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Diffstat (limited to 'tensorflow/contrib/learn/python/learn/tests/nonlinear_test.py')
| -rw-r--r-- | tensorflow/contrib/learn/python/learn/tests/nonlinear_test.py | 195 |
1 files changed, 195 insertions, 0 deletions
diff --git a/tensorflow/contrib/learn/python/learn/tests/nonlinear_test.py b/tensorflow/contrib/learn/python/learn/tests/nonlinear_test.py new file mode 100644 index 0000000000..47ee09db80 --- /dev/null +++ b/tensorflow/contrib/learn/python/learn/tests/nonlinear_test.py @@ -0,0 +1,195 @@ +# pylint: disable=g-bad-file-header +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Non-linear estimator tests.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import random + +import numpy as np +import tensorflow as tf +from tensorflow.contrib.learn.python.learn.estimators._sklearn import accuracy_score +from tensorflow.contrib.learn.python.learn.estimators._sklearn import mean_squared_error + + +class NonLinearTest(tf.test.TestCase): + """Non-linear estimator tests.""" + + def testIrisDNN(self): + random.seed(42) + iris = tf.contrib.learn.datasets.load_iris() + classifier = tf.contrib.learn.TensorFlowDNNClassifier( + hidden_units=[10, 20, 10], n_classes=3) + 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)) + weights = classifier.weights_ + self.assertEqual(weights[0].shape, (4, 10)) + self.assertEqual(weights[1].shape, (10, 20)) + self.assertEqual(weights[2].shape, (20, 10)) + self.assertEqual(weights[3].shape, (10, 3)) + biases = classifier.bias_ + self.assertEqual(len(biases), 4) + + def testBostonDNN(self): + random.seed(42) + boston = tf.contrib.learn.datasets.load_boston() + regressor = tf.contrib.learn.TensorFlowDNNRegressor( + hidden_units=[10, 20, 10], + n_classes=0, + batch_size=boston.data.shape[0], + steps=300, + learning_rate=0.001) + regressor.fit(boston.data, boston.target) + score = mean_squared_error(boston.target, regressor.predict(boston.data)) + self.assertLess(score, 110, "Failed with score = {0}".format(score)) + weights = regressor.weights_ + self.assertEqual(weights[0].shape, (13, 10)) + self.assertEqual(weights[1].shape, (10, 20)) + self.assertEqual(weights[2].shape, (20, 10)) + self.assertEqual(weights[3].shape, (10, 1)) + biases = regressor.bias_ + self.assertEqual(len(biases), 4) + + 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)) + + def testDNNDropout0_1(self): + # Dropping only a little. + iris = tf.contrib.learn.datasets.load_iris() + classifier = tf.contrib.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)) + # If the quality is lower - dropout is not working. + self.assertGreater(score, 0.9, "Failed with score = {0}".format(score)) + + def testDNNDropout0_9(self): + # Dropping out most of it. + iris = tf.contrib.learn.datasets.load_iris() + classifier = tf.contrib.learn.TensorFlowDNNClassifier( + hidden_units=[10, 20, 10], + n_classes=3, dropout=0.9) + classifier.fit(iris.data, iris.target) + score = accuracy_score(iris.target, classifier.predict(iris.data)) + self.assertGreater(score, 0.3, "Failed with score = {0}".format(score)) + self.assertLess(score, 0.4, "Failed with score = {0}".format(score)) + + def testRNN(self): + random.seed(42) + data = np.array( + list([[2, 1, 2, 2, 3], [2, 2, 3, 4, 5], [3, 3, 1, 2, 1], [2, 4, 5, 4, 1] + ]), + dtype=np.float32) + # labels for classification + labels = np.array(list([1, 0, 1, 0]), dtype=np.float32) + # targets for regression + targets = np.array(list([10, 16, 10, 16]), dtype=np.float32) + test_data = np.array(list([[1, 3, 3, 2, 1], [2, 3, 4, 5, 6]]), + dtype=np.float32) + + def _input_fn(X): + # pylint: disable=invalid-name + return tf.split(1, 5, X) + + # Classification + classifier = tf.contrib.learn.TensorFlowRNNClassifier(rnn_size=2, + cell_type="lstm", + n_classes=2, + input_op_fn=_input_fn) + classifier.fit(data, labels) + # pylint: disable=pointless-statement + classifier.weights_ + classifier.bias_ + # pylint: enable=pointless-statement + predictions = classifier.predict(test_data) + self.assertAllClose(predictions, np.array([1, 0])) + + classifier = tf.contrib.learn.TensorFlowRNNClassifier(rnn_size=2, + cell_type="rnn", + n_classes=2, + input_op_fn=_input_fn, + num_layers=2) + classifier.fit(data, labels) + classifier = tf.contrib.learn.TensorFlowRNNClassifier( + rnn_size=2, + cell_type="invalid_cell_type", + n_classes=2, + input_op_fn=_input_fn, + num_layers=2) + with self.assertRaises(ValueError): + classifier.fit(data, labels) + + # Regression + regressor = tf.contrib.learn.TensorFlowRNNRegressor(rnn_size=2, + cell_type="gru", + input_op_fn=_input_fn) + regressor.fit(data, targets) + # pylint: disable=pointless-statement + regressor.weights_ + regressor.bias_ + # pylint: enable=pointless-statement + predictions = regressor.predict(test_data) + + def testBidirectionalRNN(self): + random.seed(42) + data = np.array( + list([[2, 1, 2, 2, 3], [2, 2, 3, 4, 5], [3, 3, 1, 2, 1], [2, 4, 5, 4, 1] + ]), + dtype=np.float32) + labels = np.array(list([1, 0, 1, 0]), dtype=np.float32) + + def _input_fn(X): + # pylint: disable=invalid-name + return tf.split(1, 5, X) + + # Classification + classifier = tf.contrib.learn.TensorFlowRNNClassifier(rnn_size=2, + cell_type="lstm", + n_classes=2, + input_op_fn=_input_fn, + bidirectional=True) + classifier.fit(data, labels) + test_data = np.array(list([[1, 3, 3, 2, 1], [2, 3, 4, + 5, 6]]), dtype=np.float32) + predictions = classifier.predict(test_data) + self.assertAllClose(predictions, np.array([1, 0])) + + # TODO(ipolosukhin): Restore or remove this. + # def testDNNAutoencoder(self): + # import numpy as np + # iris = datasets.load_iris() + # autoencoder = learn.TensorFlowDNNAutoencoder(hidden_units=[10, 20]) + # transformed = autoencoder.fit_transform(iris.data[1:2]) + # expected = np.array([[ + # -3.57627869e-07, 1.17000043e+00, 1.01902664e+00, 1.19209290e-07, + # 0.00000000e+00, 1.19209290e-07, -5.96046448e-08, -2.38418579e-07, + # 9.74681854e-01, 1.19209290e-07]]) + # self.assertAllClose(transformed, expected) + + +if __name__ == "__main__": + tf.test.main() |