LostTech.TensorFlow : API Documentation

Type LinearModel

Namespace tensorflow.keras.experimental

Parent Model

Interfaces ILinearModel

Linear Model for regression and classification problems.

This model approximates the following function: $$y = \beta + \sum_{i=1}^{N} w_{i} * x_{i}$$ where $$\beta$$ is the bias and $$w_{i}$$ is the weight for each feature.

Example: This model accepts sparse float inputs as well:

Show Example
model = LinearModel()
            model.compile(optimizer='sgd', loss='mse')
            model.fit(x, y, epochs) 



Public static methods

LinearModel NewDyn(ImplicitContainer<T> units, object activation, ImplicitContainer<T> use_bias, ImplicitContainer<T> kernel_initializer, ImplicitContainer<T> bias_initializer, object kernel_regularizer, object bias_regularizer, IDictionary<string, object> kwargs)

Create a Linear Model.
ImplicitContainer<T> units
Positive integer, output dimension without the batch size.
object activation
Activation function to use. If you don't specify anything, no activation is applied.
ImplicitContainer<T> use_bias
whether to calculate the bias/intercept for this model. If set to False, no bias/intercept will be used in calculations, e.g., the data is already centered.
ImplicitContainer<T> kernel_initializer
Initializer for the `kernel` weights matrices.
ImplicitContainer<T> bias_initializer
Initializer for the bias vector.
object kernel_regularizer
regularizer for kernel vectors.
object bias_regularizer
regularizer for bias vector.
IDictionary<string, object> kwargs
The keyword arguments that are passed on to BaseLayer.__init__.

Public properties

object activation get; set;

PythonFunctionContainer activity_regularizer get; set;

object activity_regularizer_dyn get; set;

object bias get; set;

object bias_initializer get; set;

object bias_regularizer get; set;

bool built get; set;

IList<Dense> dense_layers 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;

object input_spec_dyn get;

object inputs get; set;

object kernel_initializer get; set;

object kernel_regularizer 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<object> 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;

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;