accuracy = tf.math.mean(tf.dtypes.cast(tf.math.equal(predicted, expected), TFloat32.class), tf.array(0));
+
+ // Run the graph
+ try (Session session = new Session(graph)) {
+
+ // Train the model
+ for (ImageBatch trainingBatch : dataset.trainingBatches(TRAINING_BATCH_SIZE)) {
+ try (TFloat32 batchImages = preprocessImages(trainingBatch.images());
+ TFloat32 batchLabels = preprocessLabels(trainingBatch.labels())) {
+ session.runner()
+ .addTarget(minimize)
+ .feed(images.asOutput(), batchImages)
+ .feed(labels.asOutput(), batchLabels)
+ .run();
+ }
+ }
+
+ // Test the model
+ ImageBatch testBatch = dataset.testBatch();
+ try (TFloat32 testImages = preprocessImages(testBatch.images());
+ TFloat32 testLabels = preprocessLabels(testBatch.labels());
+ var result = session.runner()
+ .fetch(accuracy)
+ .feed(images.asOutput(), testImages)
+ .feed(labels.asOutput(), testLabels)
+ .run()) {
+ TFloat32 accuracyValue = (TFloat32) result.get(0);
+ System.out.println("Accuracy: " + accuracyValue.getFloat());
+ }
+ }
+ }
+
+ private static final int VALIDATION_SIZE = 0;
+ private static final int TRAINING_BATCH_SIZE = 100;
+ private static final float LEARNING_RATE = 0.2f;
+
+ private static TFloat32 preprocessImages(ByteNdArray rawImages) {
+ Ops tf = Ops.create();
+
+ // Flatten images in a single dimension and normalize their pixels as floats.
+ long imageSize = rawImages.get(0).shape().size();
+ return tf.math.div(
+ tf.reshape(
+ tf.dtypes.cast(tf.constant(rawImages), TFloat32.class),
+ tf.array(-1L, imageSize)
+ ),
+ tf.constant(255.0f)
+ ).asTensor();
+ }
+
+ private static TFloat32 preprocessLabels(ByteNdArray rawLabels) {
+ Ops tf = Ops.create();
+
+ // Map labels to one hot vectors where only the expected predictions as a value of 1.0
+ return tf.oneHot(
+ tf.constant(rawLabels),
+ tf.constant(MnistDataset.NUM_CLASSES),
+ tf.constant(1.0f),
+ tf.constant(0.0f)
+ ).asTensor();
+ }
+
+ private final Graph graph;
+ private final MnistDataset dataset;
+
+ private SimpleMnist(Graph graph, MnistDataset dataset) {
+ this.graph = graph;
+ this.dataset = dataset;
+ }
+}
diff --git a/src/main/java/org/tensorflow/model/examples/regression/linear/LinearRegressionExample.java b/src/main/java/org/tensorflow/model/examples/regression/linear/LinearRegressionExample.java
new file mode 100644
index 0000000..b67deff
--- /dev/null
+++ b/src/main/java/org/tensorflow/model/examples/regression/linear/LinearRegressionExample.java
@@ -0,0 +1,138 @@
+/*
+ * Copyright 2020 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.
+ * =======================================================================
+ */
+package org.tensorflow.model.examples.regression.linear;
+
+import java.util.List;
+import java.util.Random;
+import org.tensorflow.Graph;
+import org.tensorflow.Session;
+import org.tensorflow.framework.optimizers.GradientDescent;
+import org.tensorflow.framework.optimizers.Optimizer;
+import org.tensorflow.ndarray.Shape;
+import org.tensorflow.op.Op;
+import org.tensorflow.op.Ops;
+import org.tensorflow.op.core.Placeholder;
+import org.tensorflow.op.core.Variable;
+import org.tensorflow.op.math.Add;
+import org.tensorflow.op.math.Div;
+import org.tensorflow.op.math.Mul;
+import org.tensorflow.op.math.Pow;
+import org.tensorflow.types.TFloat32;
+
+/**
+ * In this example TensorFlow finds the weight and bias of the linear regression during 1 epoch,
+ * training on observations one by one.
+ *
+ * Also, the weight and bias are extracted and printed.
+ */
+public class LinearRegressionExample {
+ /**
+ * Amount of data points.
+ */
+ private static final int N = 10;
+
+ /**
+ * This value is used to fill the Y placeholder in prediction.
+ */
+ public static final float LEARNING_RATE = 0.1f;
+ public static final String WEIGHT_VARIABLE_NAME = "weight";
+ public static final String BIAS_VARIABLE_NAME = "bias";
+
+ public static void main(String[] args) {
+ // Prepare the data
+ float[] xValues = {1f, 2f, 3f, 4f, 5f, 6f, 7f, 8f, 9f, 10f};
+ float[] yValues = new float[N];
+
+ Random rnd = new Random(42);
+
+ for (int i = 0; i < yValues.length; i++) {
+ yValues[i] = (float) (10 * xValues[i] + 2 + 0.1 * (rnd.nextDouble() - 0.5));
+ }
+
+ try (Graph graph = new Graph()) {
+ Ops tf = Ops.create(graph);
+
+ // Define placeholders
+ Placeholder xData = tf.placeholder(TFloat32.class, Placeholder.shape(Shape.scalar()));
+ Placeholder yData = tf.placeholder(TFloat32.class, Placeholder.shape(Shape.scalar()));
+
+ // Define variables
+ Variable weight = tf.withName(WEIGHT_VARIABLE_NAME).variable(tf.constant(1f));
+ Variable bias = tf.withName(BIAS_VARIABLE_NAME).variable(tf.constant(1f));
+
+ // Define the model function weight*x + bias
+ Mul mul = tf.math.mul(xData, weight);
+ Add yPredicted = tf.math.add(mul, bias);
+
+ // Define loss function MSE
+ Pow sum = tf.math.pow(tf.math.sub(yPredicted, yData), tf.constant(2f));
+ Div mse = tf.math.div(sum, tf.constant(2f * N));
+
+ // Back-propagate gradients to variables for training
+ Optimizer optimizer = new GradientDescent(graph, LEARNING_RATE);
+ Op minimize = optimizer.minimize(mse);
+
+ try (Session session = new Session(graph)) {
+
+ // Train the model on data
+ for (int i = 0; i < xValues.length; i++) {
+ float y = yValues[i];
+ float x = xValues[i];
+
+ try (TFloat32 xTensor = TFloat32.scalarOf(x);
+ TFloat32 yTensor = TFloat32.scalarOf(y)) {
+
+ session.runner()
+ .addTarget(minimize)
+ .feed(xData.asOutput(), xTensor)
+ .feed(yData.asOutput(), yTensor)
+ .run();
+
+ System.out.println("Training phase");
+ System.out.println("x is " + x + " y is " + y);
+ }
+ }
+
+ // Extract linear regression model weight and bias values
+ try (var result = session.runner()
+ .fetch(WEIGHT_VARIABLE_NAME)
+ .fetch(BIAS_VARIABLE_NAME)
+ .run()) {
+ System.out.println("Weight is " + result.get(WEIGHT_VARIABLE_NAME));
+ System.out.println("Bias is " + result.get(BIAS_VARIABLE_NAME));
+ }
+
+ // Let's predict y for x = 10f
+ float x = 10f;
+ float predictedY = 0f;
+
+ try (TFloat32 xTensor = TFloat32.scalarOf(x);
+ TFloat32 yTensor = TFloat32.scalarOf(predictedY);
+ TFloat32 yPredictedTensor = (TFloat32)session.runner()
+ .feed(xData.asOutput(), xTensor)
+ .feed(yData.asOutput(), yTensor)
+ .fetch(yPredicted)
+ .run().get(0)) {
+
+ predictedY = yPredictedTensor.getFloat();
+
+ System.out.println("Predicted value: " + predictedY);
+ }
+ }
+ }
+ }
+}
diff --git a/src/main/java/org/tensorflow/model/examples/tensors/TensorCreation.java b/src/main/java/org/tensorflow/model/examples/tensors/TensorCreation.java
new file mode 100644
index 0000000..35955d7
--- /dev/null
+++ b/src/main/java/org/tensorflow/model/examples/tensors/TensorCreation.java
@@ -0,0 +1,100 @@
+/*
+ * Copyright 2020 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.
