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43 lines
1.5 KiB
Python
43 lines
1.5 KiB
Python
# Copyright 2015 Google Inc. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import random
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import skflow
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import tensorflow as tf
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from sklearn import datasets, metrics
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iris = datasets.load_iris()
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X_train, X_test, y_train, y_test = cross_validation.train_test_split(iris.data, iris.target,
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test_size=0.2, random_state=42)
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random.seed(42)
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def my_model(X, y):
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"""
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This is DNN with 10, 20, 10 hidden layers, and dropout of 0.5 probability.
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Note: If you want to run this example with multiple GPUs, Cuda Toolkit 7.0 and
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CUDNN 6.5 V2 from NVIDIA need to be installed beforehand.
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"""
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with tf.device('/gpu:1'):
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layers = skflow.ops.dnn(X, [10, 20, 10], keep_prob=0.5)
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with tf.device('/gpu:2'):
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return skflow.models.logistic_regression(layers, y)
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classifier = skflow.TensorFlowEstimator(model_fn=my_model, n_classes=3)
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classifier.fit(X_train, y_train)
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score = metrics.accuracy_score(classifier.predict(X_test), y_test)
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print('Accuracy: {0:f}'.format(score))
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