LostTech.TensorFlow : API Documentation

Type LocallyConnected2D

Namespace tensorflow.keras.layers

Parent Layer

Interfaces ILocallyConnected2D

Locally-connected layer for 2D inputs.

The `LocallyConnected2D` layer works similarly to the `Conv2D` layer, except that weights are unshared, that is, a different set of filters is applied at each different patch of the input.

Show Example
# apply a 3x3 unshared weights convolution with 64 output filters on a
            32x32 image
            # with `data_format="channels_last"`:
            model = Sequential()
            model.add(LocallyConnected2D(64, (3, 3), input_shape=(32, 32, 3)))
            # now model.output_shape == (None, 30, 30, 64)
            # notice that this layer will consume (30*30)*(3*3*3*64) + (30*30)*64

# add a 3x3 unshared weights convolution on top, with 32 output filters: model.add(LocallyConnected2D(32, (3, 3))) # now model.output_shape == (None, 28, 28, 32)


Public properties

object activation get; set;

object activity_regularizer get; set;

Optional regularizer function for the output of this layer.

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;

string data_format get; set;

object dtype get;

object dtype_dyn get;

bool dynamic get;

object dynamic_dyn get;

int filters get; set;

int implementation get; set;

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;

IList<object> kernel_idxs get; set;

object kernel_initializer get; set;

object kernel_mask get; set;

object kernel_regularizer get; set;

object kernel_shape get; set;

object kernel_size 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_col get; set;

object output_dyn get;

object output_mask get;

object output_mask_dyn get;

object output_row get; set;

object output_shape get;

object output_shape_dyn get;

object padding get; set;

object PythonObject get;

bool stateful get; set;

object strides 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;

bool use_bias get; set;

object variables get;

object variables_dyn get;

IList<object> weights get;

object weights_dyn get;