remove backend cpu dependency from Adam optimizer

include/aidge/learning/optimizer/Adam.hpp

@@ -53,39 +53,33 @@ public:
                       attr<AdamAttr::Beta2>(beta2),
                       attr<AdamAttr::Epsilon>(epsilon))
     {
-        mBeta1.setBackend("cpu");
-        mBeta1.set<float>(0, beta1);
-        mReversedBeta1.setBackend("cpu");
-        mReversedBeta1.set<float>(0, 1.0f - beta1);
-		
-        mBeta2.setBackend("cpu");
-        mBeta2.set<float>(0, beta2);
-        mReversedBeta2.setBackend("cpu");
-        mReversedBeta2.set<float>(0, 1.0f - beta2);
-		
-        mEpsilon.setBackend("cpu");
-        mEpsilon.set<float>(0, epsilon);
+        mBeta1 = Tensor(Array1D<float, 1>{{beta1}});
+        mReversedBeta1 = Tensor(Array1D<float, 1>{{1.0f - beta1}});
+
+        mBeta2 = Tensor(Array1D<float, 1>{{beta2}});
+        mReversedBeta2 = Tensor(Array1D<float, 1>{{1.0f - beta2}});
+
+        mEpsilon = Tensor(Array1D<float, 1>{{epsilon}});
     }
 
-    void update() override final {		
+    void update() override final {
+        mLR = Tensor(Array1D<float, 1>{{learningRate()}});
         mLR.setBackend(mParameters[0]->getImpl()->backend());
-        mLR.set<float>(0, learningRate());
+
         if (mParameters[0]->getImpl()->backend() != mBeta1.getImpl()->backend()) {
             mBeta1.setBackend(mParameters[0]->getImpl()->backend());
             mReversedBeta1.setBackend(mParameters[0]->getImpl()->backend());
             mBeta2.setBackend(mParameters[0]->getImpl()->backend());
             mReversedBeta2.setBackend(mParameters[0]->getImpl()->backend());
         }
-		
-        Tensor alpha{std::vector<std::size_t>({1})};
+
+        Tensor alpha = Tensor(Array1D<float, 1>{{ static_cast<float>(learningRate() * std::sqrt(1.0f - std::pow(this->getAttr<AdamAttr::Beta2>(), mLRScheduler.step() + 1))
+                                           / (1.0f - std::pow(this->getAttr<AdamAttr::Beta1>(), mLRScheduler.step() + 1))) }});
         alpha.setBackend(mParameters[0]->getImpl()->backend());
-        alpha.set<float>(0, learningRate() * std::sqrt(1.0f - std::pow(mBeta2.get<float>(0), mLRScheduler.step() + 1))
-                                           / (1.0f - std::pow(mBeta1.get<float>(0), mLRScheduler.step() + 1)));
 
-        Tensor epsilon{std::vector<std::size_t>({1})};
+        Tensor epsilon = Tensor(Array1D<float, 1>{{ static_cast<float>(this->getAttr<AdamAttr::Epsilon>() * std::sqrt(1.0f - std::pow(this->getAttr<AdamAttr::Beta2>(), mLRScheduler.step() + 1))) }});
         epsilon.setBackend(mParameters[0]->getImpl()->backend());
-        epsilon.set<float>(0, mEpsilon.get<float>(0) * std::sqrt(1.0f - std::pow(mBeta2.get<float>(0), mLRScheduler.step() + 1)));
-		
+
         if (mLRScheduler.step() == 0) {
             for (std::size_t i = 0; i < mParameters.size(); ++i) {
                 mMomentum1[i].setBackend(mParameters[i]->getImpl()->backend());
@@ -112,7 +106,9 @@ public:
         mMomentum2 = std::vector<Tensor>(parameters.size());
         for (std::size_t i = 0; i < parameters.size(); ++i) {
             mMomentum1[i] = Tensor(parameters[i]->dims());
+            mMomentum1[i].setBackend(parameters[i]->getImpl()->backend());
             mMomentum2[i] = Tensor(parameters[i]->dims());
+            mMomentum2[i].setBackend(parameters[i]->getImpl()->backend());
         }
     }
 };