LostTech.TensorFlow : API Documentation

Type WideDeepModel

Namespace tensorflow.keras.experimental

Parent Model

Interfaces IWideDeepModel

Wide & Deep Model for regression and classification problems.

This model jointly train a linear and a dnn model.

Example: Both linear and dnn model can be pre-compiled and trained separately before jointly training:

Example:
Show Example 
linear_model = LinearModel()
            dnn_model = keras.Sequential([keras.layers.Dense(units=64),
                                         keras.layers.Dense(units=1)])
            combined_model = WideDeepModel(dnn_model, linear_model)
            combined_model.compile(optimizer=['sgd', 'adam'], 'mse', ['mse'])
            # define dnn_inputs and linear_inputs as separate numpy arrays or
            # a single numpy array if dnn_inputs is same as linear_inputs.
            combined_model.fit([dnn_inputs, linear_inputs], y, epochs)
            # or define a single tf.data.Dataset that contains a single tensor or
            # separate tensors for dnn_inputs and linear_inputs.
            dataset = tf.data.Dataset.from_tensors(([dnn_inputs, linear_inputs], y))
            combined_model.fit(dataset, epochs)

Properties

Public properties

object activation get; set;

PythonFunctionContainer activity_regularizer get; set;

object activity_regularizer_dyn get; set;

bool built get; set;

Sequential dnn_model 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;

IList<object> input_spec get;

object input_spec_dyn get;

IList<object> inputs get; set;

IList<Layer> layers get;

object layers_dyn get;

object linear_model get; set;

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;

IList<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;