EasiestANN_2.py

# define activation function
def relu(x):
    return np.where(x <= 0, 0, x)

# input data
input_layer = np.array([[5], [2]])
weights_1 = np.array([[2, -2], [3, 1]]) 
weights_2 = np.array([[1], [2]])

# computation of first hidden layer
node_0_output = np.dot(weights_1.T, input_layer)
node_1_input = relu(node_0_output)

# computation of second hidden layer
node_1_output = np.dot(weights_2.T, node_1_input)
output_layer = relu(node_1_output)

# print output_layer
output_layer