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Introduction to Neural Networks

bookForward Propagation

You have already implemented forward propagation for a single layer in the previous chapter. Now, the goal is to implement complete forward propagation, from inputs to outputs.

To implement the entire forward propagation process, you need to define the forward() method in the Perceptron class. This method performs forward propagation layer by layer by calling the respective method for each layer:

class Perceptron:
    def __init__(self, layers):
        self.layers = layers

    def forward(self, inputs):
        x = inputs
        for layer in ...:
            # Pass x layer by layer
            x = ...

        return ...

The inputs pass through the first hidden layer, with each layer's outputs serving as inputs for the next, until reaching the final layer to produce the final output.

Task

Swipe to start coding

Your goal is to implement forward propagation for the perceptron:

  1. Iterate over the layers of the perceptron.
  2. Pass x through each layer in the network sequentially.
  3. Return the final output after all layers have processed the input.

If the forward() method is implemented correctly, the perceptron should output a single number between 0 and 1 when given certain inputs (e.g, [1, 0]).

Solution

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SectionΒ 2. ChapterΒ 5
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bookForward Propagation

You have already implemented forward propagation for a single layer in the previous chapter. Now, the goal is to implement complete forward propagation, from inputs to outputs.

To implement the entire forward propagation process, you need to define the forward() method in the Perceptron class. This method performs forward propagation layer by layer by calling the respective method for each layer:

class Perceptron:
    def __init__(self, layers):
        self.layers = layers

    def forward(self, inputs):
        x = inputs
        for layer in ...:
            # Pass x layer by layer
            x = ...

        return ...

The inputs pass through the first hidden layer, with each layer's outputs serving as inputs for the next, until reaching the final layer to produce the final output.

Task

Swipe to start coding

Your goal is to implement forward propagation for the perceptron:

  1. Iterate over the layers of the perceptron.
  2. Pass x through each layer in the network sequentially.
  3. Return the final output after all layers have processed the input.

If the forward() method is implemented correctly, the perceptron should output a single number between 0 and 1 when given certain inputs (e.g, [1, 0]).

Solution

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

SectionΒ 2. ChapterΒ 5
single

single

close

Awesome!

Completion rate improved to 4

bookForward Propagation

Swipe to show menu

You have already implemented forward propagation for a single layer in the previous chapter. Now, the goal is to implement complete forward propagation, from inputs to outputs.

To implement the entire forward propagation process, you need to define the forward() method in the Perceptron class. This method performs forward propagation layer by layer by calling the respective method for each layer:

class Perceptron:
    def __init__(self, layers):
        self.layers = layers

    def forward(self, inputs):
        x = inputs
        for layer in ...:
            # Pass x layer by layer
            x = ...

        return ...

The inputs pass through the first hidden layer, with each layer's outputs serving as inputs for the next, until reaching the final layer to produce the final output.

Task

Swipe to start coding

Your goal is to implement forward propagation for the perceptron:

  1. Iterate over the layers of the perceptron.
  2. Pass x through each layer in the network sequentially.
  3. Return the final output after all layers have processed the input.

If the forward() method is implemented correctly, the perceptron should output a single number between 0 and 1 when given certain inputs (e.g, [1, 0]).

Solution

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

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