| Layer type | |
|---|---|
Dense
Fully connected layer, the simplest form of a neural network layer.
Use this for processed data, such as the output of a spectral analysis DSP block.
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1D Convolution / pooling
Learn features that take spatial information into account along a single dimension.
Use this for raw data, or for DSP blocks that output spatial data, such as the MFCC block.
|
|
2D Convolution / pooling
Learn features that take spatial information into account along two dimensions.
Use this for raw data, or for DSP blocks that output spatial data, such as the MFCC block.
|
|
Reshape
Turn one-dimensional data from a DSP block into multi-dimensional data.
Use this as an input to a convolutional layer.
Use this for deep learning on raw data, or to process MFCC output.
|
|
Flatten
Flatten multi-dimensional data into a single dimension.
You need to flatten data from a convolutional layer before returning.
|
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Dropout
Reduce the risk of a model overfitting your dataset by randomly cutting a fraction
of network connections during training. Can be helpful if your model's training performance
is better than its validation performance.
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To use an Input block version, connect it to a DSP block version using the Manage versions dialog on a DSP block page. The starred Input block version is the one connected to the Primary learn block, via a DSP block.
To use a DSP block version, connect it to a Learn block version using the Manage versions dialog on a Learn block page. The starred DSP block version is the one connected to the Primary learn block.
The Primary learn block version is the one that will be used when a project is deployed. When you set a Learn block version to Primary, any DSP and Input block versions it connects to will automatically be selected for deployment.
We're always looking for ways to improve Edge Impulse. If you have any feedback, please let us know!
We'll work with you on your setup and help you get the most out of Edge Impulse.