+ * =======================================================================
+ */
+package org.tensorflow.model.examples.tensors;
+
+import org.tensorflow.ndarray.Shape;
+import org.tensorflow.ndarray.IntNdArray;
+import org.tensorflow.ndarray.NdArrays;
+import org.tensorflow.types.TInt32;
+
+import java.util.Arrays;
+
+/**
+ * Creates a few tensors of ranks: 0, 1, 2, 3.
+ */
+public class TensorCreation {
+
+ public static void main(String[] args) {
+ // Rank 0 Tensor
+ TInt32 rank0Tensor = TInt32.scalarOf(42);
+
+ System.out.println("---- Scalar tensor ---------");
+
+ System.out.println("DataType: " + rank0Tensor.dataType().name());
+
+ System.out.println("Rank: " + rank0Tensor.shape().size());
+
+ System.out.println("Shape: " + Arrays.toString(rank0Tensor.shape().asArray()));
+
+ rank0Tensor.scalars().forEach(value -> System.out.println("Value: " + value.getObject()));
+
+ // Rank 1 Tensor
+ TInt32 rank1Tensor = TInt32.vectorOf(1, 2, 3, 4, 5, 6, 7, 8, 9, 10);
+
+ System.out.println("---- Vector tensor ---------");
+
+ System.out.println("DataType: " + rank1Tensor.dataType().name());
+
+ System.out.println("Rank: " + rank1Tensor.shape().size());
+
+ System.out.println("Shape: " + Arrays.toString(rank1Tensor.shape().asArray()));
+
+ System.out.println("6th element: " + rank1Tensor.getInt(5));
+
+ // Rank 2 Tensor
+ // 3x2 matrix of ints.
+ IntNdArray matrix2d = NdArrays.ofInts(Shape.of(3, 2));
+
+ matrix2d.set(NdArrays.vectorOf(1, 2), 0)
+ .set(NdArrays.vectorOf(3, 4), 1)
+ .set(NdArrays.vectorOf(5, 6), 2);
+
+ TInt32 rank2Tensor = TInt32.tensorOf(matrix2d);
+
+ System.out.println("---- Matrix tensor ---------");
+
+ System.out.println("DataType: " + rank2Tensor.dataType().name());
+
+ System.out.println("Rank: " + rank2Tensor.shape().size());
+
+ System.out.println("Shape: " + Arrays.toString(rank2Tensor.shape().asArray()));
+
+ System.out.println("6th element: " + rank2Tensor.getInt(2, 1));
+
+ // Rank 3 Tensor
+ // 3*2*4 matrix of ints.
+ IntNdArray matrix3d = NdArrays.ofInts(Shape.of(3, 2, 4));
+
+ matrix3d.elements(0).forEach(matrix -> {
+ matrix
+ .set(NdArrays.vectorOf(1, 2, 3, 4), 0)
+ .set(NdArrays.vectorOf(5, 6, 7, 8), 1);
+ });
+
+ TInt32 rank3Tensor = TInt32.tensorOf(matrix3d);
+
+ System.out.println("---- Matrix tensor ---------");
+
+ System.out.println("DataType: " + rank3Tensor.dataType().name());
+
+ System.out.println("Rank: " + rank3Tensor.shape().size());
+
+ System.out.println("Shape: " + Arrays.toString(rank3Tensor.shape().asArray()));
+
+ System.out.println("n-th element: " + rank3Tensor.getInt(2, 1, 3));
+ }
+}
diff --git a/src/main/resources/META-INF/MANIFEST.MF b/src/main/resources/META-INF/MANIFEST.MF
new file mode 100644
index 0000000..e69de29
diff --git a/src/main/resources/fashionmnist/Readme.md b/src/main/resources/fashionmnist/Readme.md
new file mode 100644
index 0000000..95b6f38
--- /dev/null
+++ b/src/main/resources/fashionmnist/Readme.md
@@ -0,0 +1,6 @@
+This dataset is distributed under MIT License and presented in next paper.
+Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms. Han Xiao, Kashif Rasul, Roland Vollgraf. arXiv:1708.07747
+
+The data was downloaded from the FashionMnist Repository
+https://github.com/zalandoresearch/fashion-mnist/tree/master/data/fashion
+
diff --git a/src/main/resources/fashionmnist/t10k-images-idx3-ubyte.gz b/src/main/resources/fashionmnist/t10k-images-idx3-ubyte.gz
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diff --git a/src/main/resources/mnist/train-images-idx3-ubyte.gz b/src/main/resources/mnist/train-images-idx3-ubyte.gz
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diff --git a/src/main/resources/mnist/train-labels-idx1-ubyte.gz b/src/main/resources/mnist/train-labels-idx1-ubyte.gz
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diff --git a/tensorflow-examples-legacy/README.md b/tensorflow-examples-legacy/README.md
deleted file mode 100644
index 6aa1a30..0000000
--- a/tensorflow-examples-legacy/README.md
+++ /dev/null
@@ -1,20 +0,0 @@
-# TensorFlow for Java: Examples
-
-These examples include using pre-trained models for [image
-classification](label_image) and [object detection](object_detection),
-and driving the [training](training) of a pre-defined model - all using the
-TensorFlow Java API.
-
-The TensorFlow Java API does not have feature parity with the Python API.
-The Java API is most suitable for inference using pre-trained models
-and for training pre-defined models from a single Java process.
-
-Python will be the most convenient language for defining the
-numerical computation of a model.
-
-- [Slides](https://docs.google.com/presentation/d/e/2PACX-1vQ6DzxNTBrJo7K5P8t5_rBRGnyJoPUPBVOJR4ooHCwi4TlBFnIriFmI719rDNpcQzojqsV58aUqmBBx/pub?start=false&loop=false&delayms=3000) from January 2018.
-- See README.md in each subdirectory for details.
