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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.
"""Example of DNNRegressor for Housing dataset."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from sklearn import cross_validation
from sklearn import metrics
from sklearn import preprocessing
import tensorflow as tf
def main(unused_argv):
# Load dataset
boston = tf.contrib.learn.datasets.load_dataset('boston')
x, y = boston.data, boston.target
# Split dataset into train / test
x_train, x_test, y_train, y_test = cross_validation.train_test_split(
x, y, test_size=0.2, random_state=42)
# Scale data (training set) to 0 mean and unit standard deviation.
scaler = preprocessing.StandardScaler()
x_train = scaler.fit_transform(x_train)
# Build 2 layer fully connected DNN with 10, 10 units respectively.
feature_columns = tf.contrib.learn.infer_real_valued_columns_from_input(
x_train)
regressor = tf.contrib.learn.DNNRegressor(
feature_columns=feature_columns, hidden_units=[10, 10])
# Fit
regressor.fit(x_train, y_train, steps=5000, batch_size=1)
# Transform
x_transformed = scaler.transform(x_test)
# Predict and score
y_predicted = list(regressor.predict(x_transformed, as_iterable=True))
score = metrics.mean_squared_error(y_predicted, y_test)
print('MSE: {0:f}'.format(score))
if __name__ == '__main__':
tf.app.run()
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