• namespace: Rindow\NeuralNetworks\Layer
• classname: Embedding

Learn and Translate from positive integer to vectors with weights.

Inputs are positive integer sequences of shape [batch_size, input_length]. Outputs are vectors of shape [batch_size, input_length, output_dim]. Weights of shape are [input_dim, output_dim].

Methods

constructor

$builer->Embedding(
    int $inputDim,
    int $outputDim,
    int $input_length=null,
    string|callable $kernel_initializer='random_uniform',
    string $name=null,
)

You can create a Attention layer instances with the Layer Builder.

Arguments

• inputDim: Size of the vocabulary. A value one greater than the maximum value in the input sequences
• outputDim: Dimension of the output embedding vectors

Options

• input_length: Sequence length.

Input shape

[batch_size, input_length] The value contained in the sequence must be a positive integer and less than input_dim.

Output shape

[batch_size, input_length, output_dim]

Example of usage

$embedding = $builder->layers()->Embedding(
    $inputDim=5
    $outputDim=4,
    input_length:3
);
....
$sequences = $mo->array([[4,3,1],[2,1,0]],NDArray::int32);

....
$outputs = $embedding->forward($sequences,true);
# $outputs->shape() : [2,3,4]