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# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from sklearn import metrics, cross_validation
from tensorflow.contrib import learn
import h5py
# Load dataset.
iris = learn.datasets.load_dataset('iris')
X_train, X_test, y_train, y_test = cross_validation.train_test_split(iris.data, iris.target,
test_size=0.2, random_state=42)
# Note that we are saving and load iris data as h5 format as a simple demonstration here.
h5f = h5py.File('test_hdf5.h5', 'w')
h5f.create_dataset('X_train', data=X_train)
h5f.create_dataset('X_test', data=X_test)
h5f.create_dataset('y_train', data=y_train)
h5f.create_dataset('y_test', data=y_test)
h5f.close()
h5f = h5py.File('test_hdf5.h5', 'r')
X_train = h5f['X_train']
X_test = h5f['X_test']
y_train = h5f['y_train']
y_test = h5f['y_test']
# Build 3 layer DNN with 10, 20, 10 units respectively.
classifier = learn.TensorFlowDNNClassifier(hidden_units=[10, 20, 10],
n_classes=3, steps=200)
# Fit and predict.
classifier.fit(X_train, y_train)
score = metrics.accuracy_score(y_test, classifier.predict(X_test))
print('Accuracy: {0:f}'.format(score))
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