diff --git a/tests/ttnn/unit_tests/operations/test_conv1d.py b/tests/ttnn/unit_tests/operations/test_conv1d.py
index 7013ef6b2db..d5f08d602bb 100644
--- a/tests/ttnn/unit_tests/operations/test_conv1d.py
+++ b/tests/ttnn/unit_tests/operations/test_conv1d.py
@@ -129,6 +129,15 @@ def run_conv(
     )
 
     tt_output_tensor = ttnn.from_device(tt_output_tensor_on_device)
+    tt_output_tensor = ttnn.reshape(
+        tt_output_tensor,
+        [
+            1,
+            1,
+            batch_size * out_length,
+            output_channels,
+        ],
+    )
     torch_output_tensor = torch.Tensor(ttnn.to_torch(tt_output_tensor))
 
     # torch_output_tensor is in row major layout and NLC shape
diff --git a/tests/ttnn/unit_tests/operations/test_new_conv2d.py b/tests/ttnn/unit_tests/operations/test_new_conv2d.py
index 37b25e6291b..eb9256cc5bf 100644
--- a/tests/ttnn/unit_tests/operations/test_new_conv2d.py
+++ b/tests/ttnn/unit_tests/operations/test_new_conv2d.py
@@ -44,13 +44,14 @@ def _nearest_32(x):
 def write_to_file(file_name, data):
     data = data.cpu().numpy()
     with open(file_name, "w") as f:
-        for i in range(1):
+        for i in range(data.shape[0]):
             for j in range(data.shape[2]):
                 for k in range(data.shape[3]):
                     for l in range(data.shape[1]):
                         f.write(str(data[i][l][j][k]) + " ")
                     f.write("\n")
                 f.write("\n")
+            f.write("\n")
 
 
 def write_to_file_special(file_name, data):
@@ -59,7 +60,7 @@ def write_to_file_special(file_name, data):
         for i in range(data.shape[0]):
             for j in range(data.shape[1]):
                 for k in range(data.shape[2]):
-                    for l in range(16):
+                    for l in range(data.shape[3]):
                         f.write(str(data[i][j][k][l]) + " ")
                     f.write("\n")
 
@@ -248,6 +249,8 @@ def run_conv(
     else:
         pcc = 0.997
 
+    # write_to_file("golden_tensor.txt", torch_out_golden_tensor.float())
+    # write_to_file("output_tensor_1.txt", torch_output_tensor.float())
     passing, pcc_msg = check_with_pcc_without_tensor_printout(torch_output_tensor, torch_out_golden_tensor, pcc=pcc)
     logger.info(f"PCC = {pcc_msg}. Threshold = {pcc}")
     assert passing
diff --git a/ttnn/cpp/ttnn/operations/conv/conv2d/device/conv2d_op.cpp b/ttnn/cpp/ttnn/operations/conv/conv2d/device/conv2d_op.cpp
index b72912b82db..60f4f47b725 100644
--- a/ttnn/cpp/ttnn/operations/conv/conv2d/device/conv2d_op.cpp
+++ b/ttnn/cpp/ttnn/operations/conv/conv2d/device/conv2d_op.cpp
@@ -217,6 +217,9 @@ std::vector<TensorSpec> OptimizedConvNew::compute_output_specs(const std::vector
     auto output_padding = Padding(
         {{0, 0}, {0, 0}, {0, 0}, {0, (padded_shape_c - shape_c)}}, Padding::PadValue::Zero);
     auto output_shape = tt::tt_metal::LegacyShape({batch_size, conv_output_h, conv_output_w, padded_shape_c}, output_padding);
+    if(conv_output_w == 1){
+        output_shape = tt::tt_metal::LegacyShape({batch_size, conv_output_w, conv_output_h, padded_shape_c}, output_padding); //handing conv1d transpose.
+    }
 
     auto output_layout = this->untilize_out ? Layout::ROW_MAJOR : Layout::TILE;
     if (this->memory_config.is_sharded()) {
diff --git a/ttnn/cpp/ttnn/tensor/tensor_impl.cpp b/ttnn/cpp/ttnn/tensor/tensor_impl.cpp
index f1f02c13bb4..987dcd55b85 100644
--- a/ttnn/cpp/ttnn/tensor/tensor_impl.cpp
+++ b/ttnn/cpp/ttnn/tensor/tensor_impl.cpp
@@ -18,7 +18,7 @@ namespace tt_metal {
 
 namespace tensor_impl {
 
-TensorPrintProfile TTNN_TENSOR_PRINT_PROFILE = TensorPrintProfile::Full;
+TensorPrintProfile TTNN_TENSOR_PRINT_PROFILE = TensorPrintProfile::Short;
 
 std::ostream& operator<<(std::ostream& os, const DataType& dtype) {
     switch (dtype) {