diff --git a/core/conversion/converters/impl/element_wise.cpp b/core/conversion/converters/impl/element_wise.cpp
index 80d5cdbe93..375e7a2d8f 100644
--- a/core/conversion/converters/impl/element_wise.cpp
+++ b/core/conversion/converters/impl/element_wise.cpp
@@ -8,18 +8,22 @@ namespace converters {
 namespace impl {
 namespace {
 
-nvinfer1::ILayer* add_elementwise(ConversionCtx* ctx, nvinfer1::ElementWiseOperation op,  nvinfer1::ITensor* self, nvinfer1::ITensor* other, float scalar=1) {
+nvinfer1::ILayer* add_elementwise(ConversionCtx* ctx, nvinfer1::ElementWiseOperation op,  nvinfer1::ITensor* self, nvinfer1::ITensor* other, const std::string& name, float scalar=1) {
     auto self_dims = self->getDimensions();
+    auto self_dims_vec = util::toVec(self_dims);
     auto other_dims = other->getDimensions();
+    auto other_dims_vec = util::toVec(other_dims);
+    auto other_batch = other_dims_vec[0];
 
-    TRTORCH_CHECK(util::volume(self_dims) == util::volume(other_dims), "Found inputs to elementwise operation do not have the same number of elements:\n  Found: self " << self_dims << " other " << other_dims);
+    // TODO: Proper broadcast check
+    TRTORCH_CHECK(util::volume(self_dims) == util::volume(other_dims) || util::volume(self_dims) == util::volume(other_dims) / other_batch, "Found inputs to elementwise operation do not have the same number of elements or is not broadcastable:\n  Found: self " << self_dims << " other " << other_dims);
 
     if (self_dims != other_dims) {
         LOG_DEBUG("Input shape dont match inserting shuffle layers to reshape to " << self_dims);
-        auto other_shuffle = ctx->net->addShuffle(*other);
-        other_shuffle->setReshapeDimensions(self_dims);
-        other_shuffle->setName(std::string("[Reshape other to " + util::toStr(self_dims) + ']').c_str());
-        other = other_shuffle->getOutput(0);
+        auto self_shuffle = ctx->net->addShuffle(*self);
+        self_shuffle->setReshapeDimensions(util::toDimsPad(self_dims_vec, other_dims_vec.size()));
+        self_shuffle->setName(std::string("[Reshape self to " + util::toStr(self_dims) + " for broadcasting (" + name + ")]").c_str());
+        self = self_shuffle->getOutput(0);
     }
 
 
@@ -72,7 +76,7 @@ auto element_wise_registrations = RegisterNodeConversionPatterns()
                 auto self = args[0].ITensor();
                 auto other = args[1].ITensor();
                 auto scalar = args[2].unwrapToScalar().to<float>();
-                auto add = add_elementwise(ctx, nvinfer1::ElementWiseOperation::kSUM, self, other, scalar);
+                auto add = add_elementwise(ctx, nvinfer1::ElementWiseOperation::kSUM, self, other, util::node_info(n), scalar);
 
                 TRTORCH_CHECK(add, "Unable to create add layer from node: " << *n);
 
@@ -89,7 +93,7 @@ auto element_wise_registrations = RegisterNodeConversionPatterns()
                 auto self = args[0].ITensor();
                 auto other = args[1].ITensor();
                 auto scalar = args[2].unwrapToScalar().to<float>();
-                auto add = add_elementwise(ctx, nvinfer1::ElementWiseOperation::kSUM, self, other, scalar);
+                auto add = add_elementwise(ctx, nvinfer1::ElementWiseOperation::kSUM, self, other, util::node_info(n), scalar);
 
                 TRTORCH_CHECK(add, "Unable to create add layer from node: " << *n);
 
@@ -106,7 +110,7 @@ auto element_wise_registrations = RegisterNodeConversionPatterns()
                 auto self = args[0].ITensor();
                 auto other = args[1].ITensor();
                 auto scalar = args[2].unwrapToScalar().to<float>();
-                auto sub = add_elementwise(ctx, nvinfer1::ElementWiseOperation::kSUB, self, other, scalar);
+                auto sub = add_elementwise(ctx, nvinfer1::ElementWiseOperation::kSUB, self, other, util::node_info(n), scalar);
 
                 TRTORCH_CHECK(sub, "Unable to create sub layer from node: " << *n);
 
@@ -122,7 +126,7 @@ auto element_wise_registrations = RegisterNodeConversionPatterns()
                 // Should implement self / other
                 auto self = args[0].ITensor();
                 auto other = args[1].ITensor();
-                auto div = add_elementwise(ctx, nvinfer1::ElementWiseOperation::kDIV, self, other);
+                auto div = add_elementwise(ctx, nvinfer1::ElementWiseOperation::kDIV, self, other, util::node_info(n));
 
                 TRTORCH_CHECK(div, "Unable to create div layer from node: " << *n);
 
@@ -138,7 +142,7 @@ auto element_wise_registrations = RegisterNodeConversionPatterns()
                 // TODO: Remove with functionalization
                 auto self = args[0].ITensor();
                 auto other = args[1].ITensor();
-                auto div = add_elementwise(ctx, nvinfer1::ElementWiseOperation::kDIV, self, other);
+                auto div = add_elementwise(ctx, nvinfer1::ElementWiseOperation::kDIV, self, other, util::node_info(n));
 
                 TRTORCH_CHECK(div, "Unable to create div layer from node: " << *n);
 
