LostTech.TensorFlow : API Documentation

Type Sequential

Namespace tensorflow.keras

Parent Model

Interfaces ISequential

Linear stack of layers.

Methods

Properties

Public instance methods

object add(PythonClassContainer layer)

Adds a layer instance on top of the layer stack.
Parameters
PythonClassContainer layer
layer instance.

object add(object layer)

Adds a layer instance on top of the layer stack.
Parameters
object layer
layer instance.

object add_dyn(object layer)

Adds a layer instance on top of the layer stack.
Parameters
object layer
layer instance.

void pop()

Removes the last layer in the model.

object pop_dyn()

Removes the last layer in the model.

object predict_classes(object x, int batch_size, int verbose)

Generate class predictions for the input samples.

The input samples are processed batch by batch.
Parameters
object x
input data, as a Numpy array or list of Numpy arrays (if the model has multiple inputs).
int batch_size
integer.
int verbose
verbosity mode, 0 or 1.
Returns
object
A numpy array of class predictions.

object predict_classes_dyn(object x, ImplicitContainer<T> batch_size, ImplicitContainer<T> verbose)

Generate class predictions for the input samples.

The input samples are processed batch by batch.
Parameters
object x
input data, as a Numpy array or list of Numpy arrays (if the model has multiple inputs).
ImplicitContainer<T> batch_size
integer.
ImplicitContainer<T> verbose
verbosity mode, 0 or 1.
Returns
object
A numpy array of class predictions.

object predict_proba(object x, int batch_size, int verbose)

Generates class probability predictions for the input samples.

The input samples are processed batch by batch.
Parameters
object x
input data, as a Numpy array or list of Numpy arrays (if the model has multiple inputs).
int batch_size
integer.
int verbose
verbosity mode, 0 or 1.
Returns
object
A Numpy array of probability predictions.

object predict_proba_dyn(object x, ImplicitContainer<T> batch_size, ImplicitContainer<T> verbose)

Generates class probability predictions for the input samples.

The input samples are processed batch by batch.
Parameters
object x
input data, as a Numpy array or list of Numpy arrays (if the model has multiple inputs).
ImplicitContainer<T> batch_size
integer.
ImplicitContainer<T> verbose
verbosity mode, 0 or 1.
Returns
object
A Numpy array of probability predictions.

Public properties

PythonFunctionContainer activity_regularizer get; set;

object activity_regularizer_dyn 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_names get; set;

IList<object> input_shape get;

object input_shape_dyn get;

object input_spec get; set;

Gets the network's input specs.

object input_spec_dyn get;

Gets the network's input specs.

object inputs get; set;

IList<Layer> layers get;

object layers_dyn get;

object loss get; set;

IList<object> loss_functions get; set;

IList<double> loss_weights get; set;

IList<object> losses get;

object losses_dyn get;

IList<object> metrics get;

object metrics_dyn get;

IList<object> metrics_names get;

object metrics_names_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;

object optimizer get; set;

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;

IList<string> output_names get; set;

object output_shape get;

object output_shape_dyn get;

object outputs get; set;

object predict_function get; set;

object PythonObject get;

Nullable<bool> run_eagerly get; set;

object run_eagerly_dyn get; set;

string sample_weight_mode get; set;

IList<Tensor> sample_weights get;

object sample_weights_dyn get;

IList<object> state_updates get;

object state_updates_dyn get;

bool stateful get;

object stateful_dyn get;

ValueTuple<object> submodules get;

object submodules_dyn get;

bool supports_masking get; set;

object test_function get; set;

Nullable<double> total_loss get; set;

object train_function 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;