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TensorFlow 1 version View source on GitHub

Class Embedding

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

Inherits From: Layer


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 = Sequential()
model.add(Embedding(1000, 64, input_length=10))
# the model will take as input an integer matrix of size (batch,
# input_length).
# the largest integer (i.e. word index) in the input should be no larger
# than 999 (vocabulary size).
# now model.output_shape == (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)
assert output_array.shape == (32, 10, 64)


  • input_dim: int > 0. Size of the vocabulary, i.e. maximum integer index + 1.
  • output_dim: int >= 0. Dimension of the dense embedding.
  • embeddings_initializer: Initializer for the embeddings matrix.
  • embeddings_regularizer: Regularizer function applied to the embeddings matrix.
  • embeddings_constraint: Constraint function applied to the embeddings matrix.
  • mask_zero: 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).


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