-
-For a recent real-world example, see the use of this API to [assess microscope
-image quality](https://research.googleblog.com/2018/03/using-deep-learning-to-facilitate.html)
-in the image processing package [Fiji (ImageJ)](https://fiji.sc/).
diff --git a/tensorflow-examples-legacy/docker/Dockerfile b/tensorflow-examples-legacy/docker/Dockerfile
deleted file mode 100644
index 7f71f83..0000000
--- a/tensorflow-examples-legacy/docker/Dockerfile
+++ /dev/null
@@ -1,7 +0,0 @@
-FROM tensorflow/tensorflow:1.4.0
-WORKDIR /
-RUN apt-get update
-RUN apt-get -y install maven openjdk-8-jdk
-RUN mvn dependency:get -Dartifact=org.tensorflow:tensorflow:1.4.0
-RUN mvn dependency:get -Dartifact=org.tensorflow:proto:1.4.0
-CMD ["/bin/bash", "-l"]
diff --git a/tensorflow-examples-legacy/docker/README.md b/tensorflow-examples-legacy/docker/README.md
deleted file mode 100644
index eaa3ca3..0000000
--- a/tensorflow-examples-legacy/docker/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
-Dockerfile for building an image suitable for running the Java examples.
-
-Typical usage:
-
-```
-docker build -t java-tensorflow .
-docker run -it --rm -v ${PWD}/..:/examples -w /examples java-tensorflow
-```
-
-That second command will pop you into a shell which has all
-the dependencies required to execute the scripts and Java
-examples.
-
-The script `sanity_test.sh` builds this container and runs a compilation
-check on all the maven projects.
diff --git a/tensorflow-examples-legacy/docker/sanity_test.sh b/tensorflow-examples-legacy/docker/sanity_test.sh
deleted file mode 100755
index a4343f2..0000000
--- a/tensorflow-examples-legacy/docker/sanity_test.sh
+++ /dev/null
@@ -1,7 +0,0 @@
-#!/bin/bash
-#
-# Silly sanity test
-DIR="$(cd "$(dirname "$0")" && pwd -P)"
-
-docker build -t java-tensorflow .
-docker run -it --rm -v ${PWD}/..:/examples java-tensorflow bash /examples/docker/test_inside_container.sh
diff --git a/tensorflow-examples-legacy/docker/test_inside_container.sh b/tensorflow-examples-legacy/docker/test_inside_container.sh
deleted file mode 100644
index 221a023..0000000
--- a/tensorflow-examples-legacy/docker/test_inside_container.sh
+++ /dev/null
@@ -1,12 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-cd /examples/label_image
-mvn compile
-
-cd /examples/object_detection
-mvn compile
-
-cd /examples/training
-mvn compile
diff --git a/tensorflow-examples-legacy/label_image/.gitignore b/tensorflow-examples-legacy/label_image/.gitignore
deleted file mode 100644
index 9aeb6ae..0000000
--- a/tensorflow-examples-legacy/label_image/.gitignore
+++ /dev/null
@@ -1,3 +0,0 @@
-images
-src/main/resources
-target
diff --git a/tensorflow-examples-legacy/label_image/README.md b/tensorflow-examples-legacy/label_image/README.md
deleted file mode 100644
index b2dbef4..0000000
--- a/tensorflow-examples-legacy/label_image/README.md
+++ /dev/null
@@ -1,23 +0,0 @@
-# Image Classification Example
-
-1. Download the model:
- - If you have [TensorFlow 1.4+ for Python installed](https://www.tensorflow.org/install/),
- run `python ./download.py`
- - If not, but you have [docker](https://www.docker.com/get-docker) installed,
- run `download.sh`.
-
-2. Compile [`LabelImage.java`](src/main/java/LabelImage.java):
-
- ```
- mvn compile
- ```
-
-3. Download some sample images:
- If you already have some images, great. Otherwise `download_sample_images.sh`
- gets a few.
-
-3. Classify!
-
- ```
- mvn -q exec:java -Dexec.args=""
- ```
diff --git a/tensorflow-examples-legacy/label_image/download.py b/tensorflow-examples-legacy/label_image/download.py
deleted file mode 100644
index c9082c2..0000000
--- a/tensorflow-examples-legacy/label_image/download.py
+++ /dev/null
@@ -1,93 +0,0 @@
-"""Create an image classification graph.
-
-Script to download a pre-trained image classifier and tweak it so that
-the model accepts raw bytes of an encoded image.
-
-Doing so involves some model-specific normalization of an image.
-Ideally, this would have been part of the image classifier model,
-but the particular model being used didn't include this normalization,
-so this script does the necessary tweaking.
-"""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from six.moves import urllib
-import os
-import zipfile
-import tensorflow as tf
-
-URL = 'https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip'
-LABELS_FILE = 'imagenet_comp_graph_label_strings.txt'
-GRAPH_FILE = 'tensorflow_inception_graph.pb'
-
-GRAPH_INPUT_TENSOR = 'input:0'
-GRAPH_PROBABILITIES_TENSOR = 'output:0'
-
-IMAGE_HEIGHT = 224
-IMAGE_WIDTH = 224
-MEAN = 117
-SCALE = 1
-
-LOCAL_DIR = 'src/main/resources'
-
-
-def download():
- print('Downloading %s' % URL)
- zip_filename, _ = urllib.request.urlretrieve(URL)
- with zipfile.ZipFile(zip_filename) as zip:
- zip.extract(LABELS_FILE)
- zip.extract(GRAPH_FILE)
- os.rename(LABELS_FILE, os.path.join(LOCAL_DIR, 'labels.txt'))
- os.rename(GRAPH_FILE, os.path.join(LOCAL_DIR, 'graph.pb'))
-
-
-def create_graph_to_decode_and_normalize_image():
- """See file docstring.
-
- Returns:
- input: The placeholder to feed the raw bytes of an encoded image.
- y: A Tensor (the decoded, normalized image) to be fed to the graph.
- """
- image = tf.placeholder(tf.string, shape=(), name='encoded_image_bytes')
- with tf.name_scope("preprocess"):
- y = tf.image.decode_image(image, channels=3)
- y = tf.cast(y, tf.float32)
- y = tf.expand_dims(y, axis=0)
- y = tf.image.resize_bilinear(y, (IMAGE_HEIGHT, IMAGE_WIDTH))
- y = (y - MEAN) / SCALE
- return (image, y)
-
-
-def patch_graph():
- """Create graph.pb that applies the model in URL to raw image bytes."""
- with tf.Graph().as_default() as g:
- input_image, image_normalized = create_graph_to_decode_and_normalize_image()
- original_graph_def = tf.GraphDef()
- with open(os.path.join(LOCAL_DIR, 'graph.pb')) as f:
- original_graph_def.ParseFromString(f.read())
- softmax = tf.import_graph_def(
- original_graph_def,
- name='inception',
- input_map={GRAPH_INPUT_TENSOR: image_normalized},
- return_elements=[GRAPH_PROBABILITIES_TENSOR])
- # We're constructing a graph that accepts a single image (as opposed to a
- # batch of images), so might as well make the output be a vector of
- # probabilities, instead of a batch of vectors with batch size 1.
