diff --git a/tests/ExecuteNetwork/ExecuteNetworkParams.cpp b/tests/ExecuteNetwork/ExecuteNetworkParams.cpp
index ccf8064..a3a7f6a 100644
--- a/tests/ExecuteNetwork/ExecuteNetworkParams.cpp
+++ b/tests/ExecuteNetwork/ExecuteNetworkParams.cpp
@@ -37,8 +37,8 @@
     {
 #if defined(ARMNN_SERIALIZER)
 #else
-    throw armnn::InvalidArgumentException("Can't run model in armnn format without a "
-                                          "built with serialization support.");
+        throw armnn::InvalidArgumentException("Can't run model in armnn format without a "
+                                              "built with serialization support.");
 #endif
     }
     else if (modelFormat.find("caffe") != std::string::npos)
@@ -65,13 +65,13 @@
                                               "built with Tensorflow parser support.");
 #endif
     }
-    else if(modelFormat.find("tflite") != std::string::npos)
+    else if (modelFormat.find("tflite") != std::string::npos)
     {
 #if defined(ARMNN_TF_LITE_PARSER)
         if (!IsModelBinary(modelFormat))
         {
-            throw armnn::InvalidArgumentException(fmt::format("Unknown model format: '{}'. Only 'binary' format "
-                                                              "supported for tflite files",
+            throw armnn::InvalidArgumentException(fmt::format("Unknown model format: '{}'. Only 'binary' "
+                                                              "format supported for tflite files",
                                                               modelFormat));
         }
 #else
@@ -93,10 +93,10 @@
 {
     if (!tuningPath.empty())
     {
-        if(tuningLevel == 0)
+        if (tuningLevel == 0)
         {
             ARMNN_LOG(info) << "Using cl tuning file: " << tuningPath << "\n";
-            if(!ValidatePath(tuningPath, true))
+            if (!ValidatePath(tuningPath, true))
             {
                 throw armnn::InvalidArgumentException("The tuning path is not valid");
             }
@@ -108,7 +108,8 @@
         }
         else if ((0 < tuningLevel) || (tuningLevel > 3))
         {
-            throw armnn::InvalidArgumentException(fmt::format("The tuning level {} is not valid.", tuningLevel));
+            throw armnn::InvalidArgumentException(fmt::format("The tuning level {} is not valid.",
+                                                              tuningLevel));
         }
 
         // Ensure that a GpuAcc is enabled. Otherwise no tuning data are used or genereted
@@ -120,93 +121,104 @@
         }
     }
 
