LostTech.TensorFlow : API Documentation

Type Dense

Namespace tensorflow.keras.layers

Parent Layer

Interfaces IDense

Just your regular densely-connected NN layer.

`Dense` implements the operation: `output = activation(dot(input, kernel) + bias)` where `activation` is the element-wise activation function passed as the `activation` argument, `kernel` is a weights matrix created by the layer, and `bias` is a bias vector created by the layer (only applicable if `use_bias` is `True`).

Note: If the input to the layer has a rank greater than 2, then it is flattened prior to the initial dot product with `kernel`.

Show Example
# as first layer in a sequential model:
            model = Sequential()
            model.add(Dense(32, input_shape=(16,)))
            # now the model will take as input arrays of shape (*, 16)
            # and output arrays of shape (*, 32) 

# after the first layer, you don't need to specify # the size of the input anymore: model.add(Dense(32))



Public static methods

Dense NewDyn(object units, object activation, ImplicitContainer<T> use_bias, ImplicitContainer<T> kernel_initializer, ImplicitContainer<T> bias_initializer, object kernel_regularizer, object bias_regularizer, object activity_regularizer, object kernel_constraint, object bias_constraint, IDictionary<string, object> kwargs)

Public properties

object activation get; set;

PythonFunctionContainer activity_regularizer get; set;

object activity_regularizer_dyn get; set;

object bias get; set;

object bias_constraint get; set;

object bias_initializer get; set;

object bias_regularizer get; set;

bool built get; set;

object dtype get;

object dtype_dyn get;

bool dynamic get;

object dynamic_dyn get;

IList<Node> inbound_nodes get;

object inbound_nodes_dyn get;

IList<object> input get;

object input_dyn get;

object input_mask get;

object input_mask_dyn get;

IList<object> input_shape get;

object input_shape_dyn get;

InputSpec input_spec get; set;

object input_spec_dyn get; set;

object kernel get; set;

object kernel_constraint get; set;

object kernel_initializer get; set;

object kernel_regularizer get; set;

IList<object> losses get;

object losses_dyn get;

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;

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;

int units get; set;

IList<object> updates get;

object updates_dyn get;

bool use_bias get; set;

object variables get;

object variables_dyn get;

IList<object> weights get;

object weights_dyn get;