| 89 | } |
| 90 | |
| 91 | void ann::back_propagate(const vector<array> signal, const array &target, |
| 92 | const double &alpha) { |
| 93 | // Get error for output layer |
| 94 | array out = signal[num_layers - 1]; |
| 95 | array err = (out - target); |
| 96 | |
| 97 | int m = target.dims(0); |
| 98 | |
| 99 | for (int i = num_layers - 2; i >= 0; i--) { |
| 100 | array in = add_bias(signal[i]); |
| 101 | array delta = (deriv(out) * err).T(); |
| 102 | |
| 103 | // Adjust weights |
| 104 | array tg = alpha * matmul(delta, in); |
| 105 | array grad = -(tg) / m; |
| 106 | weights[i] += grad.T(); |
| 107 | |
| 108 | // Input to current layer is output of previous |
| 109 | out = signal[i]; |
| 110 | |
| 111 | err = matmulTT(delta, weights[i]); |
| 112 | |
| 113 | // Remove the error of bias and propagate backward |
| 114 | err = err(span, seq(1, out.dims(1))); |
| 115 | } |
| 116 | } |
| 117 | |
| 118 | ann::ann(vector<int> layers, double range, dtype dt) |
| 119 | : num_layers(layers.size()), weights(layers.size() - 1), datatype(dt) { |