FIX: forgotten imports (#9113)

Signed-off-by: hosuaby <alexei.klenin@gmail.com>

arbiter/arbiter-deeplearning4j/src/main/java/org/deeplearning4j/arbiter/BaseNetworkSpace.java

@@ -31,6 +31,11 @@ import org.deeplearning4j.arbiter.optimize.serde.jackson.YamlMapper;
 import org.deeplearning4j.earlystopping.EarlyStoppingConfiguration;
 import org.deeplearning4j.nn.api.OptimizationAlgorithm;
 import org.deeplearning4j.nn.api.layers.LayerConstraint;
+import org.deeplearning4j.nn.conf.BackpropType;
+import org.deeplearning4j.nn.conf.ConvolutionMode;
+import org.deeplearning4j.nn.conf.GradientNormalization;
+import org.deeplearning4j.nn.conf.InputPreProcessor;
+import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 import org.deeplearning4j.nn.conf.distribution.Distribution;
 import org.deeplearning4j.nn.conf.dropout.Dropout;
 import org.deeplearning4j.nn.conf.dropout.IDropout;
@@ -43,7 +48,11 @@ import org.nd4j.linalg.learning.config.IUpdater;
 import org.nd4j.shade.jackson.annotation.JsonTypeInfo;
 import org.nd4j.shade.jackson.core.JsonProcessingException;
 
-import java.util.*;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.LinkedList;
+import java.util.List;
+import java.util.Map;
 
 /**
  * This is an abstract ParameterSpace for both MultiLayerNetworks (MultiLayerSpace) and ComputationGraph (ComputationGraphSpace)

arbiter/arbiter-deeplearning4j/src/test/java/org/deeplearning4j/arbiter/multilayernetwork/TestLayerSpace.java

@@ -16,11 +16,21 @@
 
 package org.deeplearning4j.arbiter.multilayernetwork;
 
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertFalse;
+import static org.junit.Assert.assertTrue;
+
 import org.apache.commons.lang3.ArrayUtils;
 import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.arbiter.TestUtils;
 import org.deeplearning4j.arbiter.conf.updater.SgdSpace;
-import org.deeplearning4j.arbiter.layers.*;
+import org.deeplearning4j.arbiter.layers.ActivationLayerSpace;
+import org.deeplearning4j.arbiter.layers.BatchNormalizationSpace;
+import org.deeplearning4j.arbiter.layers.ConvolutionLayerSpace;
+import org.deeplearning4j.arbiter.layers.Deconvolution2DLayerSpace;
+import org.deeplearning4j.arbiter.layers.DenseLayerSpace;
+import org.deeplearning4j.arbiter.layers.EmbeddingLayerSpace;
+import org.deeplearning4j.arbiter.layers.GravesBidirectionalLSTMLayerSpace;
 import org.deeplearning4j.arbiter.optimize.api.ParameterSpace;
 import org.deeplearning4j.arbiter.optimize.parameter.BooleanSpace;
 import org.deeplearning4j.arbiter.optimize.parameter.continuous.ContinuousParameterSpace;
@@ -31,6 +41,14 @@ import org.deeplearning4j.nn.conf.constraint.MaxNormConstraint;
 import org.deeplearning4j.nn.conf.constraint.MinMaxNormConstraint;
 import org.deeplearning4j.nn.conf.constraint.NonNegativeConstraint;
 import org.deeplearning4j.nn.conf.constraint.UnitNormConstraint;
+import org.deeplearning4j.nn.conf.layers.ActivationLayer;
+import org.deeplearning4j.nn.conf.layers.BatchNormalization;
+import org.deeplearning4j.nn.conf.layers.Convolution2D;
+import org.deeplearning4j.nn.conf.layers.ConvolutionLayer;
+import org.deeplearning4j.nn.conf.layers.Deconvolution2D;
+import org.deeplearning4j.nn.conf.layers.DenseLayer;
+import org.deeplearning4j.nn.conf.layers.EmbeddingLayer;
+import org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM;
 import org.junit.Test;
 import org.nd4j.linalg.activations.Activation;
 import org.nd4j.linalg.activations.IActivation;
@@ -41,13 +59,10 @@ import java.util.Collections;
 import java.util.List;
 import java.util.Random;
 
