Neural-Network.py

import numpy as np
import nnfs
from nnfs.datasets import spiral_data

nnfs.init()

class Layer_Dense:
    def __init__(self, n_inputs, n_neurons):
        self.weights = 0.01 * 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)

class Activation_Softmax:
    def forward(self, inputs):
        exp_values = np.exp(inputs - np.max(inputs, axis=1, keepdims=True))
        probabilities = exp_values / np.sum(exp_values, axis=1, keepdims=True)
        self.output = probabilities
    
class Loss:
    def calculate(self, output, y):
        sample_losses = self.forward(output, y)
        data_loss = np.mean(sample_losses)
        return data_loss

class Loss_CategoricalCrossentropy(Loss):
    def forward(self, y_pred, y_true):
        samples = len(y_pred)
        y_pred_clipped = np.clip(y_pred, 1e-7, 1 - 1e-7)

    # Probabilities for target values -
    # only if categorical labels
        if len(y_true.shape) == 1:
            correct_confidences = y_pred_clipped[range(samples),y_true]
    # Mask values - only for one-hot encoded labels
        elif len(y_true.shape) == 2:
            correct_confidences = np.sum(y_pred_clipped*y_true,axis=1)
    
        negative_log_likelihoods = -np.log(correct_confidences)
        return negative_log_likelihoods

# Create dataset
X, y = spiral_data(samples=100, classes=3)

# Create Dense layer with 2 input features and 3 output values
dense1 = Layer_Dense(2, 3)
# Create ReLU activation (to be used with Dense layer):
activation1 = Activation_ReLU()

# Create second Dense layer with 3 input features (as we take output
# of previous layer here) and 3 output values
dense2 = Layer_Dense(3, 3)
# Create Softmax activation (to be used with Dense layer):
activation2 = Activation_Softmax()

# Create loss function
loss_function = Loss_CategoricalCrossentropy()

# Perform a forward pass of our training data through this layer
dense1.forward(X)
# Perform a forward pass through activation function
# it takes the output of first dense layer here
activation1.forward(dense1.output)

# Perform a forward pass through second Dense layer
# it takes outputs of activation function of first layer as inputs
dense2.forward(activation1.output)
# Perform a forward pass through activation function
# it takes the output of second dense layer here
activation2.forward(dense2.output)

#looking at few values of output
print(activation2.output[:5])

loss = loss_function.calculate(activation2.output, y)
# Print loss value
print('Loss:', loss)