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:
class Perceptron:
def __init__(self, layers):
self.layers = layers
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.
Solution
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single
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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:
class Perceptron:
def __init__(self, layers):
self.layers = layers
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.
Solution
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Awesome!
Completion rate improved to 4single
