daae1077243385f0cb3eeaa52729991b8fc6d704,tensorforce/core/networks/layer.py,Input,tf_apply,#Input#Any#Any#,162

Before Change

                inputs.append(self.named_tensors[name])
            else:
                // handling nested dict
                input_element = [inner_named_tensors[name]
                                 for inner_name, inner_named_tensors in self.named_tensors.items()
                                 if isinstance(inner_named_tensors, dict) and name in inner_named_tensors]

                if len(input_element) == 0:
                    keys = sorted(self.named_tensors)
                    raise TensorForceError(

After Change

            if len(shape) > len(max_shape):
                max_shape = shape

        for n, tensor in enumerate(inputs):
            shape = util.shape(x=tensor)
            if len(shape) < len(max_shape):
                // assert shape == max_shape[:len(shape)], (shape, max_shape)
                for i in range(len(shape), len(max_shape)):
                    // assert max_shape[i] == 1, (shape, max_shape)
                    tensor = tf.expand_dims(input=tensor, axis=i)
                inputs[n] = tensor
            // else:
            //     assert shape == max_shape, (shape, max_shape)

        if self.aggregation_type == "concat":
            x = tf.concat(values=inputs, axis=self.axis)
        elif self.aggregation_type == "stack":
            x = tf.stack(values=inputs, axis=self.axis)

Instances