- output_probabilities = tf.squeeze(softmax, name='probabilities')
- # Overwrite the graph.
- with open(os.path.join(LOCAL_DIR, 'graph.pb'), 'w') as f:
- f.write(g.as_graph_def().SerializeToString())
- print('------------------------------------------------------------')
- print('MODEL GRAPH : graph.pb')
- print('LABELS : labels.txt')
- print('INPUT TENSOR : %s' % input_image.op.name)
- print('OUTPUT TENSOR: %s' % output_probabilities.op.name)
-
-
-if __name__ == '__main__':
- if not os.path.exists(LOCAL_DIR):
- os.makedirs(LOCAL_DIR)
- download()
- patch_graph()
diff --git a/tensorflow-examples-legacy/label_image/download.sh b/tensorflow-examples-legacy/label_image/download.sh
deleted file mode 100755
index 22ca88b..0000000
--- a/tensorflow-examples-legacy/label_image/download.sh
+++ /dev/null
@@ -1,4 +0,0 @@
-#!/bin/bash
-
-DIR="$(cd "$(dirname "$0")" && pwd -P)"
-docker run -it -v ${DIR}:/x -w /x --rm tensorflow/tensorflow:1.4.0 python download.py
diff --git a/tensorflow-examples-legacy/label_image/download_sample_images.sh b/tensorflow-examples-legacy/label_image/download_sample_images.sh
deleted file mode 100755
index 17deb84..0000000
--- a/tensorflow-examples-legacy/label_image/download_sample_images.sh
+++ /dev/null
@@ -1,10 +0,0 @@
-#!/bin/bash
-DIR=$(dirname $0)
-mkdir -p ${DIR}/images
-cd ${DIR}/images
-
-# Some random images
-curl -o "porcupine.jpg" -L "https://cdn.pixabay.com/photo/2014/11/06/12/46/porcupines-519145_960_720.jpg"
-curl -o "whale.jpg" -L "https://static.pexels.com/photos/417196/pexels-photo-417196.jpeg"
-curl -o "terrier1u.jpg" -L "https://upload.wikimedia.org/wikipedia/commons/3/34/Australian_Terrier_Melly_%282%29.JPG"
-curl -o "terrier2.jpg" -L "https://cdn.pixabay.com/photo/2014/05/13/07/44/yorkshire-terrier-343198_960_720.jpg"
diff --git a/tensorflow-examples-legacy/label_image/pom.xml b/tensorflow-examples-legacy/label_image/pom.xml
deleted file mode 100644
index 96ace38..0000000
--- a/tensorflow-examples-legacy/label_image/pom.xml
+++ /dev/null
@@ -1,26 +0,0 @@
-
- 4.0.0
- org.myorg
- label-image
- 1.0-SNAPSHOT
-
- LabelImage
-
-
- 1.7
- 1.7
-
-
-
- org.tensorflow
- tensorflow
- 1.4.0
-
-
-
- com.google.guava
- guava
- 23.6-jre
-
-
-
diff --git a/tensorflow-examples-legacy/label_image/src/main/java/LabelImage.java b/tensorflow-examples-legacy/label_image/src/main/java/LabelImage.java
deleted file mode 100644
index 1bcd906..0000000
--- a/tensorflow-examples-legacy/label_image/src/main/java/LabelImage.java
+++ /dev/null
@@ -1,98 +0,0 @@
-/* Copyright 2018 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.
-==============================================================================*/
-
-import com.google.common.io.ByteStreams;
-import java.io.BufferedReader;
-import java.io.IOException;
-import java.io.InputStream;
-import java.io.InputStreamReader;
-import java.nio.file.Files;
-import java.nio.file.Path;
-import java.nio.file.Paths;
-import java.util.ArrayList;
-import java.util.List;
-import org.tensorflow.Graph;
-import org.tensorflow.Session;
-import org.tensorflow.Tensor;
-import org.tensorflow.Tensors;
-
-/**
- * Simplified version of
- * https://github.com/tensorflow/tensorflow/blob/r1.4/tensorflow/java/src/main/java/org/tensorflow/examples/LabelImage.java
- */
-public class LabelImage {
- public static void main(String[] args) throws Exception {
- if (args.length < 1) {
- System.err.println("USAGE: Provide a list of image filenames");
- System.exit(1);
- }
- final List labels = loadLabels();
- try (Graph graph = new Graph();
- Session session = new Session(graph)) {
- graph.importGraphDef(loadGraphDef());
-
- float[] probabilities = null;
- for (String filename : args) {
- byte[] bytes = Files.readAllBytes(Paths.get(filename));
- try (Tensor input = Tensors.create(bytes);
- Tensor output =
- session
- .runner()
- .feed("encoded_image_bytes", input)
- .fetch("probabilities")
- .run()
- .get(0)
- .expect(Float.class)) {
- if (probabilities == null) {
- probabilities = new float[(int) output.shape()[0]];
- }
- output.copyTo(probabilities);
- int label = argmax(probabilities);
- System.out.printf(
- "%-30s --> %-15s (%.2f%% likely)\n",
- filename, labels.get(label), probabilities[label] * 100.0);
- }
- }
- }
- }
-
- private static byte[] loadGraphDef() throws IOException {
- try (InputStream is = LabelImage.class.getClassLoader().getResourceAsStream("graph.pb")) {
- return ByteStreams.toByteArray(is);
- }
- }
-
- private static ArrayList loadLabels() throws IOException {
- ArrayList labels = new ArrayList();
- String line;
- final InputStream is = LabelImage.class.getClassLoader().getResourceAsStream("labels.txt");
- try (BufferedReader reader = new BufferedReader(new InputStreamReader(is))) {
- while ((line = reader.readLine()) != null) {
- labels.add(line);
- }
- }
- return labels;
- }
-
- private static int argmax(float[] probabilities) {
- int best = 0;
- for (int i = 1; i < probabilities.length; ++i) {
- if (probabilities[i] > probabilities[best]) {
- best = i;
- }
- }
- return best;
- }
-}
diff --git a/tensorflow-examples-legacy/object_detection/.gitignore b/tensorflow-examples-legacy/object_detection/.gitignore
deleted file mode 100644
index 8149788..0000000
--- a/tensorflow-examples-legacy/object_detection/.gitignore
+++ /dev/null
@@ -1,5 +0,0 @@
-images
-labels
-models
-src/main/protobuf
-target
diff --git a/tensorflow-examples-legacy/object_detection/README.md b/tensorflow-examples-legacy/object_detection/README.md
deleted file mode 100644
index 3bb554a..0000000
--- a/tensorflow-examples-legacy/object_detection/README.md
+++ /dev/null
@@ -1,55 +0,0 @@
-# Object Detection in Java
-
-Example of using pre-trained models of the [TensorFlow Object Detection
-API](https://github.com/tensorflow/models/tree/master/research/object_detection)
-in Java.
-
-## Quickstart
-
-1. Download some metadata files:
- ```
- ./download.sh
- ```
-
-2. Download a model from the [object detection API model
- zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md).
