Hello!
I have an issue that when I am trying to predict the Q-values by feeding my inputs to the network, no matter what I feed I seem to get the same output from the network? I am assuming that this is because of the very small changes in the input vector?
This post will first show my network structure, then I will continue with the function that feeds the inputs to the network followed by the function can all how I construct the model. At the end I will show the input that was fed and the predicted values from the function.
My network structure:
class Model :public torch::nn::Module {
public:
torch::nn::Linear input_layer{ nullptr }, first_hidden_layer{ nullptr }, output_layer{ nullptr };
//Constructor
Model() {
//construct and register the layers we want
input_layer = register_module("input_layer", torch::nn::Linear(k_input_size_*k_amount_of_states_, 128));
first_hidden_layer = register_module("first_hidden_layer", torch::nn::Linear(128, 256));
output_layer = register_module("output_layer", torch::nn::Linear(256, k_output_size_));
}
~Model() {
}
//Feed forward through the network
torch::Tensor forward(torch::Tensor tensor_x) {
tensor_x = torch::relu(input_layer->forward(tensor_x));
tensor_x = torch::relu(first_hidden_layer->forward(tensor_x));
tensor_x = output_layer->forward(tensor_x);
return (tensor_x);
}
};
My prediction function:
int PerceivedActions::getAction(PerceivedActions::Model &model_, const std::vector<double> &input_vector) const {
std::vector<float> float_vec(input_vector.begin(), input_vector.end());
torch::Tensor input_tensor = torch::from_blob(float_vec.data(), { k_input_size_*k_amount_of_states_ }/*size*/);
std::cout << "input_tensor: " << input_tensor << std::endl;
torch::NoGradGuard no_grad;
model_.eval();
auto DNN_out = model_.forward(input_tensor);
std::cout << DNN_out << std::endl;
int action = DNN_out.argmax(0).item().toInt();
return action;
}
Function call and constructing the model:
model_ = std::make_unique<PerceivedActions::Model>();
action = getAction(*model_, last_states_);
The cout from the function:
input_tensor: 3.4798e-02
-7.2078e-05
1.3180e-02
-9.9746e-01
-2.0106e-04
-2.8676e-03
6.9767e-06
4.3532e-04
-3.5193e-06
1.7581e-04
-9.9486e-01
8.4208e-03
-5.6824e-06
4.0093e-04
1.0000e+00
-1.0000e+00
1.0000e+00
3.4785e-02
-2.6299e-04
1.3100e-02
-9.9477e-01
-2.0134e-04
-2.8676e-03
1.0840e-05
2.1770e-03
-2.9073e-05
8.1203e-04
-9.9119e-01
3.0049e-02
-5.5876e-04
1.4589e-03
1.0000e+00
-1.0000e+00
1.0000e+00
3.4772e-02
-4.5389e-04
1.3066e-02
-9.9144e-01
-2.0145e-04
-2.8674e-03
9.5414e-06
4.7711e-03
-9.5219e-05
1.7521e-03
-9.8700e-01
4.0107e-02
-1.0612e-03
1.9446e-03
1.0000e+00
-1.0000e+00
1.0000e+00
3.4759e-02
-6.4478e-04
1.3047e-02
-9.8802e-01
-2.0139e-04
-2.8673e-03
8.3816e-06
7.4450e-03
-1.6590e-04
2.7179e-03
