mnist fix

Author: ivan-vasilev

nn-samples/src/test/java/com/github/neuralnetworks/samples/test/MnistTest.java

@@ -7,19 +7,15 @@
 
 import com.amd.aparapi.Kernel.EXECUTION_MODE;
 import com.github.neuralnetworks.architecture.NeuralNetworkImpl;
-import com.github.neuralnetworks.architecture.types.Autoencoder;
 import com.github.neuralnetworks.architecture.types.NNFactory;
-import com.github.neuralnetworks.architecture.types.RBM;
 import com.github.neuralnetworks.input.MultipleNeuronsOutputError;
 import com.github.neuralnetworks.input.ScalingInputFunction;
 import com.github.neuralnetworks.samples.mnist.MnistInputProvider;
-import com.github.neuralnetworks.training.Trainer;
 import com.github.neuralnetworks.training.TrainerFactory;
 import com.github.neuralnetworks.training.backpropagation.BackPropagationTrainer;
 import com.github.neuralnetworks.training.events.LogTrainingListener;
 import com.github.neuralnetworks.training.random.MersenneTwisterRandomInitializer;
 import com.github.neuralnetworks.training.random.NNRandomInitializer;
-import com.github.neuralnetworks.training.rbm.AparapiCDTrainer;
 import com.github.neuralnetworks.util.Environment;
 
 /**
@@ -72,44 +68,6 @@ public void testSigmoidHiddenBP() {
 	assertEquals(0, bpt.getOutputError().getTotalNetworkError(), 0.1);
     }
 
-    @Test
-    public void testRBM() {
-	RBM rbm = NNFactory.rbm(784, 10, false);
-	MnistInputProvider trainInputProvider = new MnistInputProvider("train-images.idx3-ubyte", "train-labels.idx1-ubyte");
-	trainInputProvider.addInputModifier(new ScalingInputFunction(255));
-	MnistInputProvider testInputProvider = new MnistInputProvider("t10k-images.idx3-ubyte", "t10k-labels.idx1-ubyte");
-	testInputProvider.addInputModifier(new ScalingInputFunction(255));
-
-	AparapiCDTrainer t = TrainerFactory.cdSigmoidBinaryTrainer(rbm, trainInputProvider, testInputProvider,  new MultipleNeuronsOutputError(), new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f)), 0.01f, 0.5f, 0f, 0f, 1, 1, 1, false);
-
-	t.addEventListener(new LogTrainingListener(Thread.currentThread().getStackTrace()[1].getMethodName(), false, true));
-	Environment.getInstance().setExecutionMode(EXECUTION_MODE.CPU);
-	t.train();
-	t.test();
-
-	assertEquals(0, t.getOutputError().getTotalNetworkError(), 0.8);
-    }
-
-    @Test
-    public void testAE() {
-	Autoencoder nn = NNFactory.autoencoderSigmoid(784, 10, true);
-
-	MnistInputProvider trainInputProvider = new MnistInputProvider("train-images.idx3-ubyte", "train-labels.idx1-ubyte");
-	trainInputProvider.addInputModifier(new ScalingInputFunction(255));
-	MnistInputProvider testInputProvider = new MnistInputProvider("t10k-images.idx3-ubyte", "t10k-labels.idx1-ubyte");
-	testInputProvider.addInputModifier(new ScalingInputFunction(255));
-
-	Trainer<?> t = TrainerFactory.backPropagationAutoencoder(nn, trainInputProvider, testInputProvider,  new MultipleNeuronsOutputError(), new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f)), 0.01f, 0.5f, 0f, 0f, 0f, 1, 1000, 1);
-
-	t.addEventListener(new LogTrainingListener(Thread.currentThread().getStackTrace()[1].getMethodName(), false, true));
-	Environment.getInstance().setExecutionMode(EXECUTION_MODE.CPU);
-	t.train();
-	nn.removeLayer(nn.getOutputLayer());
-	t.test();
-
-	assertEquals(0, t.getOutputError().getTotalNetworkError(), 0.1);
-    }
-
     @Test
     public void testLeNetSmall() {
 	// cpu execution mode
@@ -180,6 +138,9 @@ public void testLeNetTiny() {
      */
     @Test
     public void testLeNetTiny2() {
+	Environment.getInstance().setUseDataSharedMemory(false);
+	Environment.getInstance().setUseWeightsSharedMemory(false);
+
 	// very simple convolutional network with a single convolutional layer with 6 5x5 filters and a single 2x2 max pooling layer
 	NeuralNetworkImpl nn = NNFactory.convNN(new int[][] { { 28, 28, 1 }, { 5, 5, 6, 1 }, {2, 2}, {10} }, true);
 	nn.setLayerCalculator(NNFactory.lcSigmoid(nn, null));