- For example:
- ```
- mkdir -p models
- curl -L \
- http://download.tensorflow.org/models/object_detection/ssd_inception_v2_coco_2017_11_17.tar.gz \
- | tar -xz -C models/
- ```
-
-3. Have some test images handy. For example:
- ```
- mkdir -p images
- curl -L -o images/test.jpg \
- https://pixnio.com/free-images/people/mother-father-and-children-washing-dog-labrador-retriever-outside-in-the-fresh-air-725x483.jpg
- ```
-
-4. Compile and run!
- ```
- mvn -q compile exec:java \
- -Dexec.args="models/ssd_inception_v2_coco_2017_11_17/saved_model labels/mscoco_label_map.pbtxt images/test.jpg"
- ```
-
-## Notes
-
-- This example demonstrates the use of the TensorFlow [SavedModel
- format](https://www.tensorflow.org/guide/saved_model). If you have
- TensorFlow for Python installed, you could explore the model to get the names
- of the tensors using `saved_model_cli` command. For example:
- ```
- saved_model_cli show --dir models/ssd_inception_v2_coco_2017_11_17/saved_model/ --all
- ```
-
-- The file in `src/main/object_detection/protos/` was generated using:
-
- ```
- ./download.sh
- protoc -Isrc/main/protobuf --java_out=src/main/java src/main/protobuf/string_int_label_map.proto
- ```
-
- Where `protoc` was downloaded from
- https://github.com/google/protobuf/releases/tag/v3.5.1
diff --git a/tensorflow-examples-legacy/object_detection/download.sh b/tensorflow-examples-legacy/object_detection/download.sh
deleted file mode 100755
index f301af2..0000000
--- a/tensorflow-examples-legacy/object_detection/download.sh
+++ /dev/null
@@ -1,18 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-DIR="$(cd "$(dirname "$0")" && pwd -P)"
-cd "${DIR}"
-
-# The protobuf file needed for mapping labels to human readable names.
-# From:
-# https://github.com/tensorflow/models/blob/f87a58c/research/object_detection/protos/string_int_label_map.proto
-mkdir -p src/main/protobuf
-curl -L -o src/main/protobuf/string_int_label_map.proto "https://raw.githubusercontent.com/tensorflow/models/f87a58cd96d45de73c9a8330a06b2ab56749a7fa/research/object_detection/protos/string_int_label_map.proto"
-
-# Labels from:
-# https://github.com/tensorflow/models/tree/865c14c/research/object_detection/data
-mkdir -p labels
-curl -L -o labels/mscoco_label_map.pbtxt "https://raw.githubusercontent.com/tensorflow/models/865c14c1209cb9ae188b2a1b5f0883c72e050d4c/research/object_detection/data/mscoco_label_map.pbtxt"
-curl -L -o labels/oid_bbox_trainable_label_map.pbtxt "https://raw.githubusercontent.com/tensorflow/models/865c14c1209cb9ae188b2a1b5f0883c72e050d4c/research/object_detection/data/oid_bbox_trainable_label_map.pbtxt"
diff --git a/tensorflow-examples-legacy/object_detection/pom.xml b/tensorflow-examples-legacy/object_detection/pom.xml
deleted file mode 100644
index c9123da..0000000
--- a/tensorflow-examples-legacy/object_detection/pom.xml
+++ /dev/null
@@ -1,25 +0,0 @@
-
- 4.0.0
- org.myorg
- detect-objects
- 1.0-SNAPSHOT
-
- DetectObjects
-
-
- 1.7
- 1.7
-
-
-
- org.tensorflow
- tensorflow
- 1.4.0
-
-
- org.tensorflow
- proto
- 1.4.0
-
-
-
diff --git a/tensorflow-examples-legacy/object_detection/src/main/java/DetectObjects.java b/tensorflow-examples-legacy/object_detection/src/main/java/DetectObjects.java
deleted file mode 100644
index 6f74240..0000000
--- a/tensorflow-examples-legacy/object_detection/src/main/java/DetectObjects.java
+++ /dev/null
@@ -1,188 +0,0 @@
-/* Copyright 2018 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.
-==============================================================================*/
-
-import static object_detection.protos.StringIntLabelMapOuterClass.StringIntLabelMap;
-import static object_detection.protos.StringIntLabelMapOuterClass.StringIntLabelMapItem;
-
-import com.google.protobuf.TextFormat;
-import java.awt.Graphics2D;
-import java.awt.image.BufferedImage;
-import java.awt.image.DataBufferByte;
-import java.io.File;
-import java.io.IOException;
-import java.io.PrintStream;
-import java.nio.ByteBuffer;
-import java.nio.charset.StandardCharsets;
-import java.nio.file.Files;
-import java.nio.file.Paths;
-import java.util.List;
-import java.util.Map;
-import javax.imageio.ImageIO;
-import org.tensorflow.SavedModelBundle;
-import org.tensorflow.Tensor;
-import org.tensorflow.framework.MetaGraphDef;
-import org.tensorflow.framework.SignatureDef;
-import org.tensorflow.framework.TensorInfo;
-import org.tensorflow.types.UInt8;
-
-/**
- * Java inference for the Object Detection API at:
- * https://github.com/tensorflow/models/blob/master/research/object_detection/
- */
-public class DetectObjects {
- public static void main(String[] args) throws Exception {
- if (args.length < 3) {
- printUsage(System.err);
- System.exit(1);
- }
- final String[] labels = loadLabels(args[1]);
- try (SavedModelBundle model = SavedModelBundle.load(args[0], "serve")) {
- printSignature(model);
- for (int arg = 2; arg < args.length; arg++) {
- final String filename = args[arg];
- List> outputs = null;
- try (Tensor input = makeImageTensor(filename)) {
- outputs =
- model
- .session()
- .runner()
- .feed("image_tensor", input)
- .fetch("detection_scores")
- .fetch("detection_classes")
- .fetch("detection_boxes")
- .run();
- }
- try (Tensor scoresT = outputs.get(0).expect(Float.class);
- Tensor classesT = outputs.get(1).expect(Float.class);
- Tensor boxesT = outputs.get(2).expect(Float.class)) {
- // All these tensors have:
- // - 1 as the first dimension
- // - maxObjects as the second dimension
- // While boxesT will have 4 as the third dimension (2 sets of (x, y) coordinates).
- // This can be verified by looking at scoresT.shape() etc.
- int maxObjects = (int) scoresT.shape()[1];
- float[] scores = scoresT.copyTo(new float[1][maxObjects])[0];
- float[] classes = classesT.copyTo(new float[1][maxObjects])[0];
- float[][] boxes = boxesT.copyTo(new float[1][maxObjects][4])[0];
- // Print all objects whose score is at least 0.5.
