def __init__(self,
epochs,
loss = 'mean_squared_error',
init_method = 'he_uniform',
optimizer = {},
penalty = 'ridge',
penalty_weight = 0.5,
l1_ratio = 0.5):
self.epochs = epochs
self.loss = objective(loss)
self.init_method = init(init_method)
self.optimizer = optimize(optimizer)
self.regularization = regularize(penalty, penalty_weight, l1_ratio = l1_ratio)
def __init__(self,
epochs,
loss='binary_crossentropy',
init_method='he_normal',
optimizer={},
penalty='lasso',
penalty_weight=0,
l1_ratio=0.5):
self.epochs = epochs
self.loss = objective(loss)
self.init_method = init(init_method)
self.optimizer = optimize(optimizer)
self.activate = activation('sigmoid')
self.regularization = regularize(penalty,
penalty_weight,
l1_ratio=l1_ratio)
test_size = 0.3)
opt = register_opt(optimizer_name = 'sgd', momentum = 0.01, learning_rate = 0.001)
model = PolynomialRegression(degree = 5,
epochs = 100,
optimizer = opt,
penalty = 'elastic',
penalty_weight = 0.5,
l1_ratio = 0.3)
fit_stats = model.fit(train_data, train_label)
targets = np.expand_dims(test_label, axis = 1)
predictions = np.expand_dims(model.predict(test_data), axis = 1)
mse = objective('mean_squared_error').forward(predictions, targets)
print('Mean Squared Error: {:.2f}'.format(mse))
plot_metric('Accuracy vs Loss',
len(fit_stats['train_loss']),
fit_stats['train_acc'],
fit_stats['train_loss'],
legend = ['acc', 'loss'])
plot_regression_results(train_data, train_label,
test_data,
test_label,
input_data,
model.predict(input_data),
mse,
'Polynomial Regression',