Merge branch 'master' into patch-1

samples/languages/java/training/.gitignore

+target
+checkpoint

samples/languages/java/training/README.md

+# 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.

samples/languages/java/training/model/create_graph.py

+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())

samples/languages/java/training/pom.xml

+<project>
+    <modelVersion>4.0.0</modelVersion>
+    <groupId>org.myorg</groupId>
+    <artifactId>training</artifactId>
+    <version>1.0-SNAPSHOT</version>
+    <properties>
+      <exec.mainClass>Train</exec.mainClass>
+      <!-- The sample code requires at least JDK 1.7. -->
+      <!-- The maven compiler plugin defaults to a lower version -->
+      <maven.compiler.source>1.7</maven.compiler.source>
+      <maven.compiler.target>1.7</maven.compiler.target>
+    </properties>
+    <dependencies>
+      <dependency>
+        <groupId>org.tensorflow</groupId>
+        <artifactId>tensorflow</artifactId>
+        <version>1.4.0</version>
+      </dependency>
+    </dependencies>
+</project>

samples/languages/java/training/src/main/java/Train.java

+/* 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<String> checkpointPrefix =
+            Tensors.create(Paths.get(checkpointDir, "ckpt").toString())) {
+      graph.importGraphDef(graphDef);
+
+      // Initialize or restore
+      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<Float> input = Tensors.create(in);
+              Tensor<Float> target = Tensors.create(3 * in + 2)) {
+            sess.runner().feed("input", input).feed("target", target).addTarget("train").run();
+          }
+        }
+        System.out.printf("After %5d examples: ", i*NUM_EXAMPLES);
+        printVariables(sess);
+      }
+
+      // Checkpoint
+      sess.runner().feed("save/Const", checkpointPrefix).addTarget("save/control_dependency").run();
+
+      // Example of "inference" in the same graph:
+      try (Tensor<Float> input = Tensors.create(1.0f);
+          Tensor<Float> 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<Tensor<?>> 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();
+    }
+  }
+}

tutorials/embedding/README.md

@@ -19,17 +19,19 @@ unzip -p source-archive.zip  word2vec/trunk/questions-words.txt > questions-word
 rm text8.zip source-archive.zip
 ```
 
-You will need to compile the ops as follows:
+You will need to compile the ops as follows (See 
+[Adding a New Op to TensorFlow](https://www.tensorflow.org/how_tos/adding_an_op/#building_the_op_library)
+for more details).:
 
 ```shell
-TF_INC=$(python -c 'import tensorflow as tf; print(tf.sysconfig.get_include())')
-g++ -std=c++11 -shared word2vec_ops.cc word2vec_kernels.cc -o word2vec_ops.so -fPIC -I $TF_INC -O2 -D_GLIBCXX_USE_CXX11_ABI=0
+TF_CFLAGS=( $(python -c 'import tensorflow as tf; print(" ".join(tf.sysconfig.get_compile_flags()))') )
+TF_LFLAGS=( $(python -c 'import tensorflow as tf; print(" ".join(tf.sysconfig.get_link_flags()))') )
+g++ -std=c++11 -shared word2vec_ops.cc word2vec_kernels.cc -o word2vec_ops.so -fPIC ${TF_CFLAGS[@]} ${TF_LFLAGS[@]} -O2 -D_GLIBCXX_USE_CXX11_ABI=0
 ```
 
-On Mac, add `-undefined dynamic_lookup` to the g++ command.
+On Mac, add `-undefined dynamic_lookup` to the g++ command. The flag `-D_GLIBCXX_USE_CXX11_ABI=0` is included to support newer versions of gcc. However, if you compiled TensorFlow from source using gcc 5 or later, you may need to exclude the flag. Specifically, if you get an error similar to the following: `word2vec_ops.so: undefined symbol: _ZN10tensorflow7strings6StrCatERKNS0_8AlphaNumES3_S3_S3_` then you likely need to exclude the flag.
 
-(For an explanation of what this is doing, see the tutorial on [Adding a New Op to TensorFlow](https://www.tensorflow.org/how_tos/adding_an_op/#building_the_op_library). The flag `-D_GLIBCXX_USE_CXX11_ABI=0` is included to support newer versions of gcc. However, if you compiled TensorFlow from source using gcc 5 or later, you may need to exclude the flag.)
-Then run using:
+Once you've successfully compiled the ops, run the model as follows:
 
 ```shell
 python word2vec_optimized.py \