Adagrad¶
- class mlpractice.gradient_descent.Adagrad(W0: numpy.ndarray, lambda_: float, eps: float = 1e-08, S0: float = 1, P: float = 0.5)¶
Adaptive gradient algorithm class.
- Parameters
- W0np.ndarray
Initialize weights.
- lambda_float
Learning rate parameter (step scale).
- epsfloat
Smoothing term.
- S0float
Learning rate parameter.
- Pfloat
Learning rate parameter.
- Attributes
- Wnp.ndarray
Weights.
Methods
calc_gradient
(X, Y)Calculating MSE gradient.
step
(X, Y, iteration)Descending step.
update_weights
(gradient, iteration)Changing weights with respect to gradient.
- calc_gradient(X: numpy.ndarray, Y: numpy.ndarray) numpy.ndarray ¶
Calculating MSE gradient.
- Parameters
- Xnp.ndarray
Features.
- Ynp.ndarray
Targets.
- Returns
- gradientnp.ndarray
Calculating gradient.
- update_weights(gradient: numpy.ndarray, iteration: int) numpy.ndarray ¶
Changing weights with respect to gradient.
- Parameters
- gradientnp.ndarray
Gradient of MSE.
- iterationint
Iteration number.
- Returns
- weigh_diffnp.ndarray
Weight difference.