AI

4月 162020
 

After careful consideration of the evolving COVID-19 situation, SAS made the decision in March to cancel the in-person SAS Global Forum 2020 conference in Washington, DC. The health and well-being of SAS customers and employees was the company's top priority in making that decision, and while it's unfortunate that we [...]

SAS Global Forum 2020: Hybrid marketing with SAS Customer Intelligence 360 was published on Customer Intelligence Blog.

4月 022020
 

In February, SAS was recognized as a Leader in the 2020 Gartner Magic Quadrant for Data Science & Machine Learning Platforms report. SAS is the only vendor to be a leader in this report for all seven years of its existence. According to us, the topic of the research is [...]

SAS Customer Intelligence 360: Analyst viewpoints was published on Customer Intelligence Blog.

3月 262020
 

Excitement levels are high for the March 2020 release of SAS Customer Intelligence 360, which includes multiple years of research and development culminating in enhancements to the platform's underlying data model. The changes will introduce the unification of a comprehensive data model recording both: Customer behavior -- what users are [...]

SAS Customer Intelligence 360: Unified data model, marketing attribution and AutoML was published on Customer Intelligence Blog.

3月 172020
 

Data management has never been the shiny object that caught the imagination of the mainstream. And let’s be honest, it's not nearly as interesting as analytics, machine learning or artificial intelligence. In fact, entire movies get created about analytics, and people actually pay to see them! Data management? Not so [...]

Past, present and future ... it's always been about data management was published on SAS Voices by Todd Wright

3月 122020
 

In parts one and two of this blog series, we introduced hybrid marketing as a method that combines both direct and digital marketing capabilities while absorbing insights from machine learning. According to Daniel Newman (Futurum Research) and Wilson Raj (SAS) in the October 2019 research study Experience 2030: “Brands must [...]

SAS Customer Intelligence 360: Hybrid marketing and analytic's last mile [Part 3] was published on Customer Intelligence Blog.

3月 052020
 

Fifty years ago, as the women’s liberation movement was gaining momentum in the U.S., my maternal great-grandmother, Pearl, worked in a factory sewing American flags while volunteering with the Girl Scouts and caring for her grandchildren. My paternal grandmother, Greta, also worked in local factories while caring for her family. [...]

50 years of strong, intelligent women was published on SAS Voices by Ashley Binder

1月 312020
 

Everyone is talking about artificial intelligence (AI) and how it affects our  lives -- there are even AI toothbrushes! But how do businesses use AI to help them compete in the market? According to Gartner research, only half of all AI projects are deployed and 90% take more than three [...]

Driving faster value from analytics – how to deploy models and decisions quickly was published on SAS Voices by Janice Newell

1月 172020
 

Where in your business process can analytics and AI play a contributing role in enhancing your decision making capability?  At the information interpretation stage.  As a framework for understanding what analytic and AI opportunities may arise, the simple diagram below illustrates the relationships between data, information and knowledge, and how [...]

Opportunities for analytics: Interpretation was published on SAS Voices by Leo Sadovy

12月 062019
 

Another year, another traditional Christmas song or carol turned into a fun technology-related version! This is the sixth year and my ninth song. I hope you enjoy your 2019 holiday song, based on this famous tune. The Data Science and AI Song Computer vision processing on an open stack The [...]

The Data Science and AI Song was published on SAS Voices by David Pope

9月 092019
 

Editor's Note: This article was translated and edited by SAS USA and was originally written by Makoto Unemi. The original text is here.

SAS previously provided SAS Scripting Wrapper for Analytics Transfer (SWAT), a package for using SAS Viya functions from various general-purpose programming languages ​​such as Python.

In addition to SWAT, SAS launched Deep Learning Python (DLPy), a higher-level API package for Python, making it possible to use SAS Viya functions more efficiently from Python. In this article I outline more about what DLPy is and how it's implementation.

About DLPy

DLPy is a high-level package for the Python API created for deep learning and image action set after Viya3.3. DLPy provides an API similar to Keras to improve the efficiency of deep learning and image processing coding. With just a little rewriting of the existing Keras code, it is possible to execute the processing on SAS Viya.

For example, below is an example of a Convolutional Neural Network (CNN) layer definition; you can see that it is very similar to Keras.

The layers supported by DLPy are: InputLayer, Conv2d, Pooling, Dense, Recurrent, BN, Res, Proj, and OutputLayer. The following is an example of learning.

DLPy functions

Introducing DLPy's functions (partial excerpts), taking as an example the learning of multiple dolphins and giraffe images using CNN and applying test images to the model.

Implementation of major deep learning networks

DLPy offers the following pre-built deep learning models: VGG11/13/16/19, ResNet34/50/101/152, wide_resnet, and dense_net.

The following models also offer pre-trained weights using ImageNet data (these weights can be used for unique tasks by transfer learning): VGG16, VGG19, ResNet50, ResNet101, and ResNet152. The following is an example of transferring ResNet50 pre-trained weights.

CNN judgment basis information

Using the heat_map_analysis() method, you can output a colorful heat map and check where you focused on the image.

In addition, the get_feature_maps() method is used to get the feature map of each layer of CNN, and feature_maps.display() method is used to specify and display the obtained feature map layer and check can also do.

The following is the output result of layer 1 feature map.

The following is the output result of layer 18 feature map.

Deep learning & image processing related task support function

resize() method: Resize image data

as_patches() method: Image data expansion (generates a patch from the original image)

two_way_split() method: Data split (learning, testing)

plot_network() method: draws the structure of the defined deep learning layer (network) as a graphical diagram

plot_training_history() method: Iterative learning history display

predict() method: Display prediction (scoring) results

plot_predict_res() method: Display classification results

And of course, you can use DLPy to get data from a SAS Viya in-memory session, pass it to your local client, and convert it to common data formats like numpy arrays and Pandas DataFrames. The converted data can be smoothly supplied to models of other open source packages such as scikit-learn.

Regarding image classification using DLPy, videos are also available in the Deep Learning with Python (DLPy) Demo Series section of the DLPy product page.

SAS Viya: Package for Python API for deep learning and image processing: DLPy was published on SAS Users.