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dnn_regression.py
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dnn_regression.py
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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.
# ==============================================================================
"""Regression using the DNNRegressor Estimator."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import imports85 # pylint: disable=g-bad-import-order
STEPS = 5000
PRICE_NORM_FACTOR = 1000
def main(argv):
"""Builds, trains, and evaluates the model."""
assert len(argv) == 1
(train, test) = imports85.dataset()
# Switch the labels to units of thousands for better convergence.
def normalize_price(features, labels):
return features, labels / PRICE_NORM_FACTOR
train = train.map(normalize_price)
test = test.map(normalize_price)
# Build the training input_fn.
def input_train():
return (
# Shuffling with a buffer larger than the data set ensures
# that the examples are well mixed.
train.shuffle(1000).batch(128)
# Repeat forever
.repeat().make_one_shot_iterator().get_next())
# Build the validation input_fn.
def input_test():
return (test.shuffle(1000).batch(128)
.make_one_shot_iterator().get_next())
# The first way assigns a unique weight to each category. To do this you must
# specify the category's vocabulary (values outside this specification will
# receive a weight of zero). Here we specify the vocabulary using a list of
# options. The vocabulary can also be specified with a vocabulary file (using
# `categorical_column_with_vocabulary_file`). For features covering a
# range of positive integers use `categorical_column_with_identity`.
body_style_vocab = ["hardtop", "wagon", "sedan", "hatchback", "convertible"]
body_style = tf.feature_column.categorical_column_with_vocabulary_list(
key="body-style", vocabulary_list=body_style_vocab)
make = tf.feature_column.categorical_column_with_hash_bucket(
key="make", hash_bucket_size=50)
feature_columns = [
tf.feature_column.numeric_column(key="curb-weight"),
tf.feature_column.numeric_column(key="highway-mpg"),
# Since this is a DNN model, convert categorical columns from sparse
# to dense.
# Wrap them in an `indicator_column` to create a
# one-hot vector from the input.
tf.feature_column.indicator_column(body_style),
# Or use an `embedding_column` to create a trainable vector for each
# index.
tf.feature_column.embedding_column(make, dimension=3),
]
# Build a DNNRegressor, with 2x20-unit hidden layers, with the feature columns
# defined above as input.
model = tf.estimator.DNNRegressor(
hidden_units=[20, 20], feature_columns=feature_columns)
# Train the model.
model.train(input_fn=input_train, steps=STEPS)
# Evaluate how the model performs on data it has not yet seen.
eval_result = model.evaluate(input_fn=input_test)
# The evaluation returns a Python dictionary. The "average_loss" key holds the
# Mean Squared Error (MSE).
average_loss = eval_result["average_loss"]
# Convert MSE to Root Mean Square Error (RMSE).
print("\n" + 80 * "*")
print("\nRMS error for the test set: ${:.0f}"
.format(PRICE_NORM_FACTOR * average_loss**0.5))
print()
if __name__ == "__main__":
# The Estimator periodically generates "INFO" logs; make these logs visible.
tf.logging.set_verbosity(tf.logging.INFO)
tf.app.run(main=main)