keras.layers.embeddings.Embedding(input_dim, output_dim, init='uniform', input_length=None, W_regularizer=None, activity_regularizer=None, W_constraint=None, mask_zero=False, weights=None, dropout=0.0)

Turn positive integers (indexes) into dense vectors of fixed size. eg. [[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, ie. 1 + maximum integer index occurring in the input data.
  • output_dim: int >= 0. Dimension of the dense embedding.
  • init: name of initialization function for the weights of the layer (see: initializations), or alternatively, Theano function to use for weights initialization. This parameter is only relevant if you don't pass a weights argument.
  • weights: list of Numpy arrays to set as initial weights. The list should have 1 element, of shape (input_dim, output_dim).
  • W_regularizer: instance of the regularizers module (eg. L1 or L2 regularization), applied to the embedding matrix.
  • W_constraint: instance of the constraints module (eg. maxnorm, nonneg), applied to the embedding matrix.
  • mask_zero: Whether or not the input value 0 is a special "padding" value that should be masked out. This is useful for 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 |vocabulary| + 2).
  • 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).
  • dropout: float between 0 and 1. Fraction of the embeddings to drop.

Input shape

2D tensor with shape: (nb_samples, sequence_length).

Output shape

3D tensor with shape: (nb_samples, sequence_length, output_dim).