Data Preparation and CNN Training

We used neural networks for the image recognition task. Neural networks are massively parallel adaptive processing structures consisting of one or more layers, each layer one or more neurons. Three types of layers exist: Input (receiving data from its environment, providing processed data to other layers), hidden (receiving and providing processed data from and to other layers) and output (receiving processed data from other layers, providing information to the environment) layers. Weighted connections exist between the neurons of each layer, changing them is the key to its adaptability, which happens based on the difference between the predicted and the expected results.

A key advantage of neural networks is the fact that they can learn nonlinear relationships between variables. This gave researchers the idea to study neural networks in image recognition and object detection tasks.

Data Preparation and CNN Training

 

Resources

EXAMPLES Server: 60_Innovation_Notes/08_Image_Recognition_for_Retail/01_Data_Preparation_and_CNN_Training60_Innovation_Notes/08_Image_Recognition_for_Retail/01_Data_Preparation_and_CNN_Training*
Download a zip-archive

 

 


* Find more about the Examples Server here.
The link will open the workflow directly in KNIME Analytics Platform (requirements: Windows; KNIME Analytics Platform must be installed with the Installer version 3.2.0 or higher). In other cases, please use the link to a zip-archive or open the provided path manually