SameDiff: Listener changes and training api update (#99)

* example api Signed-off-by: Ryan Nett <rnett@skymind.io> * Lambda based evaluation Signed-off-by: Ryan Nett <rnett@skymind.io> * lambda test Signed-off-by: Ryan Nett <rnett@skymind.io> * fixes Signed-off-by: Ryan Nett <rnett@skymind.io> * partial fixes, use get-variable listener framework, example EvaluationListener Signed-off-by: Ryan Nett <rnett@skymind.io> * javadoc fix and newInstance implementations Signed-off-by: Ryan Nett <rnett@skymind.io> * fit and evaluate methods with validation data (for fit) and listeners Signed-off-by: Ryan Nett <rnett@skymind.io> * output method overloads + listener args Signed-off-by: Ryan Nett <rnett@skymind.io> * history and evaluation helpers Signed-off-by: Ryan Nett <rnett@skymind.io> * fixes Signed-off-by: Ryan Nett <rnett@skymind.io> * more fixes Signed-off-by: Ryan Nett <rnett@skymind.io> * FitConfig and added getters and setters Signed-off-by: Ryan Nett <rnett@skymind.io> * javadocs Signed-off-by: Ryan Nett <rnett@skymind.io> * fixes, javadoc, added activations to history, added latest activation listener Signed-off-by: Ryan Nett <rnett@skymind.io> * fixes, start of tests Signed-off-by: Ryan Nett <rnett@skymind.io> * fixes and updates Signed-off-by: Ryan Nett <rnett@skymind.io> * newInstance fixes, tests Signed-off-by: Ryan Nett <rnett@skymind.io> * test fixes Signed-off-by: Ryan Nett <rnett@skymind.io> * javadocs, getters with SDVariable overrides, CustomEvaluation fix Signed-off-by: Ryan Nett <rnett@skymind.io> * more operation config classes (evaluation, output, exec/single batch output), fix custom eval tests Signed-off-by: Ryan Nett <rnett@skymind.io> * merge fixes Signed-off-by: Ryan Nett <rnett@skymind.io> * fix Signed-off-by: Ryan Nett <rnett@skymind.io> * fixes, most old fit/evaluate/output methods use the builders Signed-off-by: Ryan Nett <rnett@skymind.io> * fixes Signed-off-by: Ryan Nett <rnett@skymind.io> * numerous fixes/cleanup Signed-off-by: Ryan Nett <rnett@skymind.io> * fixes Signed-off-by: Ryan Nett <rnett@skymind.io> * javadoc Signed-off-by: Ryan Nett <rnett@skymind.io> * Polish round 1 Signed-off-by: AlexDBlack <blacka101@gmail.com> * Round 2 Signed-off-by: AlexDBlack <blacka101@gmail.com> * Formatting + round 3 Signed-off-by: AlexDBlack <blacka101@gmail.com> * Round 4 Signed-off-by: AlexDBlack <blacka101@gmail.com>
deeplearning4j · Aug 10, 2019 · 11bddb3 · 11bddb3
1 parent 6ed0321
commit 11bddb3
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diff --git a/...ning4j/src/main/java/org/deeplearning4j/arbiter/scoring/impl/RegressionScoreFunction.java b/...ning4j/src/main/java/org/deeplearning4j/arbiter/scoring/impl/RegressionScoreFunction.java
@@ -21,12 +21,13 @@
 import org.deeplearning4j.nn.graph.ComputationGraph;
 import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
 import org.nd4j.evaluation.regression.RegressionEvaluation;
+import org.nd4j.evaluation.regression.RegressionEvaluation.Metric;
 import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
 import org.nd4j.linalg.dataset.api.iterator.MultiDataSetIterator;
 
 /**
  * Score function for regression (including multi-label regression) for a MultiLayerNetwork or ComputationGraph
- * on a test set. Supports all regression metrics: {@link RegressionEvaluation.Metric}
+ * on a test set. Supports all regression metrics: {@link Metric}
  *
  * @author Alex Black
  */
@@ -35,13 +36,13 @@
 @NoArgsConstructor(access = AccessLevel.PROTECTED)  //For JSON
 public class RegressionScoreFunction extends BaseNetScoreFunction {
 
-    protected RegressionEvaluation.Metric metric;
+    protected Metric metric;
 
     public RegressionScoreFunction(@NonNull org.deeplearning4j.eval.RegressionEvaluation.Metric metric) {
         this(metric.toNd4j());
     }
 
