graphenv.examples.tsp.tsp_model.TSPModel

class TSPModel(*args, num_nodes, hidden_dim=32, embed_dim=32, **kwargs)

Bases: graphenv.examples.tsp.tsp_model.BaseTSPModel, ray.rllib.models.tf.tf_modelv2.TFModelV2

Initializes a TFModelV2 instance.

Here is an example implementation for a subclass MyModelClass(TFModelV2):

def __init__(self, *args, **kwargs):
    super(MyModelClass, self).__init__(*args, **kwargs)
    input_layer = tf.keras.layers.Input(...)
    hidden_layer = tf.keras.layers.Dense(...)(input_layer)
    output_layer = tf.keras.layers.Dense(...)(hidden_layer)
    value_layer = tf.keras.layers.Dense(...)(hidden_layer)
    self.base_model = tf.keras.Model(
        input_layer, [output_layer, value_layer])

Methods

context	Returns a contextmanager for the current TF graph.
custom_loss	Override to customize the loss function used to optimize this model.
forward	Tensorflow/Keras style forward method.
forward_vertex	Forward function returning a value and weight tensor for the vertices observed via input_dict (a dict of tensors for each vertex property)
from_batch
get_initial_state	Get the initial recurrent state values for the model.
import_from_h5	Imports weights from an h5 file.
is_time_major	If True, data for calling this ModelV2 must be in time-major format.
last_output	Returns the last output returned from calling the model.
metrics	Override to return custom metrics from your model.
register_variables	Register the given list of variables with this model.
trainable_variables	Returns the list of trainable variables for this model.
update_ops	Return the list of update ops for this model.
value_function	returns A tensor of current state values.
variables	Returns the list (or a dict) of variables for this model.

Parameters

• num_nodes (int) –

• hidden_dim (int) –

• embed_dim (int) –