- System.out.printf("* %s\n", filename);
- boolean foundSomething = false;
- for (int i = 0; i < scores.length; ++i) {
- if (scores[i] < 0.5) {
- continue;
- }
- foundSomething = true;
- System.out.printf("\tFound %-20s (score: %.4f)\n", labels[(int) classes[i]], scores[i]);
- }
- if (!foundSomething) {
- System.out.println("No objects detected with a high enough score.");
- }
- }
- }
- }
- }
-
- private static void printSignature(SavedModelBundle model) throws Exception {
- MetaGraphDef m = MetaGraphDef.parseFrom(model.metaGraphDef());
- SignatureDef sig = m.getSignatureDefOrThrow("serving_default");
- int numInputs = sig.getInputsCount();
- int i = 1;
- System.out.println("MODEL SIGNATURE");
- System.out.println("Inputs:");
- for (Map.Entry entry : sig.getInputsMap().entrySet()) {
- TensorInfo t = entry.getValue();
- System.out.printf(
- "%d of %d: %-20s (Node name in graph: %-20s, type: %s)\n",
- i++, numInputs, entry.getKey(), t.getName(), t.getDtype());
- }
- int numOutputs = sig.getOutputsCount();
- i = 1;
- System.out.println("Outputs:");
- for (Map.Entry entry : sig.getOutputsMap().entrySet()) {
- TensorInfo t = entry.getValue();
- System.out.printf(
- "%d of %d: %-20s (Node name in graph: %-20s, type: %s)\n",
- i++, numOutputs, entry.getKey(), t.getName(), t.getDtype());
- }
- System.out.println("-----------------------------------------------");
- }
-
- private static String[] loadLabels(String filename) throws Exception {
- String text = new String(Files.readAllBytes(Paths.get(filename)), StandardCharsets.UTF_8);
- StringIntLabelMap.Builder builder = StringIntLabelMap.newBuilder();
- TextFormat.merge(text, builder);
- StringIntLabelMap proto = builder.build();
- int maxId = 0;
- for (StringIntLabelMapItem item : proto.getItemList()) {
- if (item.getId() > maxId) {
- maxId = item.getId();
- }
- }
- String[] ret = new String[maxId + 1];
- for (StringIntLabelMapItem item : proto.getItemList()) {
- ret[item.getId()] = item.getDisplayName();
- }
- return ret;
- }
-
- private static void bgr2rgb(byte[] data) {
- for (int i = 0; i < data.length; i += 3) {
- byte tmp = data[i];
- data[i] = data[i + 2];
- data[i + 2] = tmp;
- }
- }
-
- private static Tensor makeImageTensor(String filename) throws IOException {
- BufferedImage img = ImageIO.read(new File(filename));
- if (img.getType() != BufferedImage.TYPE_3BYTE_BGR) {
- BufferedImage newImage = new BufferedImage(
- img.getWidth(), img.getHeight(), BufferedImage.TYPE_3BYTE_BGR);
- Graphics2D g = newImage.createGraphics();
- g.drawImage(img, 0, 0, img.getWidth(), img.getHeight(), null);
- g.dispose();
- img = newImage;
- }
-
- byte[] data = ((DataBufferByte) img.getData().getDataBuffer()).getData();
- // ImageIO.read seems to produce BGR-encoded images, but the model expects RGB.
- bgr2rgb(data);
- final long BATCH_SIZE = 1;
- final long CHANNELS = 3;
- long[] shape = new long[] {BATCH_SIZE, img.getHeight(), img.getWidth(), CHANNELS};
- return Tensor.create(UInt8.class, shape, ByteBuffer.wrap(data));
- }
-
- private static void printUsage(PrintStream s) {
- s.println("USAGE: [] []");
- s.println("");
- s.println("Where");
- s.println(" is the path to the SavedModel directory of the model to use.");
- s.println(" For example, the saved_model directory in tarballs from ");
- s.println(
- " https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md)");
- s.println("");
- s.println(
- " is the path to a file containing information about the labels detected by the model.");
- s.println(" For example, one of the .pbtxt files from ");
- s.println(
- " https://github.com/tensorflow/models/tree/master/research/object_detection/data");
- s.println("");
- s.println(" is the path to an image file.");
- s.println(" Sample images can be found from the COCO, Kitti, or Open Images dataset.");
- s.println(
- " See: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md");
- }
-}
diff --git a/tensorflow-examples-legacy/object_detection/src/main/java/object_detection/protos/StringIntLabelMapOuterClass.java b/tensorflow-examples-legacy/object_detection/src/main/java/object_detection/protos/StringIntLabelMapOuterClass.java
deleted file mode 100644
index a6808ce..0000000
--- a/tensorflow-examples-legacy/object_detection/src/main/java/object_detection/protos/StringIntLabelMapOuterClass.java
+++ /dev/null
@@ -1,1785 +0,0 @@
-// Generated by the protocol buffer compiler. DO NOT EDIT!
-// source: string_int_label_map.proto
-
-package object_detection.protos;
-
-public final class StringIntLabelMapOuterClass {
- private StringIntLabelMapOuterClass() {}
- public static void registerAllExtensions(
- com.google.protobuf.ExtensionRegistryLite registry) {
- }
-
- public static void registerAllExtensions(
- com.google.protobuf.ExtensionRegistry registry) {
- registerAllExtensions(
- (com.google.protobuf.ExtensionRegistryLite) registry);
- }
- public interface StringIntLabelMapItemOrBuilder extends
- // @@protoc_insertion_point(interface_extends:object_detection.protos.StringIntLabelMapItem)
- com.google.protobuf.MessageOrBuilder {
-
- /**
- *
- * String name. The most common practice is to set this to a MID or synsets
- * id.
- *
- *
- * optional string name = 1;
- */
- boolean hasName();
- /**
- *
- * String name. The most common practice is to set this to a MID or synsets
- * id.
- *
- *
- * optional string name = 1;
- */
- java.lang.String getName();
- /**
- *
- * String name. The most common practice is to set this to a MID or synsets
- * id.
- *
- *
- * optional string name = 1;
- */
- com.google.protobuf.ByteString
- getNameBytes();
-
- /**
- *
- * Integer id that maps to the string name above. Label ids should start from
- * 1.
- *
- *
- * optional int32 id = 2;
- */
- boolean hasId();
- /**
- *
- * Integer id that maps to the string name above. Label ids should start from
- * 1.
- *
- *
- * optional int32 id = 2;
- */
- int getId();
-
- /**
- *
- * Human readable string label.
- *
- *
- * optional string display_name = 3;
- */
- boolean hasDisplayName();
- /**
- *
- * Human readable string label.
- *
- *
- * optional string display_name = 3;
- */
- java.lang.String getDisplayName();
- /**
- *
- * Human readable string label.
- *
- *
- * optional string display_name = 3;
- */
- com.google.protobuf.ByteString
- getDisplayNameBytes();
- }
- /**
- * Protobuf type {@code object_detection.protos.StringIntLabelMapItem}
- */
- public static final class StringIntLabelMapItem extends
- com.google.protobuf.GeneratedMessageV3 implements
- // @@protoc_insertion_point(message_implements:object_detection.protos.StringIntLabelMapItem)
- StringIntLabelMapItemOrBuilder {
- private static final long serialVersionUID = 0L;
- // Use StringIntLabelMapItem.newBuilder() to construct.
