private void initWeight() {
for (int i = 0; i < label_size; i++) {
for (int j = 0; j < feature_size; j++) {
W[i][j] = MathUtils.gaussian();
}
}
}
public double train(int[] input, double learningRate, int[] label) {
double[] p_label = new double[this.label_size];
for (int i = 0; i < this.label_size; i++) {
p_label[i] = 0;
for (int j = 0; j < this.feature_size; j++) {
p_label[i] += W[i][j] * input[j];
}
p_label[i] += bias[i];
}
MathUtils.softmax(p_label);
// 根据
// http://deeplearning.stanford.edu/wiki/index.php/Softmax%E5%9B%9E%E5%BD%92
// 进行更新
for (int i = 0; i < this.label_size; i++) {
double delta = label[i] - p_label[i];
for (int j = 0; j < this.feature_size; j++) {
W[i][j] += learningRate * input[j] * delta;
}
bias[i] += learningRate * delta;
}
return MathUtils.reconstructError(label, p_label);
}