@Test
public void testGravesLSTMInit() {
int nIn = 8;
int nOut = 25;
int nHiddenUnits = 17;
MultiLayerConfiguration conf =
new NeuralNetConfiguration.Builder()
.list(2)
.layer(
0,
new org.deeplearning4j.nn.conf.layers.GravesLSTM.Builder()
.nIn(nIn)
.nOut(nHiddenUnits)
.weightInit(WeightInit.DISTRIBUTION)
.activation("tanh")
.build())
.layer(
1,
new OutputLayer.Builder(LossFunctions.LossFunction.SQUARED_LOSS)
.nIn(nHiddenUnits)
.nOut(nOut)
.weightInit(WeightInit.DISTRIBUTION)
.activation("tanh")
.build())
.build();
MultiLayerNetwork network = new MultiLayerNetwork(conf);
network.init();
// Ensure that we have the correct number weights and biases, that these have correct shape etc.
Layer layer = network.getLayer(0);
assertTrue(layer instanceof GravesLSTM);
Map<String, INDArray> paramTable = layer.paramTable();
assertTrue(paramTable.size() == 3); // 2 sets of weights, 1 set of biases
INDArray recurrentWeights = paramTable.get(GravesLSTMParamInitializer.RECURRENT_WEIGHT_KEY);
assertArrayEquals(
recurrentWeights.shape(),
new int[] {
nHiddenUnits, 4 * nHiddenUnits + 3
}); // Should be shape: [layerSize,4*layerSize+3]
INDArray inputWeights = paramTable.get(GravesLSTMParamInitializer.INPUT_WEIGHT_KEY);
assertArrayEquals(
inputWeights.shape(),
new int[] {nIn, 4 * nHiddenUnits}); // Should be shape: [nIn,4*layerSize]
INDArray biases = paramTable.get(GravesLSTMParamInitializer.BIAS_KEY);
assertArrayEquals(
biases.shape(), new int[] {1, 4 * nHiddenUnits}); // Should be shape: [1,4*layerSize]
// Want forget gate biases to be initialized to > 0. See parameter initializer for details
INDArray forgetGateBiases =
biases.get(new INDArrayIndex[] {NDArrayIndex.interval(nHiddenUnits, 2 * nHiddenUnits)});
assertTrue(forgetGateBiases.gt(0).sum(Integer.MAX_VALUE).getDouble(0) == nHiddenUnits);
int nParams = recurrentWeights.length() + inputWeights.length() + biases.length();
assertTrue(nParams == layer.numParams());
}
@Test
public void testGRUInit() {
int nIn = 8;
int nOut = 25;
int nHiddenUnits = 17;
MultiLayerConfiguration conf =
new NeuralNetConfiguration.Builder()
.list(2)
.layer(
0,
new org.deeplearning4j.nn.conf.layers.GRU.Builder()
.nIn(nIn)
.nOut(nHiddenUnits)
.weightInit(WeightInit.DISTRIBUTION)
.build())
.layer(
1,
new OutputLayer.Builder(LossFunctions.LossFunction.SQUARED_LOSS)
.nIn(nHiddenUnits)
.nOut(nOut)
.weightInit(WeightInit.DISTRIBUTION)
.build())
.build();
MultiLayerNetwork network = new MultiLayerNetwork(conf);
network.init();
// Ensure that we have the correct number weights and biases, that these have correct shape etc.
Layer layer = network.getLayer(0);
assertTrue(layer instanceof GRU);
Map<String, INDArray> paramTable = layer.paramTable();
assertTrue(paramTable.size() == 3); // 2 sets of weights, 1 set of biases
INDArray recurrentWeights = paramTable.get(GRUParamInitializer.RECURRENT_WEIGHT_KEY);
assertArrayEquals(
recurrentWeights.shape(),
new int[] {nHiddenUnits, 3 * nHiddenUnits}); // Should be shape: [layerSize,3*layerSize]
INDArray inputWeights = paramTable.get(GRUParamInitializer.INPUT_WEIGHT_KEY);
assertArrayEquals(
inputWeights.shape(),
new int[] {nIn, 3 * nHiddenUnits}); // Should be shape: [nIn,3*layerSize]
INDArray biases = paramTable.get(GRUParamInitializer.BIAS_KEY);
assertArrayEquals(
biases.shape(), new int[] {1, 3 * nHiddenUnits}); // Should be shape: [1,3*layerSize]
int nParams = recurrentWeights.length() + inputWeights.length() + biases.length();
assertTrue(nParams == layer.numParams());
}