- private StringIntLabelMapItem(com.google.protobuf.GeneratedMessageV3.Builder builder) {
- super(builder);
- }
- private StringIntLabelMapItem() {
- name_ = "";
- id_ = 0;
- displayName_ = "";
- }
-
- @java.lang.Override
- public final com.google.protobuf.UnknownFieldSet
- getUnknownFields() {
- return this.unknownFields;
- }
- private StringIntLabelMapItem(
- com.google.protobuf.CodedInputStream input,
- com.google.protobuf.ExtensionRegistryLite extensionRegistry)
- throws com.google.protobuf.InvalidProtocolBufferException {
- this();
- if (extensionRegistry == null) {
- throw new java.lang.NullPointerException();
- }
- int mutable_bitField0_ = 0;
- com.google.protobuf.UnknownFieldSet.Builder unknownFields =
- com.google.protobuf.UnknownFieldSet.newBuilder();
- try {
- boolean done = false;
- while (!done) {
- int tag = input.readTag();
- switch (tag) {
- case 0:
- done = true;
- break;
- default: {
- if (!parseUnknownField(
- input, unknownFields, extensionRegistry, tag)) {
- done = true;
- }
- break;
- }
- case 10: {
- com.google.protobuf.ByteString bs = input.readBytes();
- bitField0_ |= 0x00000001;
- name_ = bs;
- break;
- }
- case 16: {
- bitField0_ |= 0x00000002;
- id_ = input.readInt32();
- break;
- }
- case 26: {
- com.google.protobuf.ByteString bs = input.readBytes();
- bitField0_ |= 0x00000004;
- displayName_ = bs;
- break;
- }
- }
- }
- } catch (com.google.protobuf.InvalidProtocolBufferException e) {
- throw e.setUnfinishedMessage(this);
- } catch (java.io.IOException e) {
- throw new com.google.protobuf.InvalidProtocolBufferException(
- e).setUnfinishedMessage(this);
- } finally {
- this.unknownFields = unknownFields.build();
- makeExtensionsImmutable();
- }
- }
- public static final com.google.protobuf.Descriptors.Descriptor
- getDescriptor() {
- return object_detection.protos.StringIntLabelMapOuterClass.internal_static_object_detection_protos_StringIntLabelMapItem_descriptor;
- }
-
- protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
- internalGetFieldAccessorTable() {
- return object_detection.protos.StringIntLabelMapOuterClass.internal_static_object_detection_protos_StringIntLabelMapItem_fieldAccessorTable
- .ensureFieldAccessorsInitialized(
- object_detection.protos.StringIntLabelMapOuterClass.StringIntLabelMapItem.class, object_detection.protos.StringIntLabelMapOuterClass.StringIntLabelMapItem.Builder.class);
- }
-
- private int bitField0_;
- public static final int NAME_FIELD_NUMBER = 1;
- private volatile java.lang.Object name_;
- /**
- *
- * String name. The most common practice is to set this to a MID or synsets
- * id.
- *
- *
- * optional string name = 1;
- */
- public boolean hasName() {
- return ((bitField0_ & 0x00000001) == 0x00000001);
- }
- /**
- *
- * String name. The most common practice is to set this to a MID or synsets
- * id.
- *
- *
- * optional string name = 1;
- */
- public java.lang.String getName() {
- java.lang.Object ref = name_;
- if (ref instanceof java.lang.String) {
- return (java.lang.String) ref;
- } else {
- com.google.protobuf.ByteString bs =
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- public com.google.protobuf.Parser getParserForType() {
- return PARSER;
- }
-
- public object_detection.protos.StringIntLabelMapOuterClass.StringIntLabelMap getDefaultInstanceForType() {
- return DEFAULT_INSTANCE;
- }
-
- }
-
- private static final com.google.protobuf.Descriptors.Descriptor
- internal_static_object_detection_protos_StringIntLabelMapItem_descriptor;
- private static final
- com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
- internal_static_object_detection_protos_StringIntLabelMapItem_fieldAccessorTable;
- private static final com.google.protobuf.Descriptors.Descriptor
- internal_static_object_detection_protos_StringIntLabelMap_descriptor;
- private static final
- com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
- internal_static_object_detection_protos_StringIntLabelMap_fieldAccessorTable;
-
- public static com.google.protobuf.Descriptors.FileDescriptor
- getDescriptor() {
- return descriptor;
- }
- private static com.google.protobuf.Descriptors.FileDescriptor
- descriptor;
- static {
- java.lang.String[] descriptorData = {
- "\n\032string_int_label_map.proto\022\027object_det" +
- "ection.protos\"G\n\025StringIntLabelMapItem\022\014" +
- "\n\004name\030\001 \001(\t\022\n\n\002id\030\002 \001(\005\022\024\n\014display_name" +
- "\030\003 \001(\t\"Q\n\021StringIntLabelMap\022<\n\004item\030\001 \003(" +
- "\0132..object_detection.protos.StringIntLab" +
- "elMapItem"
- };
- com.google.protobuf.Descriptors.FileDescriptor.InternalDescriptorAssigner assigner =
- new com.google.protobuf.Descriptors.FileDescriptor. InternalDescriptorAssigner() {
- public com.google.protobuf.ExtensionRegistry assignDescriptors(
- com.google.protobuf.Descriptors.FileDescriptor root) {
- descriptor = root;
- return null;
- }
- };
- com.google.protobuf.Descriptors.FileDescriptor
- .internalBuildGeneratedFileFrom(descriptorData,
- new com.google.protobuf.Descriptors.FileDescriptor[] {
- }, assigner);
- internal_static_object_detection_protos_StringIntLabelMapItem_descriptor =
- getDescriptor().getMessageTypes().get(0);
- internal_static_object_detection_protos_StringIntLabelMapItem_fieldAccessorTable = new
- com.google.protobuf.GeneratedMessageV3.FieldAccessorTable(
- internal_static_object_detection_protos_StringIntLabelMapItem_descriptor,
- new java.lang.String[] { "Name", "Id", "DisplayName", });
- internal_static_object_detection_protos_StringIntLabelMap_descriptor =
- getDescriptor().getMessageTypes().get(1);
- internal_static_object_detection_protos_StringIntLabelMap_fieldAccessorTable = new
- com.google.protobuf.GeneratedMessageV3.FieldAccessorTable(
- internal_static_object_detection_protos_StringIntLabelMap_descriptor,
- new java.lang.String[] { "Item", });
- }
-
- // @@protoc_insertion_point(outer_class_scope)
-}
diff --git a/tensorflow-examples-legacy/training/.gitignore b/tensorflow-examples-legacy/training/.gitignore
deleted file mode 100644
index e8448ec..0000000
--- a/tensorflow-examples-legacy/training/.gitignore
+++ /dev/null
@@ -1,2 +0,0 @@
-target
-checkpoint
diff --git a/tensorflow-examples-legacy/training/README.md b/tensorflow-examples-legacy/training/README.md
deleted file mode 100644
index 29d77af..0000000
--- a/tensorflow-examples-legacy/training/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
-# Training models in Java
-
-Example of training a model (and saving and restoring checkpoints) using the
-TensorFlow Java API.
