![]() ![]() Let’s see how we can convert the above function into a lambda function: # Developing the Sigmoid Function in numpy as a Lambda Function Similarly, in many deep learning models, you’ll encounter the function written as an anonymous lambda function. Let’s see how we can make use of the function by passing in the value of 0.5: # Testing Out Our Sigmoid Function In the function above, we made use of the numpy.exp() function, which raises e to the power of the negative argument. Let’s see how we can accomplish this: # Developing the Sigmoid Function in numpy While numpy doesn’t provide a built-in function for calculating the sigmoid function, it makes it easy to develop a custom function to accomplish this. How to Implement the Sigmoid Function in Python with numpy Finally, the derivate of the function can be expressed in terms of itself. Similarly, since the step of backpropagation depends on an activation function being differentiable, the sigmoid function is a great option. This is because the function returns a value that is between 0 and 1. The sigmoid function is often used as an activation function in deep learning. You may be wondering how this function is relevant to deep learning. ![]()
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