Examples of TensorIterator

cc.redberry.core.tensor.TensorIterator
An iterator for tensors that allows the programmer to traverse the tensor structure, modify the tensor during iteration. A {@code TensorIterator}has no current element; its cursor position always lies before the element that would be returned by a call to next().
Note that the remove() and set(Tensor) methods are not defined in terms of the cursor position; they are defined to operate on the last element returned by a call to next().
This interface has two main types of implementations. The first type belongs iterators returned by {@link Tensor#iterator()} method. This iterators traverse the tensor elements, without respect to its tree structure. For example, iterator returned by fraction will traverse only through two elements - first numerator and second denominator, and will not traverse numerator and denominator. For example:
```
 Fraction f = CC.parse("(a*b)/(c*d)"); for(Tensor t: f) System.out.println(t);
```
will print a*b, c*d consequentially.
Second type of implementations are tree iterators, witch traverse through all tree tensor structure and specified in {@link TensorTreeIterator}. For example:
```
 Fraction f = CC.parse("(a*b)/(c*d)"); TensorIterator it = new TensorFirstTreeIterator(t); while(it.hasNext()) System.out.println(it.next());
```
will print (a*b)/(c*d), a*b, a, b, c*d, c, d consequentially. @see Iterator @see Tensor @see TensorTreeIterator @author Dmitry Bolotin @author Stanislav Poslavsky
com.github.neuralnetworks.tensor.Tensor.TensorIterator

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


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

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

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

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

    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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