tf.keras.layers.Embedding

Turns positive integers (indexes) into dense vectors of fixed size.

Inherits From: Layer, 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,
    **kwargs
)

Used in the notebooks

Used in the guide Used in the tutorials

e.g. [[4], [20]] -> [[0.25, 0.1], [0.6, -0.2]]

This layer can only be used on positive 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)

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).
embeddings_regularizer Regularizer function applied to the embeddings matrix (see keras.regularizers).
embeddings_constraint Constraint function applied to the embeddings matrix (see 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).

2D tensor with shape: (batch_size, input_length).

3D tensor with shape: (batch_size, input_length, output_dim).

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.

Methods

enable_lora

View source

enable_lora(
    rank, a_initializer='he_uniform', b_initializer='zeros'
)

from_config

View source

@classmethod
from_config(
    config
)

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

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

Returns
A layer instance.

quantized_build

View source

quantized_build(
    input_shape, mode
)

symbolic_call

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symbolic_call(
    *args, **kwargs
)

QUANTIZATION_MODE_ERROR_TEMPLATE "Invalid quantization mode. Expected 'int8'. Received: quantization_mode={mode}"