本文整理汇总了Python中tensorflow.contrib.learn.python.learn.datasets.load_iris函数的典型用法代码示例。如果您正苦于以下问题：Python load_iris函数的具体用法？Python load_iris怎么用？Python load_iris使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了load_iris函数的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: testIrisES
def testIrisES(self):
random.seed(42)
iris = datasets.load_iris()
x_train, x_test, y_train, y_test = train_test_split(iris.data,
iris.target,
test_size=0.2,
random_state=42)
x_train, x_val, y_train, y_val = train_test_split(
x_train, y_train, test_size=0.2)
val_monitor = learn.monitors.ValidationMonitor(x_val, y_val,
early_stopping_rounds=100)
# classifier without early stopping - overfitting
classifier1 = learn.TensorFlowDNNClassifier(hidden_units=[10, 20, 10],
n_classes=3,
steps=1000)
classifier1.fit(x_train, y_train)
accuracy_score(y_test, classifier1.predict(x_test))
# classifier with early stopping - improved accuracy on testing set
classifier2 = learn.TensorFlowDNNClassifier(hidden_units=[10, 20, 10],
n_classes=3,
steps=1000)
classifier2.fit(x_train, y_train, monitors=[val_monitor])
accuracy_score(y_test, classifier2.predict(x_test))示例2: testIrisMomentum
def testIrisMomentum(self):
random.seed(42)
iris = datasets.load_iris()
x_train, x_test, y_train, y_test = train_test_split(iris.data,
iris.target,
test_size=0.2,
random_state=42)
def custom_optimizer(learning_rate):
return tf.train.MomentumOptimizer(learning_rate, 0.9)
cont_features = [
tf.contrib.layers.real_valued_column("", dimension=4)]
classifier = learn.TensorFlowDNNClassifier(
feature_columns=cont_features,
hidden_units=[10, 20, 10],
n_classes=3,
steps=400,
learning_rate=0.01,
optimizer=custom_optimizer)
classifier.fit(x_train, y_train)
score = accuracy_score(y_test, classifier.predict(x_test))
self.assertGreater(score, 0.65, "Failed with score = {0}".format(score))示例3: testIris
def testIris(self):
path = tf.test.get_temp_dir() + '/tmp.saver'
random.seed(42)
iris = datasets.load_iris()
classifier = learn.TensorFlowLinearClassifier(n_classes=3)
classifier.fit(iris.data, iris.target)
classifier.save(path)示例4: testIrisStreaming
def testIrisStreaming(self):
iris = datasets.load_iris()
def iris_data():
while True:
for x in iris.data:
yield x
def iris_predict_data():
for x in iris.data:
yield x
def iris_target():
while True:
for y in iris.target:
yield y
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=500)
score1 = accuracy_score(iris.target, classifier.predict(iris.data))
score2 = accuracy_score(iris.target,
classifier.predict(iris_predict_data()))
self.assertGreater(score1, 0.5, "Failed with score = {0}".format(score1))
self.assertEqual(score2, score1, "Scores from {0} iterator doesn't "
"match score {1} from full "
"data.".format(score2, score1))示例5: testIrisClassWeight
def testIrisClassWeight(self):
iris = datasets.load_iris()
classifier = learn.TensorFlowLinearClassifier(
n_classes=3, class_weight=[0.1, 0.8, 0.1])
classifier.fit(iris.data, iris.target)
score = accuracy_score(iris.target, classifier.predict(iris.data))
self.assertLess(score, 0.7, "Failed with score = {0}".format(score))示例6: testIrisStreaming
def testIrisStreaming(self):
iris = datasets.load_iris()
def iris_data():
while True:
for x in iris.data:
yield x
def iris_predict_data():
for x in iris.data:
yield x
def iris_target():
while True:
for y in iris.target:
yield y
classifier = learn.TensorFlowLinearClassifier(n_classes=3, steps=100)
classifier.fit(iris_data(), iris_target())
score1 = accuracy_score(iris.target, classifier.predict(iris.data))
score2 = accuracy_score(iris.target,
classifier.predict(iris_predict_data()))
self.assertGreater(score1, 0.5, "Failed with score = {0}".format(score1))
self.assertEqual(score2, score1, "Scores from {0} iterator doesn't "
"match score {1} from full "
"data.".format(score2, score1))示例7: testDNNDropout0_1
def testDNNDropout0_1(self):
