@Override
public AVector asVector() {
AVector v = Vector0.INSTANCE;
for (INDArray a : slices) {
v = v.join(a.asVector());
}
return v;
}
/**
* Pushes an input vector through the LSTM and gets an output. Cell state and recurrent result
* value are updated.
*
* @param input The input vector to push through
* @return The result vector
*/
public AVector step(AVector input) {
// concatenate result vector onto the end of input vector, dimension zero as it is 1-dimensional
// AVector
// System.out.println(input);
// System.out.println(result);
input = input.join(result);
if (input.length() != weights[0].getShape(1)) {
throw new RuntimeException(
"Input was the wrong shape! Input ( + last out) was "
+ input.length()
+ "but weights were "
+ weights[0].getShape(1));
}
// System.out.println(input);
// System.out.println(input.rows());
/* There are 4 layers in LSTM, in order of sig(0) sig(1) sig(2) tanh(3) */
// For each sigmoid layer, multiply its weight matrix by the input vector, add its bias vector,
// and perform sigmoid operation on resultant vector
for (int i = 0; i < 3; i++) {
// System.out.println(weights[i].columns());
// The "columns" of the weights 3d matrix should represent the two-dimensional matrices for
// each of the activations.
sigmoidMult1[i] = weights[i].innerProduct(input);
// System.out.println(sigmoidMult1[i]);
sigmoidMult1[i].add(biases[i]);
// System.out.println(sigmoidMult1[i]);
sigmoidLayers[i] = Operations.Sigmoid.operate(sigmoidMult1[i]);
// System.out.println(sigmoidLayers[i]);
}
// Calculate tanh layer in same fashion as sigmoid layer, but with tanh activation function
tanhLayer = weights[3].innerProduct(input);
tanhLayer.add(biases[3]);
tanhLayer = Operations.Tanh.operate(tanhLayer);
// do the first element-wise multiplication: sigmoid layer 1 and the current cell state
sigmoidLayers[0].multiply(cellState);
// System.out.println("multiplication of forget and cell state: " + sigmoidLayers[0]);
// do the second element-wise multiplication: sigmoid layer 2 and the tanh layer
sigmoidLayers[1].multiply(tanhLayer);
// System.out.println("multiplication of input and activation layer: " + sigmoidLayers[1]);
sigmoidLayers[0].add(sigmoidLayers[1]);
// System.out.println("addition of " + sigmoidLayers[1]);
cellState = sigmoidLayers[0];
sigmoidLayers[0] = null;
sigmoidLayers[1] = null;
AVector tanhOp = Operations.Tanh.operate(cellState.copy());
sigmoidLayers[2].multiply(tanhOp);
result = sigmoidLayers[2];
sigmoidLayers[2] = null;
return result.copy();
}