Delete outdated examples.
PiperOrigin-RevId: 265760118
This commit is contained in:
Mark Daoust
TensorFlower Gardener
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commit
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# Description:
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# Examples of tf.learn usage
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package(
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default_visibility = ["//visibility:public"],
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licenses = ["notice"], # Apache 2.0
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)
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exports_files(["LICENSE"])
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py_binary(
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name = "iris_custom_decay_dnn",
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srcs = ["iris_custom_decay_dnn.py"],
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python_version = "PY2",
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srcs_version = "PY2AND3",
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deps = ["//tensorflow:tensorflow_py"],
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)
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py_binary(
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name = "iris_custom_model",
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srcs = ["iris_custom_model.py"],
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python_version = "PY2",
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srcs_version = "PY2AND3",
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deps = ["//tensorflow:tensorflow_py"],
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)
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sh_test(
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name = "examples_test",
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size = "large",
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srcs = ["examples_test.sh"],
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data = [
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":iris_custom_decay_dnn",
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":iris_custom_model",
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],
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tags = [
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"manual",
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"notap",
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],
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)
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# Estimator Examples
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TensorFlow Estimators are a high-level API for TensorFlow that allows you to
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create, train, and use deep learning models easily.
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See the [Quickstart tutorial](https://www.tensorflow.org/get_started/estimator)
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for an introduction to the API.
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## Basics
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* [Building a Custom Model](https://www.tensorflow.org/code/tensorflow/examples/learn/iris_custom_model.py)
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## Techniques
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* [Deep Neural Network with Customized Decay Function](https://www.tensorflow.org/code/tensorflow/examples/learn/iris_custom_decay_dnn.py)
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#!/bin/bash
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# 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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# This script exercises the examples of using TF.Learn.
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DIR="$TEST_SRCDIR"
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# Check if TEST_WORKSPACE is defined, and set as empty string if not.
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if [ -z "${TEST_WORKSPACE-}" ]
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then
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TEST_WORKSPACE=""
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fi
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if [ ! -z "$TEST_WORKSPACE" ]
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then
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DIR="$DIR"/"$TEST_WORKSPACE"
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fi
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TFLEARN_EXAMPLE_BASE_DIR=$DIR/tensorflow/examples/learn
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function test() {
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echo "Test $1:"
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$TFLEARN_EXAMPLE_BASE_DIR/$1 $2
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if [ $? -eq 0 ]
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then
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echo "Test passed."
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return 0
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else
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echo "Test failed."
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exit 1
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fi
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}
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test iris_custom_decay_dnn
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test iris_custom_model
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# 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, with exponential decay."""
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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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import numpy as np
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from sklearn import datasets
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from sklearn import metrics
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from sklearn import model_selection
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import tensorflow as tf
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X_FEATURE = 'x' # Name of the input feature.
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def my_model(features, labels, mode):
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"""DNN with three hidden layers."""
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# Create three fully connected layers respectively of size 10, 20, and 10.
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net = features[X_FEATURE]
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for units in [10, 20, 10]:
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net = tf.layers.dense(net, units=units, activation=tf.nn.relu)
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# Compute logits (1 per class).
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logits = tf.layers.dense(net, 3, activation=None)
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# Compute predictions.
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predicted_classes = tf.argmax(logits, 1)
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if mode == tf.estimator.ModeKeys.PREDICT:
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predictions = {
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'class': predicted_classes,
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'prob': tf.nn.softmax(logits)
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}
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return tf.estimator.EstimatorSpec(mode, predictions=predictions)
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# Compute loss.
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loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
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# Create training op with exponentially decaying learning rate.
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if mode == tf.estimator.ModeKeys.TRAIN:
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global_step = tf.train.get_global_step()
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learning_rate = tf.train.exponential_decay(
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learning_rate=0.1, global_step=global_step,
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decay_steps=100, decay_rate=0.001)
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optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate)
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train_op = optimizer.minimize(loss, global_step=global_step)
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return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
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# Compute evaluation metrics.