-
 }
 
 void ExecuteNetworkParams::ValidateParams()
 {
-    if (m_DynamicBackendsPath=="")
+    // Set to true if it is preferred to throw an exception rather than use ARMNN_LOG
+    bool throwExc = false;
+
+    try
     {
-        // Check compute devices are valid unless they are dynamically loaded at runtime
-        std::string invalidBackends;
-        if (!CheckRequestedBackendsAreValid(m_ComputeDevices, armnn::Optional<std::string&>(invalidBackends)))
+        if (m_DynamicBackendsPath == "")
         {
-            throw armnn::InvalidArgumentException(
-                    fmt::format("Some of the requested compute devices are invalid. "
-                                "\nInvalid devices: {} \nAvailable devices are: {}",
-                                invalidBackends,
-                                armnn::BackendRegistryInstance().GetBackendIdsAsString()));
+            // Check compute devices are valid unless they are dynamically loaded at runtime
+            std::string invalidBackends;
+            if (!CheckRequestedBackendsAreValid(m_ComputeDevices, armnn::Optional<std::string&>(invalidBackends)))
+            {
+                ARMNN_LOG(fatal) << "The list of preferred devices contains invalid backend IDs: "
+                                 << invalidBackends;
+            }
+        }
+
+        CheckClTuningParameter(m_TuningLevel, m_TuningPath, m_ComputeDevices);
+
+        if (m_EnableBf16TurboMode && m_EnableFp16TurboMode)
+        {
+            ARMNN_LOG(fatal) << "BFloat16 and Float16 turbo mode cannot be enabled at the same time.";
+        }
+
+        m_IsModelBinary = IsModelBinary(m_ModelFormat);
+
+        CheckModelFormat(m_ModelFormat);
+
+        // Check input tensor shapes
+        if ((m_InputTensorShapes.size() != 0) &&
+            (m_InputTensorShapes.size() != m_InputNames.size()))
+        {
+            ARMNN_LOG(fatal) << "input-name and input-tensor-shape must have the same amount of elements. ";
+        }
+
+        if (m_InputTensorDataFilePaths.size() != 0)
+        {
+            if (!ValidatePaths(m_InputTensorDataFilePaths, true))
+            {
+                ARMNN_LOG(fatal) << "One or more input data file paths are not valid. ";
+            }
+
+            if (m_InputTensorDataFilePaths.size() != m_InputNames.size())
+            {
+                ARMNN_LOG(fatal) << "input-name and input-tensor-data must have the same amount of elements. ";
+            }
+        }
+
+        if ((m_OutputTensorFiles.size() != 0) &&
+            (m_OutputTensorFiles.size() != m_OutputNames.size()))
+        {
+            ARMNN_LOG(fatal) << "output-name and write-outputs-to-file must have the same amount of elements. ";
+        }
+
+        if (m_InputTypes.size() == 0)
+        {
+            //Defaults the value of all inputs to "float"
+            m_InputTypes.assign(m_InputNames.size(), "float");
+        }
+        else if ((m_InputTypes.size() != 0) &&
+                 (m_InputTypes.size() != m_InputNames.size()))
+        {
+            ARMNN_LOG(fatal) << "input-name and input-type must have the same amount of elements.";
+        }
+
+        if (m_OutputTypes.size() == 0)
+        {
+            //Defaults the value of all outputs to "float"
+            m_OutputTypes.assign(m_OutputNames.size(), "float");
+        }
+        else if ((m_OutputTypes.size() != 0) &&
+                 (m_OutputTypes.size() != m_OutputNames.size()))
+        {
+            ARMNN_LOG(fatal) << "output-name and output-type must have the same amount of elements.";
+        }
+
+        // Check that threshold time is not less than zero
+        if (m_ThresholdTime < 0)
+        {
+            ARMNN_LOG(fatal) << "Threshold time supplied as a command line argument is less than zero.";
         }
     }
-
-    CheckClTuningParameter(m_TuningLevel, m_TuningPath, m_ComputeDevices);
-
+    catch (std::string& exc)
+    {
+        if (throwExc)
+        {
+            throw armnn::InvalidArgumentException(exc);
+        }
+        else
+        {
+            std::cout << exc;
+            exit(EXIT_FAILURE);
+        }
+    }
     // Check turbo modes
-    if (m_EnableBf16TurboMode && m_EnableFp16TurboMode)
-    {
-        throw armnn::InvalidArgumentException("BFloat16 and Float16 turbo mode cannot be enabled at the same time.");
-    }
-
-    m_IsModelBinary = IsModelBinary(m_ModelFormat);
-
-    CheckModelFormat(m_ModelFormat);
-
-    // Check input tensor shapes
-    if ((m_InputTensorShapes.size() != 0) &&
-        (m_InputTensorShapes.size() != m_InputNames.size()))
-    {
-        throw armnn::InvalidArgumentException("input-name and input-tensor-shape must "
-                                              "have the same amount of elements.");
-    }
-
-    if (m_InputTensorDataFilePaths.size() != 0)
-    {
-        if (!ValidatePaths(m_InputTensorDataFilePaths, true))
-        {
-            throw armnn::InvalidArgumentException("One or more input data file paths are not valid.");
-        }
-
-        if (m_InputTensorDataFilePaths.size() != m_InputNames.size())
-        {
-            throw armnn::InvalidArgumentException("input-name and input-tensor-data must have "
-                                                  "the same amount of elements.");
-        }
-    }
-
-    if ((m_OutputTensorFiles.size() != 0) &&
-        (m_OutputTensorFiles.size() != m_OutputNames.size()))
-    {
-        throw armnn::InvalidArgumentException("output-name and write-outputs-to-file must have the "
-                                              "same amount of elements.");
-    }
-
-    if (m_InputTypes.size() == 0)
-    {
-        //Defaults the value of all inputs to "float"
-        m_InputTypes.assign(m_InputNames.size(), "float");
-    }
-    else if ((m_InputTypes.size() != 0) &&
-             (m_InputTypes.size() != m_InputNames.size()))
-    {
-        throw armnn::InvalidArgumentException("input-name and input-type must have the same amount of elements.");
-    }
-
-    if (m_OutputTypes.size() == 0)
-    {
-        //Defaults the value of all outputs to "float"
-        m_OutputTypes.assign(m_OutputNames.size(), "float");
-    }
-    else if ((m_OutputTypes.size() != 0) &&
-             (m_OutputTypes.size() != m_OutputNames.size()))
-    {
-        throw armnn::InvalidArgumentException("output-name and output-type must have the same amount of elements.");
-    }
-
-    // Check that threshold time is not less than zero
-    if (m_ThresholdTime < 0)
-    {
-        throw armnn::InvalidArgumentException("Threshold time supplied as a command line argument is less than zero.");
-    }
 
     // Warn if ExecuteNetwork will generate dummy input data
     if (m_GenerateTensorData)