tf.keras.layers.Embedding + tf.keras.layers.TextVectorization + tf.keras.layers.StringLookup + tf.keras.layers.IntegerLookup

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

  • Migrate tf.feature_columns to Keras preprocessing layers
  • Ragged tensors
  • Working with preprocessing layers

• Used in the tutorials

  • Parameter server training with ParameterServerStrategy
  • Basic text classification
  • Load text
  • Using side features: feature preprocessing
  • Taking advantage of context features

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)