Turns positive integers(indexes) into dense vectors of fixed size.
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.
Inherits From: [Layer](https://tensorflow.google.cn/api_docs/python/tf/keras/layers/Layer), [Module](https://tensorflow.google.cn/api_docs/python/tf/Module)
tf.keras.layers.Embedding(
input_dim,
output_dim,
embeddings_initializer='uniform',
embeddings_regularizer=None,
activity_regularizer=None,
embeddings_constraint=None,
mask_zero=False,
input_length=None,
sparse=False,
**kwargs
)
Used in the notebooks
-
Used in the guide
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Used in the tutorials
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)