In [12]: # Wrapping the vectors in NumPy arrays
In [13]: input_vector = np.array([1.66, 1.56])
In [14]: weights_1 = np.array([1.45, -0.66])
In [15]: bias = np.array([0.0])
In [16]: def sigmoid(x):
...: return 1 / (1 + np.exp(-x))
In [17]: def make_prediction(input_vector, weights, bias):
...: layer_1 = np.dot(input_vector, weights) + bias
...: layer_2 = sigmoid(layer_1)
...: return layer_2
In [18]: prediction = make_prediction(input_vector, weights_1, bias)
In [19]: print(f"The prediction result is: {prediction}")
Out[19]: The prediction result is: [0.7985731]