoftware/python-dlpy/blob/master/LICENSE.txt)

### for more products do visit our github and contribute.
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

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