ó ¾÷Xc@@sŠddlmZddlmZddlmZddlmZddlmZddlm Z ddl m Z d e fd „ƒYZ d S( i(tabsolute_importi(tbackend(t initializers(t regularizers(t constraints(tLayer(t interfacest EmbeddingcB@sbeZdZejddddedd„ƒZd„Zdd„Z d„Z d„Z d„Z RS( s¼ Turns 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. # Example ```python 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) ``` # Arguments 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. embeddings_initializer: Initializer for the `embeddings` matrix (see [initializers](../initializers.md)). embeddings_regularizer: Regularizer function applied to the `embeddings` matrix (see [regularizer](../regularizers.md)). embeddings_constraint: Constraint function applied to the `embeddings` matrix (see [constraints](../constraints.md)). mask_zero: Whether or not the input value 0 is a special "padding" value that should be masked out. This is useful when using [recurrent layers](recurrent.md) 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). # Input shape 2D tensor with shape: `(batch_size, sequence_length)`. # Output shape 3D tensor with shape: `(batch_size, sequence_length, output_dim)`. # References - [A Theoretically Grounded Application of Dropout in Recurrent Neural Networks](http://arxiv.org/abs/1512.05287) tuniformc K@s¿d| ds