Index
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B
- back(Matrix) - Method in interface com.jml.neural_network.activations.ActivationFunction
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Applies backward pass of the activation function to a matrix (i.e.
- back(Matrix) - Method in class com.jml.neural_network.activations.Linear
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Applies the derivative of the activation function, element-wise, to a matrix.
- back(Matrix) - Method in class com.jml.neural_network.activations.Relu
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Applies the derivative of the ReLU activation function to a matrix element-wise.
- back(Matrix) - Method in class com.jml.neural_network.activations.Sigmoid
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Applies the derivative of the sigmoid activation function element-wise to a matrix.
- back(Matrix) - Method in class com.jml.neural_network.activations.Softmax
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Applies the derivative of the activation function, element-wise, to a matrix.
- back(Matrix) - Method in class com.jml.neural_network.activations.Tanh
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Applies the derivative of the activation function, element-wise, to a matrix.
- back(Matrix) - Method in interface com.jml.neural_network.layers.BaseLayer
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Computes backward pass for layer.
- back(Matrix) - Method in class com.jml.neural_network.layers.Dense
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Computes the backward pass for this layer.
- back(Matrix) - Method in class com.jml.neural_network.layers.Dropout
- back(Matrix) - Method in class com.jml.neural_network.layers.Linear
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Computes backward pass for layer.
- BaseLayer - Interface in com.jml.neural_network.layers
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Base layer interface.
- BIAS - Enum constant in enum class com.jml.neural_network.ModelTags
- binCrossEntropy - Static variable in class com.jml.losses.LossFunctions
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The binary cross-entropy loss function.
- Block - Class in com.jml.core
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Blocks are used to define a model.
- Block(String, String) - Constructor for class com.jml.core.Block
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Constructs a block with specified name and content.
- buildFileContent(Block...) - Static method in class com.jml.core.Block
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Builds the content of a markup file containing blocks.
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