-9.8552e-01
4.0108e-02
-1.0608e-03
1.9390e-03
1.0000e+00
-1.0000e+00
1.0000e+00
[ CPUFloatType{68} ]
12.0482
-0.2093
7.3530
-0.5870
13.6391
[ CPUFloatType{5} ]
input_tensor: 3.4785e-02
-2.6299e-04
1.3100e-02
-9.9477e-01
-2.0134e-04
-2.8676e-03
1.0840e-05
2.1770e-03
-2.9073e-05
8.1203e-04
-9.9119e-01
3.0049e-02
-5.5876e-04
1.4589e-03
1.0000e+00
-1.0000e+00
1.0000e+00
3.4772e-02
-4.5389e-04
1.3066e-02
-9.9144e-01
-2.0145e-04
-2.8674e-03
9.5414e-06
4.7711e-03
-9.5219e-05
1.7521e-03
-9.8700e-01
4.0107e-02
-1.0612e-03
1.9446e-03
1.0000e+00
-1.0000e+00
1.0000e+00
3.4759e-02
-6.4478e-04
1.3047e-02
-9.8802e-01
-2.0139e-04
-2.8673e-03
8.3816e-06
7.4450e-03
-1.6590e-04
2.7179e-03
-9.8552e-01
4.0108e-02
-1.0608e-03
1.9390e-03
1.0000e+00
-1.0000e+00
1.0000e+00
3.4746e-02
-8.3567e-04
1.3033e-02
-9.8456e-01
-2.0135e-04
-2.8672e-03
7.0877e-06
1.0119e-02
-2.3652e-04
3.6809e-03
-9.8481e-01
4.0108e-02
-1.0603e-03
1.9346e-03
1.0000e+00
-1.0000e+00
1.0000e+00
[ CPUFloatType{68} ]
12.0482
-0.2093
7.3530
-0.5870
13.6391
[ CPUFloatType{5} ]
input_tensor: 3.4772e-02
-4.5389e-04
1.3066e-02
-9.9144e-01
-2.0145e-04
-2.8674e-03
9.5414e-06
4.7711e-03
-9.5219e-05
1.7521e-03
-9.8700e-01
4.0107e-02
-1.0612e-03
1.9446e-03
1.0000e+00
-1.0000e+00
1.0000e+00
3.4759e-02
-6.4478e-04
1.3047e-02
-9.8802e-01
-2.0139e-04
-2.8673e-03
8.3816e-06
7.4450e-03
-1.6590e-04
2.7179e-03
-9.8552e-01
4.0108e-02
-1.0608e-03
1.9390e-03
1.0000e+00
-1.0000e+00
1.0000e+00
3.4746e-02
-8.3567e-04
1.3033e-02
-9.8456e-01
-2.0135e-04
-2.8672e-03
7.0877e-06
1.0119e-02
-2.3652e-04
3.6809e-03
-9.8481e-01
4.0108e-02
-1.0603e-03
1.9346e-03
1.0000e+00
-1.0000e+00
1.0000e+00
3.4732e-02
-1.0265e-03
1.3022e-02
-9.8109e-01
-2.0130e-04
-2.8670e-03
6.5288e-06
1.2793e-02
-3.0710e-04
4.6419e-03
-9.8440e-01
4.0108e-02
-1.0599e-03
1.9310e-03
1.0000e+00
-1.0000e+00
1.0000e+00
[ CPUFloatType{68} ]
12.0482
-0.2093
7.3530
-0.5870
13.6391
[ CPUFloatType{5} ]
input_tensor: 3.4759e-02
-6.4478e-04
1.3047e-02
-9.8802e-01
-2.0139e-04
-2.8673e-03
8.3816e-06
7.4450e-03
-1.6590e-04
2.7179e-03
-9.8552e-01
4.0108e-02
-1.0608e-03
1.9390e-03
1.0000e+00
-1.0000e+00
1.0000e+00
3.4746e-02
-8.3567e-04
1.3033e-02
-9.8456e-01
-2.0135e-04
-2.8672e-03
7.0877e-06
1.0119e-02
-2.3652e-04
3.6809e-03
-9.8481e-01
4.0108e-02
-1.0603e-03
1.9346e-03
1.0000e+00
-1.0000e+00
1.0000e+00
3.4732e-02
-1.0265e-03
1.3022e-02
-9.8109e-01
-2.0130e-04
-2.8670e-03
6.5288e-06
1.2793e-02
-3.0710e-04
4.6419e-03
-9.8440e-01
4.0108e-02
-1.0599e-03
1.9310e-03
1.0000e+00
-1.0000e+00
1.0000e+00
3.4719e-02
-1.2174e-03
1.3012e-02
-9.7760e-01
-2.0125e-04
-2.8669e-03
5.7976e-06
2.3993e-02
-9.9653e-04
8.8582e-03
-9.7347e-01
8.2793e-02
-4.1984e-03
4.1589e-03
1.0000e+00
-1.0000e+00
1.0000e+00
[ CPUFloatType{68} ]
12.0482
-0.2093
7.3530
-0.5870
13.6391
[ CPUFloatType{5} ]