Data

The data components specify different types of data to train the model with. See here for a list of supported data files. Exist three different ways to import the data to the model:
Local
Environment
Random
Local 
Allows you to specify file(s) containing data to be used by the model.  PerceptiLabs' Data Component currently supports the following file formats: .csv .txt .npy (NumPy file) .npz (NumPy zipped file). The Data Component can also be configured to point to a directory of image files in the following formats: .jpg .png .jpeg .tiff .tif. The files are read by PerceptiLabs in alphabetical/numerical order.
Parameters
Preview
Load data
Choose files: selects the data models to train.
Choose folders: selects the location where data model files are stored. 
Split dataset: partitions the dataset into the following  groups:
Training: core training data on which to train the model. 
Validation (aka verification data): data used to test model fit during training.
Test: data to test the model against after training, to see how well the trained model handles data it hasn't seen before.
Shuffle: randomizes the order to train the data on, to make the model more robust. Note that if you have multiple Data components which correspond to each other (e.g., one for images and one for the corresponding labels), you must ensure that Shuffle is configured the same for all of them.
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Note: Paths cannot contain non-ascii characters.
Environment
Specifies that data be read in from a Gym environment. Environment component is a collection of test problems — environments — that you can use to work out your reinforcement learning algorithms provided by OpenAI.
Note
This component requires installation dependencies before it can be used. See Setting up OpenAI Gym Environments for Reinforcement Learning for more information.
Parameters:
Sample: selects the environment from Gym or Unity.
_action_space: displays the number of actions that can be taken in the selected environment. Note that this value is automatically calculated. 
Random 
Generates random data (e.g., random noise).
Parameters:
Distribution: specifies the random distribution function to sample the values from. PerceptiLabs currently supports Normal (aka Gaussian) and Uniform.
Mean: the mean value that the random function will use to "center" the random values around.
Stddev: the standard deviation that the random function to use. The larger the value, the more spread out the random values will be.
Shape: the dimension and amount of random values to use.
References - Previous
Components
Processing
Last updated 2 weeks ago