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TensorFlow 1 version
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View source on GitHub
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Turns positive integers (indexes) into dense vectors of fixed size.
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, **kwargs
)
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)
Arguments | |
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input_dim
|
Integer. Size of the vocabulary, i.e. maximum integer index + 1. |
output_dim
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Integer. Dimension of the dense embedding. |
embeddings_initializer
|
Initializer for the embeddings
matrix (see keras.initializers).
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embeddings_regularizer
|
Regularizer function applied to
the embeddings matrix (see keras.regularizers).
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embeddings_constraint
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Constraint function applied to
the embeddings matrix (see keras.constraints).
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mask_zero
|
Boolean, 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).
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input_length
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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).
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Input shape:
2D tensor with shape: (batch_size, input_length).
Output shape:
3D tensor with shape: (batch_size, input_length, output_dim).