56 lines
1.9 KiB
Python
56 lines
1.9 KiB
Python
# Copyright 2016 The TensorFlow Authors. 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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"""Example of DNNClassifier for Iris plant dataset, h5 format."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from sklearn import cross_validation
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from sklearn import metrics
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from tensorflow.contrib import learn
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import h5py # pylint: disable=g-bad-import-order
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# Load dataset.
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iris = learn.datasets.load_dataset('iris')
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x_train, x_test, y_train, y_test = cross_validation.train_test_split(
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iris.data, iris.target, test_size=0.2, random_state=42)
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# Note that we are saving and load iris data as h5 format as a simple
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# demonstration here.
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h5f = h5py.File('test_hdf5.h5', 'w')
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h5f.create_dataset('X_train', data=x_train)
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h5f.create_dataset('X_test', data=x_test)
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h5f.create_dataset('y_train', data=y_train)
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h5f.create_dataset('y_test', data=y_test)
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h5f.close()
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h5f = h5py.File('test_hdf5.h5', 'r')
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x_train = h5f['X_train']
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x_test = h5f['X_test']
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y_train = h5f['y_train']
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y_test = h5f['y_test']
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# Build 3 layer DNN with 10, 20, 10 units respectively.
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feature_columns = learn.infer_real_valued_columns_from_input(x_train)
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classifier = learn.DNNClassifier(
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feature_columns=feature_columns, hidden_units=[10, 20, 10], n_classes=3)
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# Fit and predict.
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classifier.fit(x_train, y_train, steps=200)
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score = metrics.accuracy_score(y_test, classifier.predict(x_test))
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print('Accuracy: {0:f}'.format(score))
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