|---| | [ View source on GitHub](https://github.com/keras-team/keras/tree/v3.15.1/keras/src/layers/core/embedding.py#L20-L792) | Turns nonnegative integers (indexes) into dense vectors of fixed size. Inherits From: [`Layer`](https://www.tensorflow.org/api_docs/python/tf/keras/Layer), [`Operation`](https://www.tensorflow.org/api_docs/python/tf/keras/Operation) tf.keras.layers.Embedding( input_dim, output_dim, embeddings_initializer='uniform', embeddings_regularizer=None, embeddings_constraint=None, mask_zero=False, weights=None, lora_rank=None, lora_alpha=None, quantization_config=None, **kwargs ) ### Used in the notebooks | Used in the guide | Used in the tutorials | |---|---| | - [Migrate \`tf.feature_column\`s to Keras preprocessing layers](https://www.tensorflow.org/guide/migrate/migrating_feature_columns) - [Ragged tensors](https://www.tensorflow.org/guide/ragged_tensor) - [Working with preprocessing layers](https://www.tensorflow.org/guide/keras/preprocessing_layers) | - [Parameter server training with ParameterServerStrategy](https://www.tensorflow.org/tutorials/distribute/parameter_server_training) - [Basic text classification](https://www.tensorflow.org/tutorials/keras/text_classification) - [Load text](https://www.tensorflow.org/tutorials/load_data/text) - [Using side features: feature preprocessing](https://www.tensorflow.org/recommenders/examples/featurization) - [Taking advantage of context features](https://www.tensorflow.org/recommenders/examples/context_features) | e.g. `[[4], [20]] -> [[0.25, 0.1], [0.6, -0.2]]` This layer can only be used on nonnegative integer inputs of a fixed range. #### Example: model = keras.Sequential() model.add(keras.layers.Embedding(1000, 64)) # The model will take as input an integer matrix of size (batch, # input_length), and the largest integer (i.e. word index) in the input # should be no larger than 999 (vocabulary size). # Now model.output_shape is (None, 10, 64), where `None` is the batch # dimension. input_array = np.random.randint(1000, size=(32, 10)) model.compile('rmsprop', 'mse') output_array = model.predict(input_array) print(output_array.shape) (32, 10, 64)


| ## Args || |---|---| | `input_dim` | Integer. Size of the vocabulary, i.e. maximum integer index + 1. | | `output_dim` | Integer. Dimension of the dense embedding. | | `embeddings_initializer` | Initializer for the `embeddings` matrix (see [`keras.initializers`](https://www.tensorflow.org/api_docs/python/tf/keras/initializers)). | | `embeddings_regularizer` | Regularizer function applied to the `embeddings` matrix (see [`keras.regularizers`](https://www.tensorflow.org/api_docs/python/tf/keras/regularizers)). | | `embeddings_constraint` | Constraint function applied to the `embeddings` matrix (see [`keras.constraints`](https://www.tensorflow.org/api_docs/python/tf/keras/constraints)). | | `mask_zero` | Boolean, whether or not the input value 0 is a special "padding" value that should be masked out. This is useful when using recurrent layers which may take variable length input. If this is `True`, then all subsequent layers in the model need to support masking or an exception will be raised. If `mask_zero` is set to `True`, as a consequence, index 0 cannot be used in the vocabulary (`input_dim` should equal size of vocabulary + 1). | | `weights` | Optional floating-point matrix of size `(input_dim, output_dim)`. The initial embeddings values to use. | | `lora_rank` | Optional integer. If set, the layer's forward pass will implement LoRA (Low-Rank Adaptation) with the provided rank. LoRA sets the layer's embeddings matrix to non-trainable and replaces it with a delta over the original matrix, obtained via multiplying two lower-rank trainable matrices. This can be useful to reduce the computation cost of fine-tuning large embedding layers. You can also enable LoRA on an existing `Embedding` layer by calling `layer.enable_lora(rank)`. | | `lora_alpha` | Optional integer. If set, this parameter scales the low-rank adaptation delta (computed as the product of two lower-rank trainable matrices) during the forward pass. The delta is scaled by `lora_alpha / lora_rank`, allowing you to fine-tune the strength of the LoRA adjustment independently of `lora_rank`. |



| ## Input shape || |---|---| | 2D tensor with shape: `(batch_size, input_length)`. ||



| ## Output shape || |---|---| | 3D tensor with shape: `(batch_size, input_length, output_dim)`. ||



| ## Attributes || |---|---| | `embeddings` |

| | `input` | Retrieves the input tensor(s) of a symbolic operation.
Only returns the tensor(s) corresponding to the *first time* the operation was called. | | `output` | Retrieves the output tensor(s) of a layer.
Only returns the tensor(s) corresponding to the *first time* the operation was called. | | `variable_serialization_spec` | Returns a dict mapping quantization modes to variable names in order.
This spec is used by `save_own_variables` and `load_own_variables` to determine the correct ordering of variables during serialization for each quantization mode. `None` means no quantization. |
## Methods ### `enable_lora` [View source](https://github.com/keras-team/keras/tree/v3.15.1/keras/src/layers/core/embedding.py#L214-L259) enable_lora( rank, lora_alpha=None, a_initializer='he_uniform', b_initializer='zeros' ) ### `from_config` [View source](https://github.com/keras-team/keras/tree/v3.15.1/keras/src/layers/core/embedding.py#L369-L377) @classmethod from_config( config ) Creates an operation from its config. This method is the reverse of `get_config`, capable of instantiating the same operation from the config dictionary. > [!NOTE] > **Note:** If you override this method, you might receive a serialized dtype config, which is a `dict`. You can deserialize it as follows: if "dtype" in config and isinstance(config["dtype"], dict): policy = dtype_policies.deserialize(config["dtype"])


| Args || |---|---| | `config` | A Python dictionary, typically the output of `get_config`. |



| Returns || |---|---| | An operation instance. ||