nnet.py

import numpy as np
import matplotlib.pyplot as plt
import nnfs

nnfs.init()

def create_dataset(points, classes):
    X = np.zeros((points * classes, 2))
    y = np.zeros(points * classes, dtype='uint8')

    for class_number in range(classes):
        ix = range(points * class_number, points * (class_number + 1))
        r = np.linspace(0.0, 1, points) # radius
        t = np.linspace(class_number * 4, (class_number + 1) * 4, points) + np.random.randn(points) * 0.2
        X[ix] = np.c_[r * np.sin(t * 2.5), r * np.cos(t * 2.5)]
        y[ix] = class_number
    return X, y

X, y = create_dataset(100, 3)

plt.scatter(X[:,0], X[:,1])
plt.show()

plt.scatter(X[:,0], X[:,1], c=y, cmap='brg')
plt.show()
'''
# X is our input data set
X = [[1, 2, 3, 2.5],
     [2.0, 5.0, -1.0, 2.0],
     [-1.5, 2.7, 3.3, -0.8]]
'''
class Layer_Dense:
    def __init__(self, n_inputs: int, n_neurons: int):
        self.weights = 0.10 * np.random.randn(n_inputs, n_neurons)
        self.biases = np.zeros((1, n_neurons))

    def forward(self, inputs):
        self.output = np.dot(inputs, self.weights) + self.biases

class Activation_ReLU:
    def forward(self, inputs):
        self.output = np.maximum(0, inputs)

layer1 = Layer_Dense(2, 5)
activation1 = Activation_ReLU()

layer1.forward(X)
activation1.forward(layer1.output)
print(activation1.output)