-import static org.junit.Assert.*;
-
 public class TestLayerSpace extends BaseDL4JTest {
 
     @Test
     public void testBasic1() {
-
         DenseLayer expected = new DenseLayer.Builder().nOut(13).activation(Activation.RELU).build();
 
         DenseLayerSpace space = new DenseLayerSpace.Builder().nOut(13).activation(Activation.RELU).build();
@@ -188,8 +203,6 @@ public class TestLayerSpace extends BaseDL4JTest {
             ActivationLayer al = als.getValue(d);
             IActivation activation = al.getActivationFn();
 
-//            System.out.println(activation);
-
             assertTrue(containsActivationFunction(actFns, activation));
         }
     }
@@ -226,8 +239,6 @@ public class TestLayerSpace extends BaseDL4JTest {
             IActivation activation = el.getActivationFn();
             long nOut = el.getNOut();
 
-//            System.out.println(activation + "\t" + nOut);
-
             assertTrue(containsActivationFunction(actFns, activation));
             assertTrue(nOut >= 10 && nOut <= 20);
         }
@@ -293,8 +304,6 @@ public class TestLayerSpace extends BaseDL4JTest {
             long nOut = el.getNOut();
             double forgetGate = el.getForgetGateBiasInit();
 
-//            System.out.println(activation + "\t" + nOut + "\t" + forgetGate);
-
             assertTrue(containsActivationFunction(actFns, activation));
             assertTrue(nOut >= 10 && nOut <= 20);
             assertTrue(forgetGate >= 0.5 && forgetGate <= 0.8);

arbiter/arbiter-deeplearning4j/src/test/java/org/deeplearning4j/arbiter/multilayernetwork/TestMultiLayerSpace.java

@@ -56,6 +56,16 @@ import org.deeplearning4j.nn.conf.constraint.UnitNormConstraint;
 import org.deeplearning4j.nn.conf.dropout.Dropout;
 import org.deeplearning4j.nn.conf.dropout.IDropout;
 import org.deeplearning4j.nn.conf.inputs.InputType;
+import org.deeplearning4j.nn.conf.layers.BaseLayer;
+import org.deeplearning4j.nn.conf.layers.ConvolutionLayer;
+import org.deeplearning4j.nn.conf.layers.DenseLayer;
+import org.deeplearning4j.nn.conf.layers.FeedForwardLayer;
+import org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer;
+import org.deeplearning4j.nn.conf.layers.GravesLSTM;
+import org.deeplearning4j.nn.conf.layers.LSTM;
+import org.deeplearning4j.nn.conf.layers.OutputLayer;
+import org.deeplearning4j.nn.conf.layers.PoolingType;
+import org.deeplearning4j.nn.conf.layers.SubsamplingLayer;
 import org.deeplearning4j.nn.conf.layers.variational.BernoulliReconstructionDistribution;
 import org.deeplearning4j.nn.conf.layers.variational.GaussianReconstructionDistribution;
 import org.deeplearning4j.nn.conf.layers.variational.ReconstructionDistribution;
@@ -809,7 +819,6 @@ public class TestMultiLayerSpace extends BaseDL4JTest {
             Candidate<DL4JConfiguration> conf = candidateGenerator.getCandidate();
             MultiLayerConfiguration mlc = conf.getValue().getMultiLayerConfiguration();
             FeedForwardLayer ffl = ((FeedForwardLayer) mlc.getConf(0).getLayer());
-//            System.out.println(ffl.getIUpdater() + ", " + ffl.getNOut());
             actCandidates.add(new Pair<>(ffl.getIUpdater().getLearningRate(0,0), (int)ffl.getNOut()));
         }
 

rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/learning/async/a3c/discrete/A3CThreadDiscrete.java

@@ -27,6 +27,7 @@ import org.deeplearning4j.rl4j.space.Encodable;
 import org.deeplearning4j.rl4j.policy.ACPolicy;
 import org.deeplearning4j.rl4j.policy.Policy;
 import org.deeplearning4j.rl4j.space.DiscreteSpace;
+import org.nd4j.linalg.api.rng.Random;
 import org.nd4j.linalg.factory.Nd4j;
 
 /**