Package com.github.neuralnetworks.tensor.Tensor

Examples of com.github.neuralnetworks.tensor.Tensor.TensorIterator

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  for (int i = 0; i < batchSize; i++) {
      beforeSample();

      if (ti.getInput() != null) {
    inputLimits[0][inputDims.length - 1] = inputLimits[1][inputDims.length - 1] = i;
    TensorIterator inputIt = ti.getInput().iterator(inputLimits);
    float[] inputEl = getNextInput();
    IntStream.range(0, inputEl.length).forEach(j -> ti.getInput().getElements()[inputIt.next()] = inputEl[j]);
      }

      if (ti.getTarget() != null) {
    targetLimits[0][targetDims.length - 1] = targetLimits[1][targetDims.length - 1] = i;
    TensorIterator targetIt = ti.getTarget().iterator(targetLimits);
    float[] targetEl = getNextTarget();
    IntStream.range(0, targetEl.length).forEach(j -> ti.getTarget().getElements()[targetIt.next()] = targetEl[j]);
      }

      afterSample();
  }
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    biasValue.forEach(i -> biasValue.getElements()[i] = 1);
      }

      Tensor v = TensorFactory.tensor(bias.getOutputLayer(), bias, vp);
      Tensor w = bias.getWeights();
      TensorIterator it = v.iterator();

      while (it.hasNext()) {
    v.getElements()[it.next()] = w.get(it.getCurrentPosition()[0], 0, 0, 0);
      }
  }
    }
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  Conv2DConnection c = new ConnectionFactory().conv2d(new Layer(), new Layer(), 3, 3, 2, 2, 2, 1, 1);

  c.getWeights().setElements(new float[] {1, 2, 3, 4, 1, 2, 3, 4});

  ValuesProvider vp = TensorFactory.tensorProvider(c, 1, true);
  TensorIterator it = vp.get(c.getInputLayer()).iterator();
  for (int i = 0; i < vp.get(c.getInputLayer()).getSize(); i++) {
      vp.get(c.getInputLayer()).getElements()[it.next()] = i + 1;
  }

  AparapiConv2D conv = new AparapiConv2DFF(c, vp, c.getOutputLayer());
  conv.calculate(c, vp, c.getOutputLayer());
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  Environment.getInstance().setUseWeightsSharedMemory(true);
  Conv2DConnection c = new ConnectionFactory().conv2d(new Layer(), new Layer(), 3, 3, 2, 2, 2, 2, 1);
  c.getWeights().setElements(new float[] {1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4});

  ValuesProvider vp = TensorFactory.tensorProvider(c, 1, true);
  TensorIterator it = vp.get(c.getInputLayer()).iterator();
  for (int i = 0; i < vp.get(c.getInputLayer()).getSize(); i++) {
      vp.get(c.getInputLayer()).getElements()[it.next()] = i + 1;
  }

  AparapiConv2D conv = new AparapiConv2DFF(c, vp, c.getOutputLayer());
  conv.calculate(c, vp, c.getOutputLayer());
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  Conv2DConnection c = (Conv2DConnection) nn.getInputLayer().getConnections().get(0);
  c.getWeights().setElements(new float[] {1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4});

  ValuesProvider vp = TensorFactory.tensorProvider(nn, 1, true);
  TensorIterator it = vp.get(c.getInputLayer()).iterator();
  for (int i = 0; i < vp.get(c.getInputLayer()).getSize(); i++) {
      vp.get(c.getInputLayer()).getElements()[it.next()] = i + 1;
  }

  Set<Layer> calculatedLayers = new HashSet<>();
  calculatedLayers.add(nn.getInputLayer());
  nn.getLayerCalculator().calculate(nn, nn.getOutputLayer(), calculatedLayers, vp);
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  Environment.getInstance().setUseWeightsSharedMemory(true);
  NeuralNetworkImpl nn = NNFactory.convNN(new int[][] { { 3, 3, 2 }, { 2, 2, 1, 1 } }, true);
  nn.setLayerCalculator(NNFactory.lcSigmoid(nn, null));

  Conv2DConnection c = (Conv2DConnection) nn.getInputLayer().getConnections().get(0);
  TensorIterator it = c.getWeights().iterator();
  float x = 0.1f;
  while (it.hasNext()) {
      c.getWeights().getElements()[it.next()] = x;
      x += 0.1f;
  }

  Conv2DConnection b = (Conv2DConnection) nn.getOutputLayer().getConnections().get(1);
  b.getWeights().getElements()[b.getWeights().getStartIndex()] = -3f;

