本文整理汇总了Python中tensorflow.contrib.learn.python.learn.estimators._sklearn.accuracy_score函数的典型用法代码示例。如果您正苦于以下问题:Python accuracy_score函数的具体用法?Python accuracy_score怎么用?Python accuracy_score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accuracy_score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: 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2: 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3: 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4: testIrisContinueTraining
def testIrisContinueTraining(self):
iris = datasets.load_iris()
classifier = learn.TensorFlowLinearClassifier(n_classes=3,
learning_rate=0.01, continue_training=True, steps=250)
classifier.fit(iris.data, iris.target)
score1 = accuracy_score(iris.target, classifier.predict(iris.data))
classifier.fit(iris.data, iris.target)
score2 = accuracy_score(iris.target, classifier.predict(iris.data))
self.assertGreater(score2, score1,
"Failed with score = {0}".format(score2))
示例5: testIrisContinueTraining
def testIrisContinueTraining(self):
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, steps=100)
score1 = accuracy_score(iris.target, classifier.predict(iris.data))
classifier.fit(iris.data, iris.target, steps=500)
score2 = accuracy_score(iris.target, classifier.predict(iris.data))
self.assertGreater(
score2, score1,
"Failed with score2 {0} <= score1 {1}".format(score2, score1))
示例6: 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, random_state=42)
val_monitor = learn.monitors.ValidationMonitor(
x_val,
y_val,
every_n_steps=50,
early_stopping_rounds=100,
early_stopping_metric="accuracy",
early_stopping_metric_minimize=False,
)
# 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))
# Full 1000 steps, 12 summaries and no evaluation summary.
# 12 summaries = global_step + first + every 100 out of 1000 steps.
self.assertEqual(12, len(_get_summary_events(classifier1.model_dir)))
with self.assertRaises(ValueError):
_get_summary_events(classifier1.model_dir + "/eval")
# classifier with early stopping - improved accuracy on testing set
classifier2 = learn.TensorFlowDNNClassifier(
hidden_units=[10, 20, 10],
n_classes=3,
steps=2000,
config=tf.contrib.learn.RunConfig(save_checkpoints_secs=1),
)
classifier2.fit(x_train, y_train, monitors=[val_monitor])
_ = accuracy_score(y_val, classifier2.predict(x_val))
_ = accuracy_score(y_test, classifier2.predict(x_test))
# Note, this test is unstable, so not checking for equality.
# See stability_test for examples of stability issues.
if val_monitor.early_stopped:
self.assertLess(val_monitor.best_step, 2000)
# Note, due to validation monitor stopping after the best score occur,
# the accuracy at current checkpoint is less.
# TODO(ipolosukhin): Time machine for restoring old checkpoints?
# flaky, still not always best_value better then score2 value.
# self.assertGreater(val_monitor.best_value, score2_val)
# Early stopped, unstable so checking only < then max.
self.assertLess(len(_get_summary_events(classifier2.model_dir)), 21)
# Eval typically has ~6 events, but it varies based on the run.
self.assertLess(len(_get_summary_events(classifier2.model_dir + "/eval")), 8)
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: testCustomMetrics
def testCustomMetrics(self):
"""Tests weight column in evaluation."""
def _input_fn_train():
# Create 4 rows, one of them (y = x), three of them (y=Not(x))
target = tf.constant([[1], [0], [0], [0]])
features = {'x': tf.ones(shape=[4, 1], dtype=tf.float32),}
return features, target
classifier = tf.contrib.learn.DNNLinearCombinedClassifier(
linear_feature_columns=[tf.contrib.layers.real_valued_column('x')],
dnn_feature_columns=[tf.contrib.layers.real_valued_column('x')],
dnn_hidden_units=[3, 3])
classifier.train(input_fn=_input_fn_train, steps=100)
scores = classifier.evaluate(
input_fn=_input_fn_train,
steps=100,
metrics={
'my_accuracy': tf.contrib.metrics.streaming_accuracy,
'my_precision': tf.contrib.metrics.streaming_precision
})
self.assertTrue(set(['loss', 'my_accuracy', 'my_precision']).issubset(set(
scores.keys())))
predictions = classifier.predict(input_fn=_input_fn_train)
self.assertEqual(_sklearn.accuracy_score([1, 0, 0, 0], predictions),
scores['my_accuracy'])
示例9: testIrisAllDictionaryInput
def testIrisAllDictionaryInput(self):
iris = base.load_iris()
est = estimator.Estimator(model_fn=logistic_model_no_mode_fn)
iris_data = {'input': iris.data}
iris_target = {'labels': iris.target}
est.fit(iris_data, iris_target, steps=100)
scores = est.evaluate(
x=iris_data,
y=iris_target,
metrics={
('accuracy', 'class'): metric_ops.streaming_accuracy
})
predictions = list(est.predict(x=iris_data))
predictions_class = list(est.predict(x=iris_data, outputs=['class']))
self.assertEqual(len(predictions), iris.target.shape[0])
classes_batch = np.array([p['class'] for p in predictions])
self.assertAllClose(classes_batch,
np.array([p['class'] for p in predictions_class]))
self.assertAllClose(classes_batch,
np.argmax(
np.array([p['prob'] for p in predictions]), axis=1))
other_score = _sklearn.accuracy_score(iris.target, classes_batch)
self.assertAllClose(other_score, scores['accuracy'])
self.assertTrue('global_step' in scores)
self.assertEqual(scores['global_step'], 100)
示例10: 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11: 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12: 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13: testDNNDropout0
def testDNNDropout0(self):
# Dropout prob == 0.
iris = tf.contrib.learn.datasets.load_iris()
classifier = tf.contrib.learn.TensorFlowDNNClassifier(
hidden_units=[10, 20, 10], n_classes=3, dropout=0.0)
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14: 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15: 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))