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Internal architecture changes (to allow diferent activation functions for each layer and to allow hidden layers to have different number of nodes).

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davidjacnogueira
2016-11-09 02:31:53 +00:00
parent f647b05f70
commit 0a636416ed
7 changed files with 193 additions and 100 deletions
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35
src/NodeTest.cpp
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@@ -39,7 +39,10 @@ void Train(Node & node,
int error_count = 0;
for (auto & training_sample_with_bias : training_sample_set_with_bias) {
bool prediction;
node.GetBooleanOutput(training_sample_with_bias.input_vector(), &prediction, 0.5);
node.GetBooleanOutput(training_sample_with_bias.input_vector(),
utils::linear,
&prediction,
0.5);
bool correct_output = training_sample_with_bias.output_vector()[0] > 0.5 ? true : false;
if (prediction != correct_output) {
error_count++;
@@ -85,7 +88,10 @@ UNIT(LearnAND) {
for (const auto & training_sample : training_sample_set_with_bias) {
bool class_id;
my_node.GetBooleanOutput(training_sample.input_vector(), &class_id, 0.5);
my_node.GetBooleanOutput(training_sample.input_vector(),
utils::linear,
&class_id,
0.5);
bool correct_output = training_sample.output_vector()[0] > 0.5 ? true : false;
ASSERT_TRUE(class_id == correct_output);
}
@@ -117,7 +123,10 @@ UNIT(LearnNAND) {
for (const auto & training_sample : training_sample_set_with_bias) {
bool class_id;
my_node.GetBooleanOutput(training_sample.input_vector(), &class_id, 0.5);
my_node.GetBooleanOutput(training_sample.input_vector(),
utils::linear,
&class_id,
0.5);
bool correct_output = training_sample.output_vector()[0] > 0.5 ? true : false;
ASSERT_TRUE(class_id == correct_output);
}
@@ -149,7 +158,10 @@ UNIT(LearnOR) {
for (const auto & training_sample : training_sample_set_with_bias) {
bool class_id;
my_node.GetBooleanOutput(training_sample.input_vector(), &class_id, 0.5);
my_node.GetBooleanOutput(training_sample.input_vector(),
utils::linear,
&class_id,
0.5);
bool correct_output = training_sample.output_vector()[0] > 0.5 ? true : false;
ASSERT_TRUE(class_id == correct_output);
}
@@ -180,7 +192,10 @@ UNIT(LearnNOR) {
for (const auto & training_sample : training_sample_set_with_bias) {
bool class_id;
my_node.GetBooleanOutput(training_sample.input_vector(), &class_id, 0.5);
my_node.GetBooleanOutput(training_sample.input_vector(),
utils::linear,
&class_id,
0.5);
bool correct_output = training_sample.output_vector()[0] > 0.5 ? true : false;
ASSERT_TRUE(class_id == correct_output);
}
@@ -210,7 +225,10 @@ UNIT(LearnNOT) {
for (const auto & training_sample : training_sample_set_with_bias) {
bool class_id;
my_node.GetBooleanOutput(training_sample.input_vector(), &class_id, 0.5);
my_node.GetBooleanOutput(training_sample.input_vector(),
utils::linear,
&class_id,
0.5);
bool correct_output = training_sample.output_vector()[0] > 0.5 ? true : false;
ASSERT_TRUE(class_id == correct_output);
}
@@ -242,7 +260,10 @@ UNIT(LearnXOR) {
for (const auto & training_sample : training_sample_set_with_bias) {
bool class_id;
my_node.GetBooleanOutput(training_sample.input_vector(), &class_id, 0.5);
my_node.GetBooleanOutput(training_sample.input_vector(),
utils::linear,
&class_id,
0.5);
bool correct_output = training_sample.output_vector()[0] > 0.5 ? true : false;
if (class_id != correct_output) {
LOG(WARNING) << "Failed to train. " <<