Since our goal is to implement a **multilayer perceptron**, creating a `Perceptron` class will simplify model initialization. Its only **attribute**, `layers` is essentially a list of the `Layer` objects that define the structure of the network:

```python
class Perceptron:
    def __init__(self, layers):
        self.layers = layers
```

The variables used to initialize the layers are the following:

- `input_size`: the number of **input features**;
- `hidden_size`: the number of neurons in **each hidden layer** (both hidden layers will have the same number of neurons in this case);
- `output_size`: the number of neurons in the **output layer**.

The structure of the resulting perceptron should be as follows:

import unittest
import importlib
import numpy as np


def _dynamic_test(test_case, condition, success_msg, failure_msg):
    if condition:
        test_case._testMethodName = success_msg
        test_case.assertTrue(True, success_msg)
    else:
        test_case._testMethodName = failure_msg
        test_case.fail(failure_msg)


class TestPerceptron(unittest.TestCase):

    @classmethod
    def setUpClass(cls):
        cls.user_code = importlib.import_module("user_code")
        np.random.seed(10)

        cls.input_size = 2
        cls.hidden_size = 3
        cls.output_size = 1

        cls.perceptron = cls.user_code.perceptron
        cls.layers = cls.perceptron.layers

    def test_layers_structure(self):
        _dynamic_test(
            self,
            len(self.layers) == 3,
            "Three layers are defined",
            "Incorrect number of layers defined",
        )

    def test_weights_shapes(self):
        shapes = [layer.weights.shape for layer in self.layers]
        expected = [
            (self.hidden_size, self.input_size),
            (self.hidden_size, self.hidden_size),
            (self.output_size, self.hidden_size),
        ]
        _dynamic_test(
            self,
            shapes == expected,
            "Weights shapes are correct",
            f"Weights shapes are incorrect: got {shapes}, expected {expected}",
        )

    def test_biases_shapes(self):
        shapes = [layer.biases.shape for layer in self.layers]
        expected = [
            (self.hidden_size, 1),
            (self.hidden_size, 1),
            (self.output_size, 1),
        ]
        _dynamic_test(
            self,
            shapes == expected,
            "Bias shapes are correct",
            "Bias shapes are incorrect",
        )

    def test_weights_range(self):
        all_in_range = all(
            np.all((layer.weights >= -1) & (layer.weights < 1)) for layer in self.layers
        )
        _dynamic_test(
            self,
            all_in_range,
            "All weights are within [-1, 1)",
            "Weights are outside expected range",
        )

    def test_biases_range(self):
        all_in_range = all(
            np.all((layer.biases >= -1) & (layer.biases < 1)) for layer in self.layers
        )
        _dynamic_test(
            self,
            all_in_range,
            "All biases are within [-1, 1)",
            "Biases are outside expected range",
        )

    def test_third_neuron_weights_second_hidden_layer(self):
        w_user = np.round(self.layers[1].weights[2], 2)
        np.random.seed(10)
        _ = np.random.uniform(-1, 1, (self.hidden_size, self.input_size))
        _ = np.random.uniform(-1, 1, (self.hidden_size, 1))
        ref_w = np.random.uniform(-1, 1, (self.hidden_size, self.hidden_size))
        np.round(ref_w[2], 2)
        _dynamic_test(
            self,
            np.allclose(w_user, np.round(ref_w[2], 2), atol=1e-6),
            "Weights of 3rd neuron in 2nd hidden layer are correct",
            "Weights of 3rd neuron in 2nd hidden layer are incorrect",
        )

    def test_output_layer_weights(self):
        w_user = np.round(self.layers[2].weights[0], 2)
        np.random.seed(10)
        # Hidden layer 1: weights + biases
        np.random.uniform(-1, 1, (self.hidden_size, self.input_size))
        np.random.uniform(-1, 1, (self.hidden_size, 1))
        # Hidden layer 2: weights + biases
        np.random.uniform(-1, 1, (self.hidden_size, self.hidden_size))
        np.random.uniform(-1, 1, (self.hidden_size, 1))
        # Output layer weights
        ref_w = np.random.uniform(-1, 1, (self.output_size, self.hidden_size))
        _dynamic_test(
            self,
            np.allclose(w_user, np.round(ref_w[0], 2), atol=1e-6),
            "Weights of output neuron are correct",
            "Weights of output neuron are incorrect",
        )


if __name__ == "__main__":
    unittest.main(argv=["first-arg-is-ignored"], exit=False)


test_code.py

Neural networks are powerful algorithms inspired by the structure of the human brain that are used to solve complex machine learning problems. You will build your own Neural Network from scratch to understand how it works. After this course, you will be able to create neural networks for solving classification and regression problems using the scikit-learn library.

First, we will discuss what a neural network is and how it works. And also consider the scope of its application.

Next, we will try to build our own neural network and see how efficiently it copes with learning. We will also consider a ready-made solution from the scikit-learn library.

Finally, we will give you some additional useful information on how to understand which model to use and what types of neural networks there are. To complete the course, you will be tested on your acquired knowledge.

Challenge: Creating a Perceptron

Solution

Awesome!

Challenge: Creating a Perceptron

Solution