Forward 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.
Swipe to start coding
Your goal is to implement forward propagation for the perceptron:
- Iterate over the layers of the perceptron.
- Pass
xthrough each layer in the network sequentially. - 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]).
Løsning
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Completion rate improved to 4Awesome!
Completion rate improved to 4Forward 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.
Swipe to start coding
Your goal is to implement forward propagation for the perceptron:
- Iterate over the layers of the perceptron.
- Pass
xthrough each layer in the network sequentially. - 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]).
Løsning
Skift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående mulighederTak for dine kommentarer!
single
Awesome!
Completion rate improved to 4Forward Propagation
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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.
Swipe to start coding
Your goal is to implement forward propagation for the perceptron:
- Iterate over the layers of the perceptron.
- Pass
xthrough each layer in the network sequentially. - 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]).
Løsning
Skift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående mulighederTak for dine kommentarer!
