def testCustomModel(self):
path = tf.test.get_temp_dir() + '/tmp.saver2'
random.seed(42)
iris = datasets.load_iris()
def _custom_model(x, y):
return learn.models.logistic_regression(x, y)
classifier = learn.TensorFlowEstimator(model_fn=_custom_model, n_classes=3)
classifier.fit(iris.data, iris.target)
classifier.save(path)
def testCustomModel(self):
path = tf.test.get_temp_dir() + '/tmp.saver2'
random.seed(42)
iris = datasets.load_iris()
def custom_model(X, y):
return learn.models.logistic_regression(X, y)
classifier = learn.TensorFlowEstimator(model_fn=custom_model,
n_classes=3)
classifier.fit(iris.data, iris.target)
classifier.save(path)
new_classifier = learn.TensorFlowEstimator.restore(path)
self.assertEqual(type(new_classifier), type(classifier))
score = accuracy_score(iris.target, new_classifier.predict(iris.data))
self.assertGreater(score, 0.5, "Failed with score = {0}".format(score))
def testUnfitted(self):
estimator = learn.TensorFlowEstimator(model_fn=None, n_classes=1)
with self.assertRaises(base.NotFittedError):
estimator.predict([1, 2, 3])
with self.assertRaises(base.NotFittedError):
estimator.save("/tmp/path")
# 3 layer neural network with rectified linear activation.
if reset_seed:
random.seed(42)
classifier = skflow.TensorFlowDNNClassifier(hidden_units=[10, 20, 10],
n_classes=2,
batch_size=128,
steps=500,
learning_rate=learning_rate)
classifier.fit(X_train, y_train)
print(accuracy_score(classifier.predict(X_test), y_test))
# 3 layer neural network with hyperbolic tangent activation.
def dnn_tanh(X, y):
layers = skflow.ops.dnn(X, [20, 20, 20], tf.tanh)
return skflow.models.logistic_regression(layers, y)
if reset_seed:
random.seed(42)
classifier = skflow.TensorFlowEstimator(model_fn=dnn_tanh,
n_classes=2,
batch_size=128,
steps=500,
learning_rate=learning_rate)
classifier.fit(X_train, y_train)
print(accuracy_score(classifier.predict(X_test), y_test))
