nn-scratch-python/binary_logistic_regression.py at main · kaichenchai/nn-scratch-python · GitHub

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

nnfs.init()

if __name__ == "__main__":
    X, y = spiral_data(samples=100, classes=2)
    y = y.reshape(-1, 1)
    dense1 = Layer_Dense(2, 64, weight_regularizer_l2=5e-4, bias_regularizer_l2=5e-4)
    activation1 = Activation_ReLU()
    dense2 = Layer_Dense(64, 1)
    activation2 = Activation_Sigmoid()
    loss_function = Loss_BinaryCrossentropy()
    optimizer = Optimizer_Adam(decay=5e-7)

    for epoch in range(10001):
        dense1.forward(X)
        activation1.forward(dense1.output)
        dense2.forward(activation1.output)
        activation2.forward(dense2.output)

        data_loss = loss_function.calculate(activation2.output, y)
        regularization_loss = loss_function.regularization_loss(dense1) + loss_function.regularization_loss(dense2)

        loss = data_loss + regularization_loss

        predictions = (activation2.output > 0.5) * 1
        accuracy = np.mean(predictions==y)

        if not epoch % 100:
            print(f"epoch: {epoch}," +
                  f"acc: {accuracy:.3f}," +
                  f"loss: {loss:.3f} (" +
                  f"data_loss: {data_loss:.3f},"
                  f"reg_loss: {regularization_loss:.3f}),"
                  f'lr: {optimizer.current_learning_rate}')

        loss_function.backward(activation2.output, y)
        activation2.backward(loss_function.dinputs)
        dense2.backward(activation2.dinputs)
        activation1.backward(dense2.dinputs)
        dense1.backward(activation1.dinputs)

        optimizer.pre_update_params()
        optimizer.update_params(dense1)
        optimizer.update_params(dense2)
        optimizer.post_update_params()

    #validation of the model
    #test ds
    X_test, y_test = spiral_data(samples=100, classes=2)
    y_test = y_test.reshape(-1, 1)
    dense1.forward(X_test)
    activation1.forward(dense1.output)
    dense2.forward(activation1.output)
    activation2.forward(dense2.output)
    loss = loss_function.calculate(activation2.output, y_test)

    #calculate accuracies
    predictions = (activation2.output > 0.5) * 1
    accuracy = np.mean(predictions==y_test)
    print(f"validation, acc: {accuracy:.3f}, loss: {loss:.3f}")