# Dropping only a little.
iris = datasets.load_iris()
classifier = learn.TensorFlowDNNClassifier(hidden_units=[10, 20, 10], n_classes=3, dropout=0.1)
classifier.fit(iris.data, iris.target)
score = accuracy_score(iris.target, classifier.predict(iris.data))
self.assertGreater(score, 0.9, "Failed with score = {0}".format(score))示例8: testIrisMomentum
def testIrisMomentum(self):
random.seed(42)
iris = datasets.load_iris()
x_train, x_test, y_train, y_test = train_test_split(iris.data,
iris.target,
test_size=0.2,
random_state=42)
# setup exponential decay function
def exp_decay(global_step):
return tf.train.exponential_decay(learning_rate=0.1,
global_step=global_step,
decay_steps=100,
decay_rate=0.001)
def custom_optimizer(learning_rate):
return tf.train.MomentumOptimizer(learning_rate, 0.9)
classifier = learn.TensorFlowDNNClassifier(hidden_units=[10, 20, 10],
n_classes=3,
steps=400,
learning_rate=exp_decay,
optimizer=custom_optimizer)
classifier.fit(x_train, y_train)
score = accuracy_score(y_test, classifier.predict(x_test))
self.assertGreater(score, 0.65, "Failed with score = {0}".format(score))示例9: testNoCheckpoints
def testNoCheckpoints(self):
path = tf.test.get_temp_dir() + '/tmp/tmp.saver4'
random.seed(42)
iris = datasets.load_iris()
classifier = learn.TensorFlowDNNClassifier(hidden_units=[10, 20, 10],
n_classes=3)
classifier.fit(iris.data, iris.target)
classifier.save(path)示例10: testIris
def testIris(self):
iris = datasets.load_iris()
classifier = learn.TensorFlowLinearClassifier(
feature_columns=learn.infer_real_valued_columns_from_input(iris.data),
n_classes=3)
classifier.fit(iris.data, [x for x in iris.target])
score = accuracy_score(iris.target, classifier.predict(iris.data))
self.assertGreater(score, 0.7, "Failed with score = {0}".format(score))示例11: testIrisSummaries
def testIrisSummaries(self):
iris = datasets.load_iris()
output_dir = tempfile.mkdtemp() + "learn_tests/"
classifier = learn.TensorFlowLinearClassifier(n_classes=3,
model_dir=output_dir)
classifier.fit(iris.data, iris.target)
score = accuracy_score(iris.target, classifier.predict(iris.data))
self.assertGreater(score, 0.5, "Failed with score = {0}".format(score))示例12: testIris
def testIris(self):
path = tf.test.get_temp_dir() + '/tmp.saver'
random.seed(42)
iris = datasets.load_iris()
cont_features = [
tf.contrib.layers.real_valued_column('', dimension=4)]
classifier = learn.LinearClassifier(
feature_columns=cont_features, n_classes=3, model_dir=path)
classifier.fit(iris.data, iris.target, steps=200)示例13: testNoCheckpoints
def testNoCheckpoints(self):
random.seed(42)
iris = datasets.load_iris()
cont_features = [
tf.contrib.layers.real_valued_column('', dimension=4)]
classifier = learn.DNNClassifier(feature_columns=cont_features,
hidden_units=[10, 20, 10],
n_classes=3)
classifier.fit(iris.data, iris.target, max_steps=100)示例14: testIris_proba
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))示例15: testIrisSummaries
def testIrisSummaries(self):
iris = datasets.load_iris()
output_dir = tempfile.mkdtemp() + "learn_tests/"
classifier = learn.LinearClassifier(
feature_columns=learn.infer_real_valued_columns_from_input(iris.data),
n_classes=3, model_dir=output_dir)
classifier.fit(iris.data, iris.target, max_steps=100)
score = accuracy_score(iris.target, classifier.predict(iris.data))
self.assertGreater(score, 0.5, "Failed with score = {0}".format(score))