ref: 462f8462b7b7b7c05a5c2edf1b1d074f0b7774e4
parent: b299aa428c810f2b65d47c8e03aad9a5e527ab62
author: eli <eli@singularity>
date: Wed Dec 25 21:13:23 EST 2024
ann output layer activation function change
--- a/ann.c
+++ b/ann.c
@@ -47,6 +47,8 @@
double gradient_tanh(Neuron*);
double activation_leaky_relu(Neuron*);
double gradient_leaky_relu(Neuron*);
+double activation_piece(Neuron*);
+double gradient_piece(Neuron*);
#define ACTIVATION activation_leaky_relu
#define GRADIENT gradient_leaky_relu
@@ -120,6 +122,22 @@
return 0.01;
}
+double
+activation_piece(Neuron *in)
+{+ if (in->sum < 0.0)
+ return 0.0;
+ else if (in->sum > 1.0)
+ return 1.0;
+ return in->sum;
+}
+
+double
+gradient_piece(Neuron*)
+{+ return 1.0;
+}
+
Weights*
weightsinitdoubles(Weights *in, double *init)
{@@ -263,7 +281,7 @@
if (i < (num_layers-1))
ret->layers[i] = layercreate(arg, ACTIVATION, GRADIENT);
else
- ret->layers[i] = layercreate(arg, activation_sigmoid, gradient_sigmoid);
+ ret->layers[i] = layercreate(arg, activation_piece, gradient_piece);
if (i > 0) {ret->weights[i-1] = weightscreate(ret->layers[i-1]->n, ret->layers[i]->n, 1);
ret->deltas[i-1] = weightscreate(ret->layers[i-1]->n, ret->layers[i]->n, 0);
@@ -825,7 +843,12 @@
if (trainline == 0) {runoutput = annrun(ann, input);
for (i = 0; i < noutput; i++)
- print("%f%c", runoutput[i], (i == (noutput-1))? '\n': ' ');+ if (runoutput[i] == 0.0)
+ print("0%c", (i == (noutput-1))? '\n': ' ');+ else if (runoutput[i] == 1.0)
+ print("1%c", (i == (noutput-1))? '\n': ' ');+ else
+ print("%f%c", runoutput[i], (i == (noutput-1))? '\n': ' ');free(runoutput);
}
--
⑨