inputlength : positive integer or the symbol auto

Length of the generated Tensor.

With the default value auto, the shape is inferred. If inference is not possible, an error is issued.

EmbeddingLayer(inputdim,outputdim,opts) creates an embedding neural network layer with input dimension inputdim and output dimension outputdim.

This function is part of the DeepLearning package, so it can be used in the short form EmbeddingLayer(..) only after executing the command with(DeepLearning). However, it can always be accessed through the long form of the command by using DeepLearning[EmbeddingLayer](..).

The implementation of EmbeddingLayer uses tf.keras.layers.Embedding from the TensorFlow Python API. Consult the TensorFlow Python API documentation for tf.keras.layers.Embedding for more information.

$\mathrm{with}\left(\mathrm{DeepLearning}\right)$

$\left[\mathrm{AddMultiple}\,\mathrm{ApplyOperation}\,\mathrm{BatchNormalizationLayer}\,\mathrm{BidirectionalLayer}\,\mathrm{BucketizedColumn}\,\mathrm{CategoricalColumn}\,\mathrm{Classify}\,\mathrm{Concatenate}\,\mathrm{Constant}\,\mathrm{ConvolutionLayer}\,\mathrm{DNNClassifier}\,\mathrm{DNNLinearCombinedClassifier}\,\mathrm{DNNLinearCombinedRegressor}\,\mathrm{DNNRegressor}\,\mathrm{Dataset}\,\mathrm{DenseLayer}\,\mathrm{DropoutLayer}\,\mathrm{EinsteinSummation}\,\mathrm{EmbeddingLayer}\,\mathrm{Estimator}\,\mathrm{FeatureColumn}\,\mathrm{Fill}\,\mathrm{FlattenLayer}\,\mathrm{GRULayer}\,\mathrm{GatedRecurrentUnitLayer}\,\mathrm{GetDefaultGraph}\,\mathrm{GetDefaultSession}\,\mathrm{GetEagerExecution}\,\mathrm{GetVariable}\,\mathrm{GradientTape}\,\mathrm{IdentityMatrix}\,\mathrm{LSTMLayer}\,\mathrm{Layer}\,\mathrm{LinearClassifier}\,\mathrm{LinearRegressor}\,\mathrm{LongShortTermMemoryLayer}\,\mathrm{MaxPoolingLayer}\,\mathrm{Model}\,\mathrm{NumericColumn}\,\mathrm{OneHot}\,\mathrm{Ones}\,\mathrm{Operation}\,\mathrm{Optimizer}\,\mathrm{Placeholder}\,\mathrm{RandomTensor}\,\mathrm{ResetDefaultGraph}\,\mathrm{Restore}\,\mathrm{Save}\,\mathrm{Sequential}\,\mathrm{Session}\,\mathrm{SetEagerExecution}\,\mathrm{SetRandomSeed}\,\mathrm{SoftMaxLayer}\,\mathrm{SoftmaxLayer}\,\mathrm{Tensor}\,\mathrm{Variable}\,\mathrm{Variables}\,\mathrm{VariablesInitializer}\,\mathrm{Zeros}\right]$

$\mathrm{model}≔\mathrm{Sequential}\left(\left[\mathrm{EmbeddingLayer}\left(1000\,64\right)\,\mathrm{LSTMLayer}\left(128\right)\,\mathrm{DenseLayer}\left(10\right)\right]\right)$

$\mathrm{model}≔\left[\begin{array}{c}\mathrm{DeepLearning\; Model}\\ \mathrm{<keras.src.engine.sequential.Sequential\; object\; at\; 0x7ff2731bc550>}\end{array}\right]$

$\mathrm{model}:-\mathrm{Compile}\left(\right)$

The DeepLearning[EmbeddingLayer] command was introduced in Maple 2021.

For more information on Maple 2021 changes, see Updates in Maple 2021.