Functions
Here is a list of classes in the package.
Overview:
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class neural_network.AbstractFunction[source]
Class to represent an abstract function
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__call__(x: float | List[float], w: List[float] | None = None) → float[source]
Calling of the function
- Parameters:
-
- Returns:
The output value
- Return type:
float
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__init__()[source]
Constructor method
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gradient(x: float | List[float], w: List[float] | None = None) → float | List[float][source]
The gradient of the function
- Parameters:
-
- Returns:
The gradient of the function
- Return type:
float | List[float]
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class neural_network.CrossEntropyLoss[source]
Class to represent the cross entropy loss function for classification
networks.
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__call__(y_hat: List[float], y: int) → float[source]
The loss function.
- Parameters:
y_hat (List[float]) – Output vector from softmax layer
y (int) – Target class (in {0, 1, …})
- Returns:
Cross entropy loss value
- Return type:
float
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__init__()[source]
Constructor method
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class neural_network.MSELoss[source]
Class to represent the mean squared error loss for regressional neural
networks.
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__call__(y_hat: float, y: float) → float[source]
The loss function.
- Parameters:
-
- Returns:
Squared difference of the two values
- Return type:
float
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__init__()[source]
Constructor method
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gradient(y_hat: float, y: float) → float[source]
The gradient of the loss function.
- Parameters:
-
- Returns:
Difference of the two values multiplied by 2
- Return type:
float
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class neural_network.ReLU(leak: float = 0.0)[source]
Class to represent the ReLU function
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__call__(x: float, w: List[float] | None = None) → float[source]
Implementation of ReLU
- Parameters:
-
- Returns:
Output to function
- Return type:
float
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__init__(leak: float = 0.0)[source]
Constructor method
- Parameters:
leak (float) – The parameter to be used if this is a LeakyReLU
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gradient(x: float, w: List[float] | None = None) → float[source]
Gradient of ReLU
- Parameters:
-
- Returns:
Gradient of ReLU
- Return type:
float
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class neural_network.Sigmoid[source]
Class to represent the Sigmoid function
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__call__(x: float, w: List[float] | None = None) → float[source]
Implementation of Sigmoid
- Parameters:
-
- Returns:
Output to function
- Return type:
float
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__init__()[source]
Constructor method
-
gradient(x: float, w: List[float] | None = None) → float[source]
Gradient of Sigmoid
- Parameters:
-
- Returns:
Gradient of Sigmoid
- Return type:
float
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class neural_network.Softmax[source]
Class to represent the softmax function
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__call__(z_k: float) → float[source]
The loss function. Note we multiply top and bottom by _max_z to
avoid any overflow error.
- Parameters:
z_k (float) – The value of an output node
- Returns:
The softmax output
- Return type:
float
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__init__()[source]
Constructor method
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normalise(z: List[float])[source]
Calculates the normalisation constant for the softmax function. We
wish to avoid any overflow errors, so we multiply the normalisation
constant by \(e^{-m}\). We account for this when finding the
Softmax value later.
- Parameters:
z (List[float]) – The vector of values from the output layer of the main network