forked from Tencent/ncnn
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtest_innerproduct.h
55 lines (44 loc) · 1.14 KB
/
test_innerproduct.h
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
#pragma once
#include "gtest/gtest.h"
#include "layer/innerproduct.h"
/*
forward - pass:
[0,1,2,3] * [1,1,1,1 + [0.5, = [6.5,
1,1,1,1] 0.5] 6.5]
*/
TEST(innerproduct, forward)
{
// layer params
InnerProduct inner_product_layer;
inner_product_layer.num_output = 2; // W
inner_product_layer.bias_term = 1; // bias
inner_product_layer.weight_data_size = 3; // W + bias
// input & output
float_t in[] = {
0.0f, 1.0f, 2.0f, 3.0f
};
float_t expected_out[] = {
6.5, 6.5 /// 0+1+2+3+0.5
};
// weights & bias
float_t w[] = {
1.0f, 1.0f, 1.0f, 1.0f,
1.0f, 1.0f, 1.0f, 1.0f
};
float_t b[] = {
0.5f, 0.5f
};
// forward
Mat mat_in(4, in);
Mat mat_out;
inner_product_layer.bias_data.data = b;
inner_product_layer.weight_data.data = w;
inner_product_layer.forward(mat_in, mat_out);
// check expect
EXPECT_EQ(mat_out.c, 2);
for (int i = 0; i < _countof(expected_out); ++i)
{
float output_value = *(mat_out.data + mat_out.cstep * i);
EXPECT_NEAR(output_value, expected_out[i], 1E-5);
}
}