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tf.keras.layers.Embedding

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.

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('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