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

Inherits From: Layer, Module

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 as the first layer in a model.


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('rmsprop', 'mse')
output_array = model.predict(input_array)
(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).
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).

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(...)

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.