diff --git a/inference-engine/samples/ngraph_function_creation_sample/main.cpp b/inference-engine/samples/ngraph_function_creation_sample/main.cpp
index 668363e4471..b360c08d69e 100644
--- a/inference-engine/samples/ngraph_function_creation_sample/main.cpp
+++ b/inference-engine/samples/ngraph_function_creation_sample/main.cpp
@@ -382,6 +382,9 @@ int main(int argc, char* argv[]) {
                 trim(strLine);
                 labels.push_back(strLine);
             }
+            inputFile.close();
+        } else {
+            throw std::logic_error("Cannot read label file");
         }
 
         ClassificationResult classificationResult(outputBlob, images, batchSize, FLAGS_nt, labels);
diff --git a/inference-engine/src/inference_engine/generic_ie.cpp b/inference-engine/src/inference_engine/generic_ie.cpp
index bf9dcea0b27..d1c45f20d4b 100644
--- a/inference-engine/src/inference_engine/generic_ie.cpp
+++ b/inference-engine/src/inference_engine/generic_ie.cpp
@@ -63,9 +63,9 @@ ngraph::op::GenericIE::GenericIE(const ngraph::NodeVector& inputs,
     : GenericIE(as_output_vector(inputs), params, type, outputs) {}
 
 ngraph::op::GenericIE::GenericIE(const ngraph::OutputVector& inputs,
-                                 const std::map<std::string, InferenceEngine::Parameter>& params,
-                                 const std::string type, const std::vector<PortIE>& outputs)
-    : Op(inputs), params(params), outputs(outputs), type(type), initialized(0) {
+                                 const std::map<std::string, InferenceEngine::Parameter>& params_,
+                                 const std::string type_, const std::vector<PortIE>& outputs_)
+    : Op(inputs), params(params_), outputs(outputs_), type(type_), initialized(0) {
     constructor_validate_and_infer_types();
 }
 
diff --git a/inference-engine/src/inference_engine/ie_network_reader.cpp b/inference-engine/src/inference_engine/ie_network_reader.cpp
index fccdb092b8e..6c9089a166d 100644
--- a/inference-engine/src/inference_engine/ie_network_reader.cpp
+++ b/inference-engine/src/inference_engine/ie_network_reader.cpp
@@ -179,7 +179,9 @@ CNNNetwork details::ReadNetwork(const std::string& modelPath, const std::string&
                     THROW_IE_EXCEPTION << "Weights file " << bPath << " cannot be opened!";
 
                 // read model with weights
-                return reader->read(modelStream, binStream, exts);
+                auto network = reader->read(modelStream, binStream, exts);
+                modelStream.close();
+                return network;
             }
             // read model without weights
             return reader->read(modelStream, exts);
diff --git a/inference-engine/src/mkldnn_plugin/nodes/argmax_imp.cpp b/inference-engine/src/mkldnn_plugin/nodes/argmax_imp.cpp
index b90851387d6..032c93cf8ac 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/argmax_imp.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/argmax_imp.cpp
@@ -182,8 +182,6 @@ void argmax_many_classes_has_axis(const float* src_data, float* dst_data, Shape
             vmask_type vmask;
             int s_index = i0 * dim * after_num + ib1 * block_size;
 
-            std::memset(reinterpret_cast<void*>(&vmax_values[0]), 0, sizeof(vmax_values));
-
             auto vswap_func = [&](int index1, int index2) {
                 vtmp = vmax_values[index1];
                 vmax_values[index1] = _mm_uni_blendv_ps(vmax_values[index1], vmax_values[index2], vmask);
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_normalize_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_normalize_node.cpp
index a004acdd63c..2dff7d0085b 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_normalize_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_normalize_node.cpp
@@ -667,7 +667,8 @@ private:
 };
 
 MKLDNNNormalizeNode::MKLDNNNormalizeNode(const InferenceEngine::CNNLayerPtr& layer, const mkldnn::engine& eng, MKLDNNWeightsSharing::Ptr &cache)
-        : MKLDNNNode(layer, eng, cache) {}
+        : MKLDNNNode(layer, eng, cache), src_data_size(0lu), dst_data_size(0lu), weights_data_size(0lu),
+        input_prec(Precision::UNSPECIFIED), output_prec(Precision::UNSPECIFIED), weights_prec(Precision::UNSPECIFIED) {}
 
 void MKLDNNNormalizeNode::getSupportedDescriptors() {
     if (!descs.empty())