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Mark Daoust 2019-08-27 13:58:27 -07:00 committed by TensorFlower Gardener
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tensorflow/examples/learn/BUILD
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# Description:
# Examples of tf.learn usage
package(
default_visibility = ["//visibility:public"],
licenses = ["notice"], # Apache 2.0
)
exports_files(["LICENSE"])
py_binary(
name = "iris_custom_decay_dnn",
srcs = ["iris_custom_decay_dnn.py"],
python_version = "PY2",
srcs_version = "PY2AND3",
deps = ["//tensorflow:tensorflow_py"],
)
py_binary(
name = "iris_custom_model",
srcs = ["iris_custom_model.py"],
python_version = "PY2",
srcs_version = "PY2AND3",
deps = ["//tensorflow:tensorflow_py"],
)
sh_test(
name = "examples_test",
size = "large",
srcs = ["examples_test.sh"],
data = [
":iris_custom_decay_dnn",
":iris_custom_model",
],
tags = [
"manual",
"notap",
],
)

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tensorflow/examples/learn/README.md
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# Estimator Examples
TensorFlow Estimators are a high-level API for TensorFlow that allows you to
create, train, and use deep learning models easily.
See the [Quickstart tutorial](https://www.tensorflow.org/get_started/estimator)
for an introduction to the API.
## Basics
* [Building a Custom Model](https://www.tensorflow.org/code/tensorflow/examples/learn/iris_custom_model.py)
## Techniques
* [Deep Neural Network with Customized Decay Function](https://www.tensorflow.org/code/tensorflow/examples/learn/iris_custom_decay_dnn.py)

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tensorflow/examples/learn/examples_test.sh
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#!/bin/bash
# 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.
# This script exercises the examples of using TF.Learn.
DIR="$TEST_SRCDIR"
# Check if TEST_WORKSPACE is defined, and set as empty string if not.
if [ -z "${TEST_WORKSPACE-}" ]
then
TEST_WORKSPACE=""
fi
if [ ! -z "$TEST_WORKSPACE" ]
then
DIR="$DIR"/"$TEST_WORKSPACE"
fi
TFLEARN_EXAMPLE_BASE_DIR=$DIR/tensorflow/examples/learn
function test() {
echo "Test $1:"
$TFLEARN_EXAMPLE_BASE_DIR/$1 $2
if [ $? -eq 0 ]
then
echo "Test passed."
return 0
else
echo "Test failed."
exit 1
fi
}
test iris_custom_decay_dnn
test iris_custom_model

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tensorflow/examples/learn/iris_custom_decay_dnn.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.
"""Example of DNNClassifier for Iris plant dataset, with exponential decay."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from sklearn import datasets
from sklearn import metrics
from sklearn import model_selection
import tensorflow as tf
X_FEATURE = 'x' # Name of the input feature.
def my_model(features, labels, mode):
"""DNN with three hidden layers."""
# Create three fully connected layers respectively of size 10, 20, and 10.
net = features[X_FEATURE]
for units in [10, 20, 10]:
net = tf.layers.dense(net, units=units, activation=tf.nn.relu)
# Compute logits (1 per class).
logits = tf.layers.dense(net, 3, activation=None)
# Compute predictions.
predicted_classes = tf.argmax(logits, 1)
if mode == tf.estimator.ModeKeys.PREDICT:
predictions = {
'class': predicted_classes,
'prob': tf.nn.softmax(logits)
}
return tf.estimator.EstimatorSpec(mode, predictions=predictions)
# Compute loss.
loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
# Create training op with exponentially decaying learning rate.
if mode == tf.estimator.ModeKeys.TRAIN:
global_step = tf.train.get_global_step()
learning_rate = tf.train.exponential_decay(
learning_rate=0.1, global_step=global_step,
decay_steps=100, decay_rate=0.001)
optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate)
train_op = optimizer.minimize(loss, global_step=global_step)
return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
# Compute evaluation metrics.
eval_metric_ops = {
'accuracy': tf.metrics.accuracy(
labels=labels, predictions=predicted_classes)
}
return tf.estimator.EstimatorSpec(
mode, loss=loss, eval_metric_ops=eval_metric_ops)
def main(unused_argv):
iris = datasets.load_iris()
x_train, x_test, y_train, y_test = model_selection.train_test_split(
iris.data, iris.target, test_size=0.2, random_state=42)
classifier = tf.estimator.Estimator(model_fn=my_model)
# Train.
train_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(
x={X_FEATURE: x_train}, y=y_train, num_epochs=None, shuffle=True)
classifier.train(input_fn=train_input_fn, steps=1000)
# Predict.
test_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(
x={X_FEATURE: x_test}, y=y_test, num_epochs=1, shuffle=False)
predictions = classifier.predict(input_fn=test_input_fn)
y_predicted = np.array(list(p['class'] for p in predictions))
y_predicted = y_predicted.reshape(np.array(y_test).shape)
# Score with sklearn.
score = metrics.accuracy_score(y_test, y_predicted)
print('Accuracy (sklearn): {0:f}'.format(score))
# Score with tensorflow.
scores = classifier.evaluate(input_fn=test_input_fn)
print('Accuracy (tensorflow): {0:f}'.format(scores['accuracy']))
if __name__ == '__main__':
tf.app.run()