  SimpleInputProvider ts = new SimpleInputProvider(new float[][] { { 0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.6f, 0.7f, 0.8f, 0.9f, 1, 1.1f, 1.2f, 1.3f, 1.4f, 1.5f, 1.6f, 1.7f, 1.8f } }, new float[][] { { 1, 1, 1, 1 } });
  BackPropagationTrainer<?> t = TrainerFactory.backPropagation(nn, ts, null, null, null, 0.5f, 0f, 0f, 0f, 0f, 1, 1, 1);
  t.train();

  it = c.getWeights().iterator();
  assertEquals(0.11756, c.getWeights().getElements()[it.next()], 0.00001);
  assertEquals(0.22640, c.getWeights().getElements()[it.next()], 0.00001);
  assertEquals(0.34408, c.getWeights().getElements()[it.next()], 0.00001);
  assertEquals(0.45292, c.getWeights().getElements()[it.next()], 0.00001);
  assertEquals(0.59712, c.getWeights().getElements()[it.next()], 0.00001);
  assertEquals(0.70596, c.getWeights().getElements()[it.next()], 0.00001);
  assertEquals(0.82364, c.getWeights().getElements()[it.next()], 0.00001);
  assertEquals(0.93248, c.getWeights().getElements()[it.next()], 0.00001);
  assertEquals(-2.911599, b.getWeights().getElements()[b.getWeights().getStartIndex()], 0.00001);
    }
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  Environment.getInstance().setUseWeightsSharedMemory(true);
  NeuralNetworkImpl nn = NNFactory.convNN(new int[][] { { 3, 3, 2 }, { 2, 2, 1, 1 } }, true);
  nn.setLayerCalculator(NNFactory.lcSigmoid(nn, null));

  Conv2DConnection c = (Conv2DConnection) nn.getInputLayer().getConnections().get(0);
  TensorIterator it = c.getWeights().iterator();
  float x = 0.1f;
  while (it.hasNext()) {
      c.getWeights().getElements()[it.next()] = x;
      x += 0.1f;
  }

  Conv2DConnection b = (Conv2DConnection) nn.getOutputLayer().getConnections().get(1);
  b.getWeights().getElements()[b.getWeights().getStartIndex()] = -3f;
 
  SimpleInputProvider ts = new SimpleInputProvider(new float[][] { { 0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.6f, 0.7f, 0.8f, 0.9f, 1, 1.1f, 1.2f, 1.3f, 1.4f, 1.5f, 1.6f, 1.7f, 1.8f }, { 0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.6f, 0.7f, 0.8f, 0.9f, 1, 1.1f, 1.2f, 1.3f, 1.4f, 1.5f, 1.6f, 1.7f, 1.8f } }, new float[][] { { 1, 1, 1, 1 }, { 1, 1, 1, 1 } });
  BackPropagationTrainer<?> t = TrainerFactory.backPropagation(nn, ts, null, null, null, 0.5f, 0f, 0f, 0f, 0f, 1, 1, 1);
  t.train();

  it = c.getWeights().iterator();
  assertEquals(0.12317, c.getWeights().getElements()[it.next()], 0.00001);
  assertEquals(0.23533, c.getWeights().getElements()[it.next()], 0.00001);
  assertEquals(0.35966, c.getWeights().getElements()[it.next()], 0.00001);
  assertEquals(0.47182, c.getWeights().getElements()[it.next()], 0.00001);
  assertEquals(0.63263, c.getWeights().getElements()[it.next()], 0.00001);
  assertEquals(0.74479, c.getWeights().getElements()[it.next()], 0.00001);
  assertEquals(0.86911, c.getWeights().getElements()[it.next()], 0.00001);
  assertEquals(0.98127, c.getWeights().getElements()[it.next()], 0.00001);
  assertEquals(-2.87839, b.getWeights().getElements()[b.getWeights().getStartIndex()], 0.00001);
    }
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    public static Matrix matrix(float[] elements, int columns) {
  return tensor(elements, 0, elements.length / columns, columns);
    }

    public static void fill(Tensor t, float value) {
  TensorIterator it = t.iterator();
  while (it.hasNext()) {
      t.getElements()[it.next()] = value;
  }
    }
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    public static void copy(Tensor src, Tensor dest) {
  if (!Arrays.equals(src.getDimensions(), dest.getDimensions())) {
      throw new IllegalArgumentException("Dimensions don't match");
  }

  TensorIterator srcIt = src.iterator();
  TensorIterator destIt = dest.iterator();
  while (srcIt.hasNext() && destIt.hasNext()) {
      dest.getElements()[destIt.next()] = src.getElements()[srcIt.next()];
  }
    }
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Related Classes of com.github.neuralnetworks.tensor.Tensor.TensorIterator

  • com.github.neuralnetworks.calculation.neuronfunctions.ConnectionCalculatorConv
  • com.github.neuralnetworks.tensor.TensorFactory
  • com.github.neuralnetworks.test.CNNTest
  • com.github.neuralnetworks.training.TrainingInputProvider
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