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Creating a layer

Function Signature

def add_dense(
    neurons: int,
    activation: str = 'sigmoid',
    dropout: float = 0.,
    l1_penalty: float = 0.,
    l2_penalty: float = 0.
) -> None:

Parameters

  • neurons (int): The number of neurons in the dense layer. Required parameter.

  • activation (str, default='sigmoid'): The activation function to use in the dense layer. Valid options are 'sigmoid', 'relu', 'leaky relu' 'tanh', 'softmax', and 'linear'

  • dropout (float, default=0.): The dropout rate to use in the dense layer. This parameter should be a float between 0 and 1, where 0 means no dropout and 1 means all neurons are dropped.

  • l1_penalty (float, default=0.): The L1 regularization penalty to use in the dense layer. This parameter should be a float greater than or equal to 0.

l2_penalty (float, default=0.): The L2 regularization penalty to use in the dense layer. This parameter should be a float greater than or equal to 0.

Return Value

This function does not return anything. It modifies the deeprai.models.FeedForward instance by adding a dense layer with the specified parameters.

Description

The add_dense function adds a dense layer to the deeprai.models.FeedForward instance. A dense layer is a layer of fully connected neurons where each neuron is connected to every neuron in the previous layer.

If this is the first layer added to the model, then add_dense will automatically treat it as an input layer and ignore any arguments other than neurons. This is because an input layer does not have an activation function, dropout, or regularization penalties.

If this is not the first layer added to the model, then add_dense will add a dense layer with the specified parameters to the model.

Examples

Here is an example of how to use the add_dense function:

from deeprai.models import FeedForward

model = FeedForward()
model.add_dense(784)
model.add_dense(128, activation='relu', dropout=0.2)
model.add_dense(64, activation='sigmoid', l2_penalty=0.001)

This code creates a FeedForward model with an input with 784 neurons, adds a dense layer with 128 neurons, a ReLU activation function, and a 20% dropout rate, and then adds another dense layer with 64 neurons, a sigmoid activation function, and an L2 regularization penalty of 0.001.

 

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