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tensorflow/examples/learn/iris_custom_model.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.
"""Example of Estimator for Iris plant dataset."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from sklearn import datasets
from sklearn import metrics
from sklearn import model_selection
import tensorflow as tf
X_FEATURE = 'x' # Name of the input feature.
def my_model(features, labels, mode):
"""DNN with three hidden layers, and dropout of 0.1 probability."""
# Create three fully connected layers respectively of size 10, 20, and 10 with
# each layer having a dropout probability of 0.1.
net = features[X_FEATURE]
for units in [10, 20, 10]:
net = tf.layers.dense(net, units=units, activation=tf.nn.relu)
net = tf.layers.dropout(net, rate=0.1)
# Compute logits (1 per class).
logits = tf.layers.dense(net, 3, activation=None)
# Compute predictions.
predicted_classes = tf.argmax(logits, 1)
if mode == tf.estimator.ModeKeys.PREDICT:
predictions = {
'class': predicted_classes,
'prob': tf.nn.softmax(logits)
}
return tf.estimator.EstimatorSpec(mode, predictions=predictions)
# Compute loss.
loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
# Create training op.
if mode == tf.estimator.ModeKeys.TRAIN:
optimizer = tf.train.AdagradOptimizer(learning_rate=0.1)
train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())
return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
# Compute evaluation metrics.
eval_metric_ops = {
'accuracy': tf.metrics.accuracy(
labels=labels, predictions=predicted_classes)
}
return tf.estimator.EstimatorSpec(
mode, loss=loss, eval_metric_ops=eval_metric_ops)
def main(unused_argv):
iris = datasets.load_iris()
x_train, x_test, y_train, y_test = model_selection.train_test_split(
iris.data, iris.target, test_size=0.2, random_state=42)
classifier = tf.estimator.Estimator(model_fn=my_model)
# Train.
train_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(
x={X_FEATURE: x_train}, y=y_train, num_epochs=None, shuffle=True)
classifier.train(input_fn=train_input_fn, steps=1000)
# Predict.
test_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(
x={X_FEATURE: x_test}, y=y_test, num_epochs=1, shuffle=False)
predictions = classifier.predict(input_fn=test_input_fn)
y_predicted = np.array(list(p['class'] for p in predictions))
y_predicted = y_predicted.reshape(np.array(y_test).shape)
# Score with sklearn.
score = metrics.accuracy_score(y_test, y_predicted)
print('Accuracy (sklearn): {0:f}'.format(score))
# Score with tensorflow.
scores = classifier.evaluate(input_fn=test_input_fn)
print('Accuracy (tensorflow): {0:f}'.format(scores['accuracy']))
if __name__ == '__main__':
tf.app.run()