NumPy-neural-net-8.py

learning_rate = 0.01
max_epoch = 1000000

for epoch in range(1,max_epoch+1):
    y_hat_train = forward_prop(X_train) # update y_hat
    backward_prop(y_train, y_hat_train) # update (dW,db)
    
    for layer_index in range(1,len(layers_dim)):        # update (W,b)
        neural_net[layer_index].W = neural_net[layer_index].W - learning_rate * neural_net[layer_index].dW
        neural_net[layer_index].b = neural_net[layer_index].b - learning_rate * neural_net[layer_index].db
    
    if epoch % 100000 == 0:
        print(f'{get_loss(y_train, y_hat_train):.4f}')