Source code for miprometheus.models.controllers.feedforward_controller

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# Copyright (C) IBM Corporation 2018
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""lstm_controller.py: pytorch module implementing wrapper for feedforward controller."""
__author__ = "Tomasz Kornuta/Ryan L. McAvoy"

import torch
from torch.nn import Module


[docs]class FeedforwardController(Module): """ A wrapper class for a feedforward controller. """
[docs] def __init__(self, params): """ Constructor. :param params: Dictionary of parameters. """ # Call constructor of base class. super(FeedforwardController, self).__init__() # Parse parameters. # Set input and hidden dimensions. self.input_size = params["input_size"] self.ctrl_hidden_state_size = params["output_size"] # Processes input and produces hidden state of the controller. self.ff = torch.nn.Linear(self.input_size, self.ctrl_hidden_state_size)
[docs] def init_state(self, batch_size): """ Returns 'zero' (initial) state tuple - in this case empy tuple. :param batch_size: Size of the batch in given iteraction/epoch. :returns: Initial state tuple - empty (). """ return ()
[docs] def forward(self, inputs_BxI, prev_state_tuple): """ Controller forward function. :param inputs_BxI: a Tensor of input data of size [BATCH_SIZE x INPUT_SIZE] :param prev_state_tuple: unused - empty tuple () :returns: outputs a Tensor of size [BATCH_SIZE x OUTPUT_SIZE] and empty tuple. """ # Execute feedforward pass. hidden_state = self.ff(inputs_BxI) # Return hidden_state (as output) and empty state tuple. return hidden_state, ()