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
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
|
# A quick introduction to tiny-dnn
Include tiny_dnn.h:
```cpp
#include "tiny_dnn/tiny_dnn.h"
using namespace tiny_dnn;
using namespace tiny_dnn::layers;
using namespace tiny_dnn::activation;
```
Declare the model as ```network```. There are 2 types of network: ```network<sequential>``` and ```network<graph>```. The sequential model is easier to construct.
```cpp
network<sequential> net;
```
Stack layers:
```cpp
net << conv<tan_h>(32, 32, 5, 1, 6, padding::same) // in:32x32x1, 5x5conv, 6fmaps
<< max_pool<tan_h>(32, 32, 6, 2) // in:32x32x6, 2x2pooling
<< conv<tan_h>(16, 16, 5, 6, 16, padding::same) // in:16x16x6, 5x5conv, 16fmaps
<< max_pool<tan_h>(16, 16, 16, 2) // in:16x16x16, 2x2pooling
<< fc<tan_h>(8*8*16, 100) // in:8x8x16, out:100
<< fc<softmax>(100, 10); // in:100 out:10
```
Some layer takes an activation as a template parameter : ```max_pool<relu>``` means "apply a relu activation after the pooling". if the layer has no successive activation, use ```max_pool<identity>``` instead.
Declare the optimizer:
```cpp
adagrad opt;
```
In addition to gradient descent, you can use modern optimizers such as adagrad, adadelta, adam.
Now you can start the training:
```cpp
int epochs = 50;
int batch = 20;
net.fit<cross_entropy>(opt, x_data, y_data, batch, epochs);
```
If you don't have the target vector but have the class-id, you can alternatively use ```train```.
```cpp
net.train<cross_entropy>(opt, x_data, y_label, batch, epochs);
```
Validate the training result:
```cpp
auto test_result = net.test(x_data, y_label);
auto loss = net.get_loss<cross_entropy>(x_data, y_data);
```
Generate prediction on the new data:
```cpp
auto y_vector = net.predict(x_data);
auto y_label = net.predict_max_label(x_data);
```
Save the trained parameter and models:
```cpp
net.save("my-network");
```
For a more in-depth about tiny-dnn, check out [MNIST classification](https://github.com/tiny-dnn/tiny-dnn/tree/master/examples/mnist) where you can see the end-to-end example.
You will find tiny-dnn's API in [How-to](../how_tos/How-Tos.md).
|