-    public RegressionScoreFunction(@NonNull RegressionEvaluation.Metric metric) {
+    public RegressionScoreFunction(@NonNull Metric metric) {
         this.metric = metric;
     }
 

diff --git a/...deeplearning4j-core/src/test/java/org/deeplearning4j/earlystopping/TestEarlyStopping.java b/...deeplearning4j-core/src/test/java/org/deeplearning4j/earlystopping/TestEarlyStopping.java
@@ -51,7 +51,7 @@
 import org.junit.rules.TemporaryFolder;
 import org.nd4j.evaluation.classification.Evaluation;
 import org.nd4j.evaluation.classification.ROCBinary;
-import org.nd4j.evaluation.regression.RegressionEvaluation;
+import org.nd4j.evaluation.regression.RegressionEvaluation.Metric;
 import org.nd4j.linalg.activations.Activation;
 import org.nd4j.linalg.api.buffer.DataType;
 import org.nd4j.linalg.api.ndarray.INDArray;
@@ -107,7 +107,7 @@ public void testEarlyStoppingIris() {
                     min = false;
                     break;
                 case 3:
-                    sc = new RegressionScoreCalculator(RegressionEvaluation.Metric.MSE, irisIter);
+                    sc = new RegressionScoreCalculator(Metric.MSE, irisIter);
                     min = true;
                     break;
                 case 4:
@@ -561,8 +561,8 @@ public void onCompletion(EarlyStoppingResult esResult) {
     @Test
     public void testRegressionScoreFunctionSimple() throws Exception {
 
-        for(RegressionEvaluation.Metric metric : new RegressionEvaluation.Metric[]{RegressionEvaluation.Metric.MSE,
-                RegressionEvaluation.Metric.MAE}) {
+        for(Metric metric : new Metric[]{Metric.MSE,
+                Metric.MAE}) {
             log.info("Metric: " + metric);
 
             MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
@@ -604,8 +604,8 @@ public void testRegressionScoreFunctionSimple() throws Exception {
     @Test
     public void testAEScoreFunctionSimple() throws Exception {
 
-        for(RegressionEvaluation.Metric metric : new RegressionEvaluation.Metric[]{RegressionEvaluation.Metric.MSE,
-                RegressionEvaluation.Metric.MAE}) {
+        for(Metric metric : new Metric[]{Metric.MSE,
+                Metric.MAE}) {
             log.info("Metric: " + metric);
 
             MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
@@ -647,8 +647,8 @@ public void testAEScoreFunctionSimple() throws Exception {
     @Test
     public void testVAEScoreFunctionSimple() throws Exception {
 
-        for(RegressionEvaluation.Metric metric : new RegressionEvaluation.Metric[]{RegressionEvaluation.Metric.MSE,
-                RegressionEvaluation.Metric.MAE}) {
+        for(Metric metric : new Metric[]{Metric.MSE,
+                Metric.MAE}) {
             log.info("Metric: " + metric);
 
             MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()

diff --git a/...ing4j-core/src/test/java/org/deeplearning4j/earlystopping/TestEarlyStoppingCompGraph.java b/...ing4j-core/src/test/java/org/deeplearning4j/earlystopping/TestEarlyStoppingCompGraph.java
@@ -43,7 +43,7 @@
 import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
 import org.junit.Test;
 import org.nd4j.evaluation.classification.Evaluation;
-import org.nd4j.evaluation.regression.RegressionEvaluation;
+import org.nd4j.evaluation.regression.RegressionEvaluation.Metric;
 import org.nd4j.linalg.activations.Activation;
 import org.nd4j.linalg.api.buffer.DataType;
 import org.nd4j.linalg.dataset.DataSet;
@@ -289,8 +289,8 @@ public void onCompletion(EarlyStoppingResult esResult) {
     @Test
     public void testRegressionScoreFunctionSimple() throws Exception {
 
-        for(RegressionEvaluation.Metric metric : new RegressionEvaluation.Metric[]{RegressionEvaluation.Metric.MSE,
-                RegressionEvaluation.Metric.MAE}) {
+        for(Metric metric : new Metric[]{Metric.MSE,
+                Metric.MAE}) {
             log.info("Metric: " + metric);
 
             ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder()
@@ -335,8 +335,8 @@ public void testRegressionScoreFunctionSimple() throws Exception {
     public void testAEScoreFunctionSimple() throws Exception {
         DataType dt = Nd4j.defaultFloatingPointType();
 
-        for(RegressionEvaluation.Metric metric : new RegressionEvaluation.Metric[]{RegressionEvaluation.Metric.MSE,
-                RegressionEvaluation.Metric.MAE}) {
+        for(Metric metric : new Metric[]{Metric.MSE,
+                Metric.MAE}) {
             log.info("Metric: " + metric);
 
             ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder()
@@ -380,8 +380,8 @@ public void testAEScoreFunctionSimple() throws Exception {
     @Test
     public void testVAEScoreFunctionSimple() throws Exception {
 
-        for(RegressionEvaluation.Metric metric : new RegressionEvaluation.Metric[]{RegressionEvaluation.Metric.MSE,
-                RegressionEvaluation.Metric.MAE}) {
+        for(Metric metric : new Metric[]{Metric.MSE,
+                Metric.MAE}) {
             log.info("Metric: " + metric);
 
             ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder()

diff --git a/.../src/main/java/org/deeplearning4j/earlystopping/scorecalc/AutoencoderScoreCalculator.java b/.../src/main/java/org/deeplearning4j/earlystopping/scorecalc/AutoencoderScoreCalculator.java
@@ -23,23 +23,24 @@
 import org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder;
 import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
 import org.nd4j.evaluation.regression.RegressionEvaluation;
+import org.nd4j.evaluation.regression.RegressionEvaluation.Metric;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
 import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
 
