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Worksheet

8.4 A tiny network

Learning · Robot club · about 30 min

BugBotLab
NameClassDate

What this lesson is about

Weights, a loss, and watching it fall: training in the browser.

Questions 7 marks in all

  1. [1 mark]What does a trained network keep, so the data can be thrown away?

    1. AIts weights
    2. BEvery sample it was shown
    3. CJust the list of labels
    4. DThe loss from the last epoch

  2. [1 mark]What does this program print?

    labels = sorted(["open", "wall-ahead", "gap-left", "gap-right"])
row = [0] * len(labels)
row[labels.index("open")] = 1
print(labels)
print(row)

  3. [1 mark]What does this program print?

    for reading in [400, 30, 10]:
    print((reading - 30) / 20)

  4. [1 mark]What does this program print?

    inputs = [1.0, -0.5]
weights = [2.0, 4.0]
bias = 0.5
total = sum(i * w for i, w in zip(inputs, weights)) + bias
print(total)

  5. [1 mark]Put one epoch of training in order.

    Number the lines 1 to 4 to put them in the right order.

    1. Step: move every weight a little
    2. Measure how wrong they are: the loss
    3. Backward: work out the gradients
    4. Forward: work out the outputs from the inputs

  6. [1 mark]The learning rate lr is set far too big. What do you see?

    1. AThe loss bounces about and never settles
    2. BThe loss creeps down very slowly
    3. CThe loss drops to zero at once
    4. DTraining is skipped

  7. [1 mark]Scaling the inputs as X / 100 instead of (X - 30) / 20 leaves the network stuck at about 88%. Why?

    1. AThe inputs are no longer centred near zero, which gives the network worse numbers to work with
    2. BDividing by 100 deletes some samples
    3. CThe labels stop being one-hot
    4. DThe network needs more hidden numbers

The task: a tiny network

Train the network on DATA, printing epoch <n>: loss <value> every 50 epochs, then print accuracy: <n>% and get it above 90. Do not drive.

# the two lines every program starts with: the commands, then the robot
from bugbot import *
connect()
DATA = [
    ([54, 52, 51, 51, 51, 51, 52, 54, 54, 52, 51, 51, 51, 51, 52, 54], 'open'),
    ([16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16], 'wall-ahead'),
    ([36, 47, 46, 46, 46, 16, 16, 16, 36, 47, 46, 46, 46, 16, 16, 16], 'gap-left'),
    ([16, 16, 16, 46, 46, 46, 47, 36, 16, 16, 16, 46, 46, 46, 47, 36], 'gap-right'),
    ([400, 400, 61, 61, 61, 61, 400, 400, 96, 62, 61, 61, 61, 61, 62, 64], 'open'),
    ([52, 50, 49, 49, 49, 49, 50, 52, 51, 50, 49, 49, 49, 49, 50, 51], 'open'),
    ([39, 38, 37, 37, 37, 37, 38, 39, 39, 38, 37, 37, 37, 37, 38, 39], 'open'),
    ([400, 400, 61, 61, 61, 61, 400, 400, 64, 62, 61, 61, 61, 61, 62, 64], 'open'),
    ([52, 50, 49, 49, 49, 49, 50, 52, 51, 50, 49, 49, 49, 49, 50, 51], 'open'),
    ([39, 38, 37, 37, 37, 37, 38, 39, 39, 38, 37, 37, 37, 37, 38, 39], 'open'),
    ([400, 400, 61, 61, 61, 61, 400, 400, 64, 62, 61, 61, 61, 61, 62, 96], 'open'),
    ([52, 50, 49, 49, 49, 49, 50, 52, 51, 50, 49, 49, 49, 49, 50, 51], 'open'),
    ([39, 38, 37, 37, 37, 37, 38, 39, 39, 38, 37, 37, 37, 37, 38, 39], 'open'),
    ([26, 25, 25, 25, 25, 25, 25, 26, 26, 25, 25, 25, 25, 25, 25, 26], 'wall-ahead'),
    ([18, 17, 17, 17, 17, 17, 17, 18, 17, 17, 17, 17, 17, 17, 17, 17], 'wall-ahead'),
    ([9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9], 'wall-ahead'),
    ([26, 25, 25, 25, 25, 25, 25, 26, 26, 25, 25, 25, 25, 25, 25, 26], 'wall-ahead'),
    ([18, 17, 17, 17, 17, 17, 17, 18, 17, 17, 17, 17, 17, 17, 17, 17], 'wall-ahead'),
    ([9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9], 'wall-ahead'),
    ([26, 25, 25, 25, 25, 25, 25, 26, 26, 25, 25, 25, 25, 25, 25, 26], 'wall-ahead'),
    ([18, 17, 17, 17, 17, 17, 17, 18, 17, 17, 17, 17, 17, 17, 17, 17], 'wall-ahead'),
    ([9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9], 'wall-ahead'),
    ([24, 34, 55, 59, 59, 59, 29, 30, 24, 33, 55, 59, 59, 59, 29, 30], 'gap-left'),
    ([24, 34, 51, 51, 51, 51, 25, 22, 24, 33, 51, 51, 51, 51, 25, 22], 'gap-left'),
    ([24, 34, 43, 43, 43, 43, 44, 18, 24, 33, 43, 43, 43, 43, 44, 18], 'gap-left'),
    ([42, 58, 59, 59, 29, 29, 29, 30, 42, 58, 59, 59, 29, 29, 29, 30], 'gap-left'),
    ([42, 52, 51, 51, 21, 21, 21, 22, 42, 52, 51, 51, 21, 21, 21, 22], 'gap-left'),
    ([42, 44, 43, 43, 13, 13, 13, 13, 42, 44, 43, 43, 13, 13, 13, 13], 'gap-left'),
    ([30, 29, 29, 29, 59, 59, 58, 42, 30, 29, 29, 29, 59, 59, 58, 42], 'gap-right'),
    ([22, 21, 21, 21, 51, 51, 52, 42, 22, 21, 21, 21, 51, 51, 52, 42], 'gap-right'),
    ([13, 13, 13, 13, 43, 43, 44, 42, 13, 13, 13, 13, 43, 43, 44, 42], 'gap-right'),
    ([30, 29, 59, 59, 59, 55, 34, 24, 30, 29, 59, 59, 59, 55, 33, 24], 'gap-right'),
    ([22, 25, 51, 51, 51, 51, 34, 24, 22, 25, 51, 51, 51, 51, 33, 24], 'gap-right'),
    ([18, 44, 43, 43, 43, 43, 34, 24, 18, 44, 43, 43, 43, 43, 33, 24], 'gap-right'),
]
import numpy as np
X = (np.array([f for f, _ in DATA], dtype=float) - 30) / 20
print('samples:', X.shape)

Plan your program here, then type it in and press Run.

QR code
Do it on the robot
www.bugbotlab.com/learn/8-4-a-tiny-network/
The simulator checks it and tells you when it passes. Nothing to install, no account.

Challenges

  1. Try 4 hidden numbers, then 16. Does it still reach 100%? How fast?
  2. Try lr of 0.05 and of 3. Print the loss every 10 epochs to see what each does.
  3. Classify the current view: put tof_grid()[16:32] (the 16 readings that look straight ahead) through the same scaling and the trained weights, and print the label with the biggest output.