@Override
public void init(Map<String, INDArray> params, NeuralNetConfiguration conf) {
Distribution dist = Distributions.createDistribution(conf.getDist());
int nL = conf.getNOut(); // i.e., n neurons in this layer
int nLast = conf.getNIn(); // i.e., n neurons in previous layer
conf.addVariable(RECURRENT_WEIGHTS);
conf.addVariable(INPUT_WEIGHTS);
conf.addVariable(BIAS);
params.put(
RECURRENT_WEIGHTS, WeightInitUtil.initWeights(nL, 4 * nL + 3, conf.getWeightInit(), dist));
params.put(
INPUT_WEIGHTS, WeightInitUtil.initWeights(nLast, 4 * nL, conf.getWeightInit(), dist));
INDArray biases =
Nd4j.zeros(1, 4 * nL); // Order: input, forget, output, input modulation, i.e., IFOG
biases.put(
new NDArrayIndex[] {NDArrayIndex.interval(nL, 2 * nL), new NDArrayIndex(0)},
Nd4j.ones(1, nL).muli(5));
/*The above line initializes the forget gate biases to 5.
* See Sutskever PhD thesis, pg19:
* "it is important for [the forget gate activations] to be approximately 1 at the early stages of learning,
* which is accomplished by initializing [the forget gate biases] to a large value (such as 5). If it is
* not done, it will be harder to learn long range dependencies because the smaller values of the forget
* gates will create a vanishing gradients problem."
* http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf
*/
params.put(BIAS, biases);
params.get(RECURRENT_WEIGHTS).data().persist();
params.get(INPUT_WEIGHTS).data().persist();
params.get(BIAS).data().persist();
}
protected INDArray createWeightMatrix(NeuralNetConfiguration conf) {
/**
* Create a 4d weight matrix of: (number of kernels, num input channels, kernel height, kernel
* width) Inputs to the convolution layer are: (batch size, num input feature maps, image
* height, image width)
*/
Distribution dist = Distributions.createDistribution(conf.getDist());
return WeightInitUtil.initWeights(
Ints.concat(new int[] {conf.getNOut(), conf.getNIn()}, conf.getKernelSize()),
conf.getWeightInit(),
dist);
}
private static void checkNinNoutForEachLayer(
int[] expNin, int[] expNout, MultiLayerConfiguration conf, MultiLayerNetwork network) {
// Check configuration
for (int i = 0; i < expNin.length; i++) {
NeuralNetConfiguration layerConf = conf.getConf(i);
assertTrue(layerConf.getNIn() == expNin[i]);
assertTrue(layerConf.getNOut() == expNout[i]);
}
// Check Layer
for (int i = 0; i < expNin.length; i++) {
Layer layer = network.getLayers()[i];
assertTrue(layer.conf().getNIn() == expNin[i]);
assertTrue(layer.conf().getNOut() == expNout[i]);
int[] weightShape = layer.getParam(DefaultParamInitializer.WEIGHT_KEY).shape();
assertTrue(weightShape[0] == expNin[i]);
assertTrue(weightShape[1] == expNout[i]);
}
}
