Machine Learning for Developers

Playing with the sigmoid

Next, we will do an exercise to get an idea of how the sigmoid changes when multiplied by the weights and shifted by the bias to accommodate the final function towards its minimum. Let's then vary the possible parameters of a single sigmoid first and see it stretch and move:

  ws=np.arange(-1.0, 1.0, 0.2)
    bs=np.arange(-2.0, 2.0, 0.2)
    xs=np.arange(-4.0, 4.0, 0.1)
    plt.figure(figsize=(20,10))
    ax=plt.subplot(121)
    for i in ws:
        plt.plot(xs,  Sigmoid.getTransferFunction(i *xs),label= str(i));
    ax.set_title('Sigmoid variants in w')
    plt.legend(loc='upper left');

    ax=plt.subplot(122)
    for i in bs:
        plt.plot(xs,  Sigmoid.getTransferFunction(i +xs),label= str(i));
    ax.set_title('Sigmoid variants in b')
    plt.legend(loc='upper left'