-
-## Quickstart
-
-1. Train for a few steps:
- ```
- mvn -q compile exec:java -Dexec.args="model/graph.pb checkpoint"
- ```
-
-2. Resume training from previous checkpoint and train some more:
- ```
- mvn -q exec:java -Dexec.args="model/graph.pb checkpoint"
- ```
-
-3. Delete checkpoint:
- ```
- rm -rf checkpoint
- ```
-
-
-## Details
-
-The model in `model/graph.pb` represents a very simple linear model:
-
-```
-y = x * W + b
-```
-
-The `graph.pb` file is generated by executing `create_graph.py` in Python.
-
-The training is orchestrated by `src/main/java/Train.java`, which generates
-training data of the form `y = 3.0 * x + 2.0` and over time, using gradient
-descent, the model should "learn" and the value of `W` should converge to 3.0,
-and `b` to 2.0.
diff --git a/tensorflow-examples-legacy/training/model/create_graph.py b/tensorflow-examples-legacy/training/model/create_graph.py
deleted file mode 100644
index 7e043a9..0000000
--- a/tensorflow-examples-legacy/training/model/create_graph.py
+++ /dev/null
@@ -1,36 +0,0 @@
-from __future__ import print_function
-
-import tensorflow as tf
-
-x = tf.placeholder(tf.float32, name='input')
-y_ = tf.placeholder(tf.float32, name='target')
-
-W = tf.Variable(5., name='W')
-b = tf.Variable(3., name='b')
-
-y = x * W + b
-y = tf.identity(y, name='output')
-
-loss = tf.reduce_mean(tf.square(y - y_))
-optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)
-train_op = optimizer.minimize(loss, name='train')
-
-init = tf.global_variables_initializer()
-
-# Creating a tf.train.Saver adds operations to the graph to save and
-# restore variables from checkpoints.
-saver_def = tf.train.Saver().as_saver_def()
-
-print('Operation to initialize variables: ', init.name)
-print('Tensor to feed as input data: ', x.name)
-print('Tensor to feed as training targets: ', y_.name)
-print('Tensor to fetch as prediction: ', y.name)
-print('Operation to train one step: ', train_op.name)
-print('Tensor to be fed for checkpoint filename:', saver_def.filename_tensor_name)
-print('Operation to save a checkpoint: ', saver_def.save_tensor_name)
-print('Operation to restore a checkpoint: ', saver_def.restore_op_name)
-print('Tensor to read value of W ', W.value().name)
-print('Tensor to read value of b ', b.value().name)
-
-with open('graph.pb', 'w') as f:
- f.write(tf.get_default_graph().as_graph_def().SerializeToString())
diff --git a/tensorflow-examples-legacy/training/model/graph.pb b/tensorflow-examples-legacy/training/model/graph.pb
deleted file mode 100644
index 51d946d..0000000
Binary files a/tensorflow-examples-legacy/training/model/graph.pb and /dev/null differ
diff --git a/tensorflow-examples-legacy/training/pom.xml b/tensorflow-examples-legacy/training/pom.xml
deleted file mode 100644
index 39dda07..0000000
--- a/tensorflow-examples-legacy/training/pom.xml
+++ /dev/null
@@ -1,20 +0,0 @@
-
- 4.0.0
- org.myorg
- training
- 1.0-SNAPSHOT
-
- Train
-
-
- 1.7
- 1.7
-
-
-
- org.tensorflow
- tensorflow
- 1.4.0
-
-
-
diff --git a/tensorflow-examples-legacy/training/src/main/java/Train.java b/tensorflow-examples-legacy/training/src/main/java/Train.java
deleted file mode 100644
index 57176a4..0000000
--- a/tensorflow-examples-legacy/training/src/main/java/Train.java
+++ /dev/null
@@ -1,98 +0,0 @@
-/* Copyright 2018 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.
-==============================================================================*/
-
-import java.nio.file.Files;
-import java.nio.file.Paths;
-import java.util.List;
-import java.util.Random;
-import org.tensorflow.Graph;
-import org.tensorflow.Session;
-import org.tensorflow.Tensor;
-import org.tensorflow.Tensors;
-
-/**
- * Training a trivial linear model.
- */
-public class Train {
- public static void main(String[] args) throws Exception {
- if (args.length != 2) {
- System.err.println("Require two arguments: The GraphDef file and checkpoint directory");
- System.exit(1);
- }
-
- final byte[] graphDef = Files.readAllBytes(Paths.get(args[0]));
- final String checkpointDir = args[1];
- final boolean checkpointExists = Files.exists(Paths.get(checkpointDir));
-
- try (Graph graph = new Graph();
- Session sess = new Session(graph);
- Tensor checkpointPrefix =
- Tensors.create(Paths.get(checkpointDir, "ckpt").toString())) {
- graph.importGraphDef(graphDef);
-
- // Initialize or restore.
- // The names of the tensors in the graph are printed out by the program
- // that created the graph:
- // https://github.com/tensorflow/models/blob/master/samples/languages/java/training/model/create_graph.py
- if (checkpointExists) {
- sess.runner().feed("save/Const", checkpointPrefix).addTarget("save/restore_all").run();
- } else {
- sess.runner().addTarget("init").run();
- }
- System.out.print("Starting from : ");
- printVariables(sess);
-
- // Train a bunch of times.
- // (Will be much more efficient if we sent batches instead of individual values).
- final Random r = new Random();
- final int NUM_EXAMPLES = 500;
- for (int i = 1; i <= 5; i++) {
- for (int n = 0; n < NUM_EXAMPLES; n++) {
- float in = r.nextFloat();
- try (Tensor input = Tensors.create(in);
- Tensor target = Tensors.create(3 * in + 2)) {
- // Again the tensor names are from the program that created the graph.
- // https://github.com/tensorflow/models/blob/master/samples/languages/java/training/model/create_graph.py
- sess.runner().feed("input", input).feed("target", target).addTarget("train").run();
- }
- }
- System.out.printf("After %5d examples: ", i*NUM_EXAMPLES);
- printVariables(sess);
- }
-
- // Checkpoint.
- // The feed and target name are from the program that created the graph.
- // https://github.com/tensorflow/models/blob/master/samples/languages/java/training/model/create_graph.py.
- sess.runner().feed("save/Const", checkpointPrefix).addTarget("save/control_dependency").run();
-
- // Example of "inference" in the same graph:
- try (Tensor input = Tensors.create(1.0f);
- Tensor output =
- sess.runner().feed("input", input).fetch("output").run().get(0).expect(Float.class)) {
- System.out.printf(
- "For input %f, produced %f (ideally would produce 3*%f + 2)\n",
- input.floatValue(), output.floatValue(), input.floatValue());
- }
- }
- }
-
- private static void printVariables(Session sess) {
- List> values = sess.runner().fetch("W/read").fetch("b/read").run();
- System.out.printf("W = %f\tb = %f\n", values.get(0).floatValue(), values.get(1).floatValue());
- for (Tensor t : values) {
- t.close();
- }
- }
-}