LostTech.TensorFlow : API Documentation

Type SpatialDropout2D

Namespace tensorflow.keras.layers

Parent Dropout

Interfaces ISpatialDropout2D

Spatial 2D version of Dropout.

This version performs the same function as Dropout, however it drops entire 2D feature maps instead of individual elements. If adjacent pixels within feature maps are strongly correlated (as is normally the case in early convolution layers) then regular dropout will not regularize the activations and will otherwise just result in an effective learning rate decrease. In this case, SpatialDropout2D will help promote independence between feature maps and should be used instead.



Public static methods

SpatialDropout2D NewDyn(object rate, object data_format, IDictionary<string, object> kwargs)

Create an instance of `HParams` from keyword arguments.

The keyword arguments specify name-values pairs for the hyperparameters. The parameter types are inferred from the type of the values passed.

The parameter names are added as attributes of `HParams` object, so they can be accessed directly with the dot notation `hparams._name_`.

Example: Note that a few names are reserved and cannot be used as hyperparameter names. If you use one of the reserved name the constructor raises a `ValueError`.
object rate
object data_format
IDictionary<string, object> kwargs
Key-value pairs where the key is the hyperparameter name and the value is the value for the parameter.
Show Example
# Define 3 hyperparameters: 'learning_rate' is a float parameter,
            # 'num_hidden_units' an integer parameter, and 'activation' a string
            # parameter.
            hparams = tf.contrib.training.HParams(
                learning_rate=0.1, num_hidden_units=100, activation='relu') 

hparams.activation ==> 'relu'

Public properties

PythonFunctionContainer activity_regularizer get; set;

object activity_regularizer_dyn 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;

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;

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;

Nullable<ValueTuple<object, int, object>> noise_shape get; set;

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;

double rate get; set;

object seed get; set;

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;