void data_test()
{
float[] test = new float[2] {
0, 0
};
float[] test2 = new float[2] {
0, 1
};
float[] test3 = new float[2] {
1, 0
};
float[] test4 = new float[2] {
1, 1
};
float[] predict = nn.predict(test);
//float[] predict2 = nn.predict(test2);
//float[] predict3 = nn.predict(test3);
//float[] predict4 = nn.predict(test4);
Debug.Log("output data test");
Debug.Log(predict[0]);
//Debug.Log(predict2[0]);
//Debug.Log(predict3[0]);
//Debug.Log(predict4[0]);
}
/**
* test the data with XOR input
* */
void data_test()
{
float[] test1 = new float[2] {
0, 0
};
float[] test2 = new float[2] {
0, 1
};
float[] test3 = new float[2] {
1, 0
};
float[] test4 = new float[2] {
1, 1
};
float[] predict1 = nn.predict(test1);
float[] predict2 = nn.predict(test2);
float[] predict3 = nn.predict(test3);
float[] predict4 = nn.predict(test4);
Debug.Log("output data test");
//Debug.Log(predict1[0]); // output should be 0 ( false )
//Debug.Log(predict2[0]); // output should be 1 ( true )
//Debug.Log(predict3[0]); // output should be 1 ( true )
//Debug.Log(predict4[0]); // output should be 0 ( false )
/**
* NOTE: if the output data got stuck beetwen 4 and 5 for a long time, restart train data
*
* cause: random weight input wasn't good.
* */
// if predict output[0] is less then 0.01 / false, then done
if (predict1[0] < 0.01)
{
Debug.Log("Done");
}
else
{
Debug.Log(predict1[0]);
}
}
