| Package | Description |
|---|---|
| org.deeplearning4j.nn.conf.preprocessor | |
| org.deeplearning4j.nn.modelimport.keras.preprocessors |
| Class and Description |
|---|
| BaseInputPreProcessor |
| Cnn3DToFeedForwardPreProcessor
A preprocessor to allow CNN and standard feed-forward network layers to be used together.
|
| CnnToFeedForwardPreProcessor
A preprocessor to allow CNN and standard feed-forward network layers to be used together.
|
| CnnToRnnPreProcessor
A preprocessor to allow CNN and RNN layers to be used together.
|
| ComposableInputPreProcessor
Composable input pre processor
|
| FeedForwardToCnn3DPreProcessor
A preprocessor to allow 3D CNN and standard feed-forward network layers to be used together.
|
| FeedForwardToCnnPreProcessor
A preprocessor to allow CNN and standard feed-forward network layers to be used together.
|
| FeedForwardToRnnPreProcessor
A preprocessor to allow RNN and feed-forward network layers to be used together.
|
| RnnToCnnPreProcessor
A preprocessor to allow RNN and CNN layers to be used together
For example, time series (video) input -> ConvolutionLayer, or conceivable GravesLSTM -> ConvolutionLayer Functionally equivalent to combining RnnToFeedForwardPreProcessor + FeedForwardToCnnPreProcessor Specifically, this does two things: (a) Reshape 3d activations out of RNN layer, with shape [miniBatchSize, numChannels*inputHeight*inputWidth, timeSeriesLength]) into 4d (CNN) activations (with shape [numExamples*timeSeriesLength, numChannels, inputWidth, inputHeight]) (b) Reshapes 4d epsilons (weights. |
| RnnToFeedForwardPreProcessor
A preprocessor to allow RNN and feed-forward network layers to be used together.
|
| Class and Description |
|---|
| BaseInputPreProcessor |
| CnnToFeedForwardPreProcessor
A preprocessor to allow CNN and standard feed-forward network layers to be used together.
|
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