@@ -154,7 +158,7 @@ auto element_wise_registrations = RegisterNodeConversionPatterns()
                 // Should implement self * other
                 auto self = args[0].ITensor();
                 auto other = args[1].ITensor();
-                auto mul = add_elementwise(ctx, nvinfer1::ElementWiseOperation::kPROD, self, other);
+                auto mul = add_elementwise(ctx, nvinfer1::ElementWiseOperation::kPROD, self, other, util::node_info(n));
 
                 TRTORCH_CHECK(mul, "Unable to create mul layer from node: " << *n);
 
@@ -170,7 +174,7 @@ auto element_wise_registrations = RegisterNodeConversionPatterns()
                 // TODO: Remove with functionalization
                 auto self = args[0].ITensor();
                 auto other = args[1].ITensor();
-                auto mul = add_elementwise(ctx, nvinfer1::ElementWiseOperation::kPROD, self, other);
+                auto mul = add_elementwise(ctx, nvinfer1::ElementWiseOperation::kPROD, self, other, util::node_info(n));
 
                 TRTORCH_CHECK(mul, "Unable to create mul layer from node: " << *n);
 
diff --git a/tests/accuracy/accuracy_test.h b/tests/accuracy/accuracy_test.h
index 229608de6d..28cfe9cdca 100644
--- a/tests/accuracy/accuracy_test.h
+++ b/tests/accuracy/accuracy_test.h
@@ -20,6 +20,7 @@ class AccuracyTests
             std::cerr << "error loading the model\n";
             return;
         }
+        mod.eval();
     }
 
     void TearDown() {
diff --git a/tests/accuracy/test_fp16_accuracy.cpp b/tests/accuracy/test_fp16_accuracy.cpp
index 7ebcc8b0fb..6de40a6c31 100644
--- a/tests/accuracy/test_fp16_accuracy.cpp
+++ b/tests/accuracy/test_fp16_accuracy.cpp
@@ -24,7 +24,7 @@ TEST_P(AccuracyTests, FP16AccuracyIsClose) {
         jit_total += targets.sizes()[0];
         jit_correct += torch::sum(torch::eq(predictions, targets));
     }
-    torch::Tensor jit_accuracy = jit_correct / jit_total;
+    torch::Tensor jit_accuracy = (jit_correct / jit_total) * 100;
 
     std::vector<std::vector<int64_t>> input_shape = {{32, 3, 32, 32}};
     auto extra_info = trtorch::ExtraInfo({input_shape});
@@ -45,7 +45,7 @@ TEST_P(AccuracyTests, FP16AccuracyIsClose) {
         trt_correct += torch::sum(torch::eq(predictions, targets));
     }
 
-    torch::Tensor trt_accuracy = trt_correct / trt_total;
+    torch::Tensor trt_accuracy = (trt_correct / trt_total) * 100;
 
     ASSERT_TRUE(trtorch::tests::util::almostEqual(jit_accuracy, trt_accuracy, 3));
 }
diff --git a/tests/accuracy/test_fp32_accuracy.cpp b/tests/accuracy/test_fp32_accuracy.cpp
index b014340e82..d3d8bddb96 100644
--- a/tests/accuracy/test_fp32_accuracy.cpp
+++ b/tests/accuracy/test_fp32_accuracy.cpp
@@ -24,7 +24,7 @@ TEST_P(AccuracyTests, FP16AccuracyIsClose) {
         jit_total += targets.sizes()[0];
         jit_correct += torch::sum(torch::eq(predictions, targets));
     }
-    torch::Tensor jit_accuracy = jit_correct / jit_total;
+    torch::Tensor jit_accuracy = (jit_correct / jit_total) * 100;
 
     std::vector<std::vector<int64_t>> input_shape = {{32, 3, 32, 32}};
     auto extra_info = trtorch::ExtraInfo({input_shape});
@@ -45,7 +45,7 @@ TEST_P(AccuracyTests, FP16AccuracyIsClose) {
         trt_correct += torch::sum(torch::eq(predictions, targets));
     }
 
-    torch::Tensor trt_accuracy = trt_correct / trt_total;
+    torch::Tensor trt_accuracy = (trt_correct / trt_total) * 100;
 
     ASSERT_TRUE(trtorch::tests::util::almostEqual(jit_accuracy, trt_accuracy, 3));
 }
diff --git a/tests/accuracy/test_int8_accuracy.cpp b/tests/accuracy/test_int8_accuracy.cpp
index 07d399c96d..aa4824948a 100644
--- a/tests/accuracy/test_int8_accuracy.cpp
+++ b/tests/accuracy/test_int8_accuracy.cpp
@@ -54,7 +54,7 @@ TEST_P(AccuracyTests, FP16AccuracyIsClose) {
         jit_total += targets.sizes()[0];
         jit_correct += torch::sum(torch::eq(predictions, targets));
     }
-    torch::Tensor jit_accuracy = jit_correct / jit_total;
+    torch::Tensor jit_accuracy = (jit_correct / jit_total) * 100;
 
     // Compile Graph
     auto trt_mod = trtorch::CompileGraph(mod, extra_info);
@@ -72,7 +72,7 @@ TEST_P(AccuracyTests, FP16AccuracyIsClose) {
         trt_total += targets.sizes()[0];
         trt_correct += torch::sum(torch::eq(predictions, targets)).item().toFloat();
     }
-    torch::Tensor trt_accuracy = trt_correct / trt_total;
+    torch::Tensor trt_accuracy = (trt_correct / trt_total) * 100;
 
     ASSERT_TRUE(trtorch::tests::util::almostEqual(jit_accuracy, trt_accuracy, 3));
 }