Зміст курсу
Introduction to Neural Networks
Introduction to Neural Networks
Challenge: Creating a Perceptron
Since our goal is to implement a multilayer perceptron, creating a Perceptron class will simplify model initialization. Its only attribute, layers is essentially a list of the Layer objects that define the structure of the network:
The variables used to initialize the layers are the following:
input_size: the number of input features;hidden_size: the number of neurons in each hidden layer (both hidden layers will have the same number of neurons in this case);output_size: the number of neurons in the output layer.
The structure of the resulting perceptron should be as follows:
Swipe to start coding
Your goal is to set up the basic structure of the perceptron by implementing its layers:
- Initialize the weights (a matrix) and biases (a vector) with random values from a uniform distribution in range
[-1, 1)using NumPy. - Compute the raw output values of the neurons in the
forward()method of theLayerclass. - Apply the activation function to the raw outputs in the
forward()method of theLayerclass and return the result. - Define three layers in the
Perceptronclass: two hidden layers with the same number of neurons and one output layer. Both hidden layers should use thereluactivation function, while the output layer should usesigmoid.
Рішення
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівДякуємо за ваш відгук!
Challenge: Creating a Perceptron
Since our goal is to implement a multilayer perceptron, creating a Perceptron class will simplify model initialization. Its only attribute, layers is essentially a list of the Layer objects that define the structure of the network:
The variables used to initialize the layers are the following:
input_size: the number of input features;hidden_size: the number of neurons in each hidden layer (both hidden layers will have the same number of neurons in this case);output_size: the number of neurons in the output layer.
The structure of the resulting perceptron should be as follows:
Swipe to start coding
Your goal is to set up the basic structure of the perceptron by implementing its layers:
- Initialize the weights (a matrix) and biases (a vector) with random values from a uniform distribution in range
[-1, 1)using NumPy. - Compute the raw output values of the neurons in the
forward()method of theLayerclass. - Apply the activation function to the raw outputs in the
forward()method of theLayerclass and return the result. - Define three layers in the
Perceptronclass: two hidden layers with the same number of neurons and one output layer. Both hidden layers should use thereluactivation function, while the output layer should usesigmoid.
Рішення
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівДякуємо за ваш відгук!
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів