Package ai.djl.nn.core

Class Multiplication

  • All Implemented Interfaces:
    Block
    public class Multiplication
extends AbstractBlock
    A Multiplication block performs an element-wise multiplication of inputs and weights as opposed to a Linear block which additionally sums up each element-wise multiplication.

    Similar to a LinearCollection, multiple split dimensions are supported but they remain optional (i.e. \(t\) can be zero). Other differences to a Linear block are that the weight has an additional dimension of size 1 interspersed (to broadcast the weight to every input of the batch when applying the internally used algebraic operation NDArray.mul(NDArray) ) and that biases are not supported.

    Caution: the output-channel is the left-most dimension as opposed to traditionally being the right-most dimension. As the output is one dimension larger than that of a Linear block, it is more efficient and therefore recommended to apply an aggregating function (like the sum) first and only then shift the first axis of the aggregated and thus smaller NDArray instance into last position.

    It has the following shapes:

    • input X: [x_1, s_1, s_2, …, s_t, input_dim]
    • weight W: [units, 1, s_1, s_2, …, s_t, input_dim]
    • output Y: [units, x_1, s_1, s_2, …, s_t, input_dim]

    The Multiplication block should be constructed using Multiplication.Builder.

    • Method Detail

      • getOutputShapes

        public Shape[] getOutputShapes​(Shape[] inputs)
        Returns the expected output shapes of the block for the specified input shapes.
        Parameters:
        inputs - the shapes of the inputs
        Returns:
        the expected output shapes of the block

      • describeInput

        public ai.djl.util.PairList<java.lang.String,​Shape> describeInput()
        Returns a PairList of input names, and shapes.
        Specified by:
        describeInput in interface Block
        Overrides:
        describeInput in class AbstractBaseBlock
        Returns:
        the PairList of input names, and shapes

      • beforeInitialize

        protected void beforeInitialize​(Shape... inputShapes)
        Performs any action necessary before initialization. For example, keep the input information or verify the layout.
        Overrides:
        beforeInitialize in class AbstractBaseBlock
        Parameters:
        inputShapes - the expected shapes of the input

      • saveMetadata

        protected void saveMetadata​(java.io.DataOutputStream os)
                     throws java.io.IOException
        Override this method to save additional data apart from parameter values.

        This default implementation saves the currently set input shapes.

        Overrides:
        saveMetadata in class AbstractBaseBlock
        Parameters:
        os - the non-null output stream the parameter values and metadata are written to
        Throws:
        java.io.IOException - saving failed

      • loadMetadata

        public void loadMetadata​(byte loadVersion,
                         java.io.DataInputStream is)
                  throws java.io.IOException,
                         MalformedModelException
        Overwrite this to load additional metadata with the parameter values.

        If you overwrite AbstractBaseBlock.saveMetadata(DataOutputStream) or need to provide backward compatibility to older binary formats, you prabably need to overwrite this. This default implementation checks if the version number fits, if not it throws an MalformedModelException. After that it restores the input shapes.

        Overrides:
        loadMetadata in class AbstractBaseBlock
        Parameters:
        loadVersion - the version used for loading this metadata.
        is - the input stream we are loading from
        Throws:
        java.io.IOException - loading failed
        MalformedModelException - data can be loaded but has wrong format

      • multiply

        public NDList multiply​(NDArray input,
                       NDArray weight)
        Applies an element-wise multiplication to the incoming data.
        Parameters:
        input - The incoming data
        weight - The weight of this block
        Returns:
        element-wise multiplication of input and weight using broadcasting rules

      • builder

        public static Multiplication.Builder builder()
        Creates a builder to build a Linear.
        Returns:
        a new builder