tf.keras.layers.Embedding

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

Inherits From: Layer, Module

tf.keras.layers.Embedding(
    input_dim,
    output_dim,
    embeddings_initializer=&#x27;uniform',
    embeddings_regularizer=None,
    activity_regularizer=None,
    embeddings_constraint=None,
    mask_zero=False,
    input_length=None,
    sparse=False,
    **kwargs
)

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. The tf.keras.layers.TextVectorization, tf.keras.layers.StringLookup, and tf.keras.layers.IntegerLookup preprocessing layers can help prepare inputs for an Embedding layer.

This layer accepts tf.Tensor, tf.RaggedTensor and tf.SparseTensor input.

Example:

model = tf.keras.Sequential()
model.add(tf.keras.layers.Embedding(1000, 64, input_length=10))
# 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(&#x27;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`).
`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).
`input_length`	Length of input sequences, when it is constant. This argument is required if you are going to connect `Flatten` then `Dense` layers upstream (without it, the shape of the dense outputs cannot be computed).
`sparse`	If True, calling this layer returns a `tf.SparseTensor`. If False, the layer returns a dense `tf.Tensor`. For an entry with no features in a sparse tensor (entry with value 0), the embedding vector of index 0 is returned by default.

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

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

Note on variable placement: By default, if a GPU is available, the embedding matrix will be placed on the GPU. This achieves the best performance, but it might cause issues:

You may be using an optimizer that does not support sparse GPU kernels. In this case you will see an error upon training your model.
Your embedding matrix may be too large to fit on your GPU. In this case you will see an Out Of Memory (OOM) error.

In such cases, you should place the embedding matrix on the CPU memory. You can do so with a device scope, as such:

with tf.device('cpu:0'):
  embedding_layer = Embedding(...)
  embedding_layer.build()

The pre-built embedding_layer instance can then be added to a Sequential model (e.g. model.add(embedding_layer)), called in a Functional model (e.g. x = embedding_layer(x)), or used in a subclassed model.

tf.keras.layers.Embedding

Example:

Args

Input shape

Output shape