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Update dnn_introduction_ex.cpp (#2066)
Changed C-style cast to static_cast.
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@ -138,7 +138,7 @@ int main(int argc, char** argv) try
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}
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}
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cout << "training num_right: " << num_right << endl;
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cout << "training num_right: " << num_right << endl;
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cout << "training num_wrong: " << num_wrong << endl;
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cout << "training num_wrong: " << num_wrong << endl;
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cout << "training accuracy: " << num_right/(double)(num_right+num_wrong) << endl;
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cout << "training accuracy: " << num_right / static_cast<double>(num_right + num_wrong) << endl;
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// Let's also see if the network can correctly classify the testing images. Since
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// Let's also see if the network can correctly classify the testing images. Since
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// MNIST is an easy dataset, we should see at least 99% accuracy.
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// MNIST is an easy dataset, we should see at least 99% accuracy.
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@ -155,7 +155,7 @@ int main(int argc, char** argv) try
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}
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}
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cout << "testing num_right: " << num_right << endl;
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cout << "testing num_right: " << num_right << endl;
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cout << "testing num_wrong: " << num_wrong << endl;
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cout << "testing num_wrong: " << num_wrong << endl;
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cout << "testing accuracy: " << num_right/(double)(num_right+num_wrong) << endl;
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cout << "testing accuracy: " << num_right / static_cast<double>(num_right + num_wrong) << endl;
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// Finally, you can also save network parameters to XML files if you want to do
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// Finally, you can also save network parameters to XML files if you want to do
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@ -167,4 +167,3 @@ catch(std::exception& e)
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{
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{
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cout << e.what() << endl;
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cout << e.what() << endl;
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}
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}
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