def testIris_proba(self):
# If sklearn available.
if log_loss:
random.seed(42)
iris = datasets.load_iris()
classifier = learn.TensorFlowClassifier(n_classes=3, steps=250)
classifier.fit(iris.data, iris.target)
score = log_loss(iris.target, classifier.predict_proba(iris.data))
self.assertLess(score, 0.8, "Failed with score = {0}".format(score))
def testIris_proba(self):
# If sklearn available.
if log_loss:
random.seed(42)
iris = datasets.load_iris()
classifier = learn.LinearClassifier(
feature_columns=learn.infer_real_valued_columns_from_input(iris.data),
n_classes=3)
classifier.fit(iris.data, iris.target, max_steps=250)
score = log_loss(iris.target, classifier.predict_proba(iris.data))
self.assertLess(score, 0.8, "Failed with score = {0}".format(score))
def testIris_proba(self):
# If sklearn available.
if log_loss:
random.seed(42)
iris = datasets.load_iris()
classifier = learn.LinearClassifier(
feature_columns=learn.infer_real_valued_columns_from_input(iris.data),
n_classes=3)
classifier.fit(iris.data, iris.target, max_steps=250)
score = log_loss(iris.target, list(classifier.predict_proba(iris.data)))
self.assertLess(score, 0.8, "Failed with score = {0}".format(score))
