9000ad931722064fb2efe38649389a5154538ce4,tensorforce/core/networks/auto.py,AutoNetwork,internals_spec,#Any#Any#Any#Any#,125

Before Change

        internals_spec = super().internals_spec(network=network)

        if network is None:
            if internal_rnn:
                for name, internal_spec in InternalLstm.internals_spec(size=final_size).items():
                    internals_spec["internal_lstm-" + name] = internal_spec

        return internals_spec

    def tf_apply(self, x, internals, return_internals=False):
        // State-specific layers

After Change

            elif len(spec["shape"]) == 2 - embedding:
                layer = "conv1d"
            elif len(spec["shape"]) == 3 - embedding:
                layer = "conv2d"
            elif len(spec["shape"]) == 0:
                layers.append(self.add_module(name=(name + "-flatten"), module="flatten"))
                layer = "dense"
            else:
                raise TensorforceError.unexpected()

            // Repeat layer according to depth (one less if embedded)
            for n in range(self.depth - embedding):
                layers.append(
                    self.add_module(
                        name=(name + "-" + layer + str(n)), module=layer, size=self.size

Instances