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Added layer access and printing examples to inception sample

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Fm 2016-05-26 19:40:10 +03:00
parent 290b1cb15b
commit 2f7d3578d2
1 changed files with 26 additions and 10 deletions
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36
examples/dnn_inception_ex.cpp
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@ -22,19 +22,19 @@ using namespace dlib;
// Inception layer has some different convolutions inside
// Here we define blocks as convolutions with different kernel size that we will use in
// inception layer block.
template <typename SUBNET> using block_a1 = relu<con<4,1,1,1,1,SUBNET>>;
template <typename SUBNET> using block_a2 = relu<con<4,3,3,1,1,relu<con<4,1,1,1,1,SUBNET>>>>;
template <typename SUBNET> using block_a3 = relu<con<4,5,5,1,1,relu<con<4,1,1,1,1,SUBNET>>>>;
template <typename SUBNET> using block_a4 = relu<con<4,1,1,1,1,max_pool<3,3,1,1,SUBNET>>>;
template <typename SUBNET> using block_a1 = relu<con<10,1,1,1,1,SUBNET>>;
template <typename SUBNET> using block_a2 = relu<con<10,3,3,1,1,relu<con<16,1,1,1,1,SUBNET>>>>;
template <typename SUBNET> using block_a3 = relu<con<10,5,5,1,1,relu<con<16,1,1,1,1,SUBNET>>>>;
template <typename SUBNET> using block_a4 = relu<con<10,1,1,1,1,max_pool<3,3,1,1,SUBNET>>>;
// Here is inception layer definition. It uses different blocks to process input and returns combined output
template <typename SUBNET> using incept_a = inception4<block_a1,block_a2,block_a3,block_a4, SUBNET>;
// Network can have inception layers of different structure.
// Here are blocks with different convolutions
template <typename SUBNET> using block_b1 = relu<con<8,1,1,1,1,SUBNET>>;
template <typename SUBNET> using block_b2 = relu<con<8,3,3,1,1,SUBNET>>;
template <typename SUBNET> using block_b3 = relu<con<8,1,1,1,1,max_pool<3,3,1,1,SUBNET>>>;
template <typename SUBNET> using block_b1 = relu<con<4,1,1,1,1,SUBNET>>;
template <typename SUBNET> using block_b2 = relu<con<4,3,3,1,1,SUBNET>>;
template <typename SUBNET> using block_b3 = relu<con<4,1,1,1,1,max_pool<3,3,1,1,SUBNET>>>;
// Here is inception layer definition. It uses different blocks to process input and returns combined output
template <typename SUBNET> using incept_b = inception3<block_b1,block_b2,block_b3,SUBNET>;
@ -44,9 +44,9 @@ using net_type = loss_multiclass_log<
fc<10,
relu<fc<32,
max_pool<2,2,2,2,incept_b<
max_pool<2,2,2,2,incept_a<
max_pool<2,2,2,2,tag1<incept_a<
input<matrix<unsigned char>>
>>>>>>>>;
>>>>>>>>>;
int main(int argc, char** argv) try
{
@ -68,10 +68,26 @@ int main(int argc, char** argv) try
load_mnist_dataset(argv[1], training_images, training_labels, testing_images, testing_labels);
// The rest of the sample is identical to dnn_minst_ex
// Create network of predefined type.
net_type net;
// Now let's print the details of the pnet to the screen and inspect it.
cout << "The net has " << net.num_layers << " layers in it." << endl;
cout << net << endl;
// we can access inner layers with layer<> function:
// with tags
auto& in_b = layer<tag1>(net);
cout << "Found inception B layer: " << endl << in_b << endl;
// and we can access layers inside inceptions with itags
auto& in_b_1 = layer<itag1>(in_b);
cout << "Found inception B/1 layer: " << endl << in_b_1 << endl;
// or this is identical to
auto& in_b_1_a = layer<tag1,2>(net);
cout << "Found inception B/1 layer alternative way: " << endl << in_b_1_a << endl;
cout << "Traning NN..." << endl;
// The rest of the sample is identical to dnn_minst_ex
// And then train it using the MNIST data. The code below uses mini-batch stochastic
// gradient descent with an initial learning rate of 0.01 to accomplish this.
dnn_trainer<net_type> trainer(net);