LostTech.TensorFlow : API Documentation

Type Embedding

Namespace tensorflow.keras.layers

Parent Layer

Interfaces IEmbedding

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 as the first layer in a model.

Show 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)



Public static methods

Embedding NewDyn(object input_dim, object output_dim, ImplicitContainer<T> embeddings_initializer, object embeddings_regularizer, object activity_regularizer, object embeddings_constraint, ImplicitContainer<T> mask_zero, object input_length, IDictionary<string, object> kwargs)

Public properties

object activity_regularizer get; set;

Optional regularizer function for the output of this layer.

object activity_regularizer_dyn get; set;

bool built get; set;

object dtype get;

object dtype_dyn get;

bool dynamic get;

object dynamic_dyn get;

object embeddings get; set;

object embeddings_constraint get; set;

object embeddings_initializer get; set;

object embeddings_regularizer get; set;

IList<Node> inbound_nodes get;

object inbound_nodes_dyn get;

IList<object> input get;

int input_dim get; set;

object input_dyn get;

object input_length get; set;

object input_mask get;

object input_mask_dyn get;

IList<object> input_shape get;

object input_shape_dyn get;

object input_spec get; set;

object input_spec_dyn get; set;

IList<object> losses get;

object losses_dyn get;

bool mask_zero get; set;

IList<object> metrics get;

object metrics_dyn get;

object name get;

object name_dyn get;

object name_scope get;

object name_scope_dyn get;

IList<object> non_trainable_variables get;

object non_trainable_variables_dyn get;

IList<object> non_trainable_weights get;

object non_trainable_weights_dyn get;

IList<object> outbound_nodes get;

object outbound_nodes_dyn get;

IList<object> output get;

int output_dim get; set;

object output_dyn get;

object output_mask get;

object output_mask_dyn get;

object output_shape get;

object output_shape_dyn get;

object PythonObject get;

bool stateful get; set;

ValueTuple<object> submodules get;

object submodules_dyn get;

bool supports_masking get; set;

bool trainable get; set;

object trainable_dyn get; set;

object trainable_variables get;

object trainable_variables_dyn get;

IList<object> trainable_weights get;

object trainable_weights_dyn get;

IList<object> updates get;

object updates_dyn get;

object variables get;

object variables_dyn get;

IList<object> weights get;

object weights_dyn get;