def _test_adaboost():
metric = metrics.get("categorical_accuracy")
x_train, y_train = get_test_classification_data()
Models = []
weak_model_losses = []
for _ in range(num_models):
model = Sequential()
model.add(Input(input_shape=(x_train.shape[1], )))
model.add(Dense(10, activation="relu"))
model.add(Dense(y_train.shape[1], activation="softmax"))
model.compile(loss="categorical_crossentropy",
optimizer="adam",
metrics=[metric])
model.fit(x_train, y_train, epochs=3, batch_size=16, verbose=-1)
weak_model_losses.append(metric.loss(model.predict(x_train), y_train))
Models.append(model)
boosting = AdaBoost(Models)
boosting.fit(x_train, y_train, verbose=-1)
y_boosting_pred = boosting.predict(x_train)
boosting_loss = metric.loss(y_boosting_pred, y_train)
assert boosting_loss <= min(weak_model_losses)
def test_l2boosting():
metric = metrics.get("mse")
x_train, y_train = get_test_regression_data()
Models = []
weak_model_losses = []
for _ in range(num_models):
model = Sequential()
model.add(Input(input_shape=(x_train.shape[1], )))
model.add(Dense(10, activation="relu"))
model.add(Dense(y_train.shape[1], activation="linear"))
model.compile(loss="mean_squared_error",
optimizer="adam",
metrics=[metric])
model.fit(x_train, y_train, epochs=3, batch_size=16, verbose=-1)
weak_model_losses.append(metric.loss(model.predict(x_train), y_train))
Models.append(model)
boosting = L2Boosting(Models)
boosting.fit(x_train, y_train, verbose=-1)
y_boosting_pred = boosting.predict(x_train)
boosting_loss = metric.loss(y_boosting_pred, y_train)
assert boosting_loss <= min(weak_model_losses)
# coding: utf-8
from kerasy.models import Sequential
from kerasy.layers import Input, Dense
from kerasy import regularizers
from kerasy import metrics
from kerasy.utils import generate_test_data
from kerasy.utils import CategoricalEncoder
num_classes = 2
metric = metrics.get("categorical_accuracy")
def get_test_data():
(x_train, y_train), _ = generate_test_data(num_train=1000,
num_test=200,
input_shape=(10, ),
classification=True,
num_classes=num_classes,
random_state=123)
encoder = CategoricalEncoder()
y_train = encoder.to_onehot(y_train, num_classes)
return x_train, y_train
def _test_regularizer(regularizer, target=0.75):
regularizer = regularizers.get(regularizer)
x_train, y_train = get_test_data()
model = Sequential()
model.add(Input(input_shape=(x_train.shape[1], )))
model.add(Dense(10, activation="relu", kernel_regularizer=regularizer))
def _test_metrics(identifier):
metric = metrics.get(identifier)
metrics_check(metric, y_shape_)
metrics_check(metric, y_shape_1)
metrics_check(metric, y_shape_10)