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eval_metric_ops = {
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'accuracy': tf.metrics.accuracy(
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labels=labels, predictions=predicted_classes)
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}
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return tf.estimator.EstimatorSpec(
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mode, loss=loss, eval_metric_ops=eval_metric_ops)
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def main(unused_argv):
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iris = datasets.load_iris()
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x_train, x_test, y_train, y_test = model_selection.train_test_split(
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iris.data, iris.target, test_size=0.2, random_state=42)
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classifier = tf.estimator.Estimator(model_fn=my_model)
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# Train.
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train_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(
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x={X_FEATURE: x_train}, y=y_train, num_epochs=None, shuffle=True)
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classifier.train(input_fn=train_input_fn, steps=1000)
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# Predict.
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test_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(
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x={X_FEATURE: x_test}, y=y_test, num_epochs=1, shuffle=False)
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predictions = classifier.predict(input_fn=test_input_fn)
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y_predicted = np.array(list(p['class'] for p in predictions))
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y_predicted = y_predicted.reshape(np.array(y_test).shape)
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# Score with sklearn.
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score = metrics.accuracy_score(y_test, y_predicted)
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print('Accuracy (sklearn): {0:f}'.format(score))
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# Score with tensorflow.
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scores = classifier.evaluate(input_fn=test_input_fn)
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print('Accuracy (tensorflow): {0:f}'.format(scores['accuracy']))
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if __name__ == '__main__':
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tf.app.run()
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@ -1,97 +0,0 @@
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# 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 Estimator for Iris plant dataset."""
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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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import numpy as np
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from sklearn import datasets
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from sklearn import metrics
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from sklearn import model_selection
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import tensorflow as tf
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X_FEATURE = 'x' # Name of the input feature.
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def my_model(features, labels, mode):
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"""DNN with three hidden layers, and dropout of 0.1 probability."""
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# Create three fully connected layers respectively of size 10, 20, and 10 with
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# each layer having a dropout probability of 0.1.
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net = features[X_FEATURE]
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for units in [10, 20, 10]:
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net = tf.layers.dense(net, units=units, activation=tf.nn.relu)
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net = tf.layers.dropout(net, rate=0.1)
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# Compute logits (1 per class).
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logits = tf.layers.dense(net, 3, activation=None)
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# Compute predictions.
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predicted_classes = tf.argmax(logits, 1)
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if mode == tf.estimator.ModeKeys.PREDICT:
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predictions = {
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'class': predicted_classes,
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'prob': tf.nn.softmax(logits)
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}
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return tf.estimator.EstimatorSpec(mode, predictions=predictions)
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# Compute loss.
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loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
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# Create training op.
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if mode == tf.estimator.ModeKeys.TRAIN:
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optimizer = tf.train.AdagradOptimizer(learning_rate=0.1)
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train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())
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return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
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# Compute evaluation metrics.
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eval_metric_ops = {
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'accuracy': tf.metrics.accuracy(
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labels=labels, predictions=predicted_classes)
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}
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return tf.estimator.EstimatorSpec(
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mode, loss=loss, eval_metric_ops=eval_metric_ops)
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def main(unused_argv):
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iris = datasets.load_iris()
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x_train, x_test, y_train, y_test = model_selection.train_test_split(
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iris.data, iris.target, test_size=0.2, random_state=42)
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classifier = tf.estimator.Estimator(model_fn=my_model)
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# Train.
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train_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(
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x={X_FEATURE: x_train}, y=y_train, num_epochs=None, shuffle=True)
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classifier.train(input_fn=train_input_fn, steps=1000)
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# Predict.
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test_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(
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x={X_FEATURE: x_test}, y=y_test, num_epochs=1, shuffle=False)
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predictions = classifier.predict(input_fn=test_input_fn)
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y_predicted = np.array(list(p['class'] for p in predictions))
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y_predicted = y_predicted.reshape(np.array(y_test).shape)
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# Score with sklearn.
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score = metrics.accuracy_score(y_test, y_predicted)
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print('Accuracy (sklearn): {0:f}'.format(score))
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# Score with tensorflow.
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scores = classifier.evaluate(input_fn=test_input_fn)
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print('Accuracy (tensorflow): {0:f}'.format(scores['accuracy']))
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if __name__ == '__main__':
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tf.app.run()
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