 /**
  * Score function for a MultiLayerNetwork or ComputationGraph with a single
  * {@link org.deeplearning4j.nn.conf.layers.AutoEncoder} layer.
- * Calculates the specified {@link RegressionEvaluation.Metric} on the layer's reconstructions.
+ * Calculates the specified {@link Metric} on the layer's reconstructions.
  *
  * @author Alex Black
  */
 public class AutoencoderScoreCalculator extends BaseScoreCalculator<Model> {
 
-    protected final RegressionEvaluation.Metric metric;
+    protected final Metric metric;
     protected RegressionEvaluation evaluation;
 
-    public AutoencoderScoreCalculator(RegressionEvaluation.Metric metric, DataSetIterator iterator){
+    public AutoencoderScoreCalculator(Metric metric, DataSetIterator iterator){
         super(iterator);
         this.metric = metric;
     }

diff --git a/...n/src/main/java/org/deeplearning4j/earlystopping/scorecalc/RegressionScoreCalculator.java b/...n/src/main/java/org/deeplearning4j/earlystopping/scorecalc/RegressionScoreCalculator.java
@@ -19,19 +19,20 @@
 import org.deeplearning4j.earlystopping.scorecalc.base.BaseIEvaluationScoreCalculator;
 import org.deeplearning4j.nn.api.Model;
 import org.nd4j.evaluation.regression.RegressionEvaluation;
+import org.nd4j.evaluation.regression.RegressionEvaluation.Metric;
 import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
 
 /**
  * Calculate the regression score of the network (MultiLayerNetwork or ComputationGraph) on a test set, using the
- * specified regression metric - {@link RegressionEvaluation.Metric}
+ * specified regression metric - {@link Metric}
  *
  * @author Alex Black
  */
 public class RegressionScoreCalculator extends BaseIEvaluationScoreCalculator<Model, RegressionEvaluation> {
 
-    protected final RegressionEvaluation.Metric metric;
+    protected final Metric metric;
 
-    public RegressionScoreCalculator(RegressionEvaluation.Metric metric, DataSetIterator iterator){
+    public RegressionScoreCalculator(Metric metric, DataSetIterator iterator){
         super(iterator);
         this.metric = metric;
     }

diff --git a/...rc/main/java/org/deeplearning4j/earlystopping/scorecalc/VAEReconErrorScoreCalculator.java b/...rc/main/java/org/deeplearning4j/earlystopping/scorecalc/VAEReconErrorScoreCalculator.java
@@ -23,6 +23,7 @@
 import org.deeplearning4j.nn.layers.variational.VariationalAutoencoder;
 import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
 import org.nd4j.evaluation.regression.RegressionEvaluation;
+import org.nd4j.evaluation.regression.RegressionEvaluation.Metric;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
 import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
@@ -35,7 +36,7 @@
  */
 public class VAEReconErrorScoreCalculator extends BaseScoreCalculator<Model> {
 
-    protected final RegressionEvaluation.Metric metric;
+    protected final Metric metric;
     protected RegressionEvaluation evaluation;
 
     /**
@@ -44,7 +45,7 @@ public class VAEReconErrorScoreCalculator extends BaseScoreCalculator<Model> {
      * @param metric
      * @param iterator
      */
-    public VAEReconErrorScoreCalculator(RegressionEvaluation.Metric metric, DataSetIterator iterator) {
+    public VAEReconErrorScoreCalculator(Metric metric, DataSetIterator iterator) {
         super(iterator);
         this.metric = metric;
     }

diff --git a/.../nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/At.java b/.../nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/At.java
@@ -20,6 +20,22 @@ public class At {
     private int iteration;
     private int trainingThreadNum;
     private long javaThreadNum;
+    private Operation operation;
+
+    /**
+     * @return A new instance with everything set to 0, and operation set to INFERENCE
+     */
+    public static At defaultAt(){
+        return new At(0, 0, 0, 0, Operation.INFERENCE);
+    }
+
+    /**
+     * @param op Operation
+     * @return A new instance with everything set to 0, except for the specified operation
+     */
+    public static At defaultAt(@NonNull Operation op){
+        return new At(0, 0, 0, 0, op);
+    }
 
     /**
      * @return The current training epoch
@@ -48,4 +64,26 @@ public int trainingThreadNum(){
     public long javaThreadNum(){
         return javaThreadNum;
     }
+
+    /**
+     * @return The current operation
+     */
+    public Operation operation(){
+        return operation;
+    }
+
+    /**
+     * @return A copy of the current At instance
+     */
+    public At copy(){
+        return new At(epoch, iteration, trainingThreadNum, javaThreadNum, operation);
+    }
+
+    /**
+     * @param operation Operation to set in the new instance
+     * @return A copy of the current instance, but with the specified operation
+     */
+    public At copy(Operation operation){
+        return new At(epoch, iteration, trainingThreadNum, javaThreadNum, operation);
+    }
 }