Dense

class Dense(output_size: int, activation: Activation, weight_initializer: bluebird.weight_initializers.WeightInitializer = <bluebird.weight_initializers.HeWeightInitializer object>, bias_initializer: bluebird.weight_initializers.WeightInitializer = <bluebird.weight_initializers.ZerosWeightInitializer object>)

Bases: bluebird.layers.layer.Layer

Combination of linear and activation layer.

It takes activation later, and output dimension for an argument, so that you don’t have to specify them seperably.

Also ihnerits the base Layer class.

Example:

dense = Dense(50, activation=Relu())
net = NeuralNet([
        ...
        dense,
        ...
    ])

__init__(output_size: int, activation: Activation, weight_initializer: bluebird.weight_initializers.WeightInitializer = <bluebird.weight_initializers.HeWeightInitializer object>, bias_initializer: bluebird.weight_initializers.WeightInitializer = <bluebird.weight_initializers.ZerosWeightInitializer object>) → None

Initializes the object.

Parameters

• output_size (int) – dimension of the output

• activation (Activation) – activation function

• weight_initializer (WeightInitializer, optional) – defines how weights are initialized, defaults to HeWeightInitializer

• bias_initializer (WeightInitializer, optional) – defines how weights are initialized, defaults to ZerosWeightInitializer

Methods

__init__(output_size, activation[, …])	Initializes the object.
backward(grad)	Used to calculate the gradients of weights and biases.
build(input_size)	Called by the model, before its training step.
forward(inputs[, training])	Called each time the data passes throughout the nework.
get_params()	Returns params of layer.

backward(grad: numpy.ndarray) → numpy.ndarray

Used to calculate the gradients of weights and biases.

Parameters

grad (Tensor) – gradient from previous layer or loss function.

Returns

Gradient

Return type

Tensor

build(input_size: int) → None

Called by the model, before its training step.

It sets the input size and initializes the weights.

Parameters

input_size (int) – output size from previous layer

forward(inputs: numpy.ndarray, training: bool = False) → numpy.ndarray

Called each time the data passes throughout the nework.

Parameters

• inputs (Tensor) – output from the previous layer

• training (bool, optional) – set to true during training, and is false when network predicts

Returns

processed input data

Return type

Tensor

get_params() → Dict

Returns params of layer.

Returns

all params of layer

Return type

dict