keras Embedding layer [Arguments input_dim: int > 0. Size of the vocabulary, i.e. maximum integer ]

keras.layers.embeddings.Embedding(input_dim,
output_dim, embeddings_initializer='uniform',

embeddings_regularizer=None,
activity_regularizer=None,
embeddings_constraint=None,

mask_zero=False,
input_length=None)

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

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, i.e. maximum integer index + 1.
output_dim: int >= 0. Dimension of the dense embedding.
embeddings_initializer: Initializer for the 

embeddings

 matrix
(see initializers).
embeddings_regularizer: Regularizer function applied to the 

embeddings

 matrix
(see regularizer).
embeddings_constraint: Constraint function applied to the 

embeddings

 matrix
(see constraints).
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 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).
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