diff --git a/ivy/functional/backends/tensorflow/creation.py b/ivy/functional/backends/tensorflow/creation.py
index 4ee6f22cced5c..05f3487191575 100644
--- a/ivy/functional/backends/tensorflow/creation.py
+++ b/ivy/functional/backends/tensorflow/creation.py
@@ -93,6 +93,9 @@ def asarray(
     try:
         ret = tf.convert_to_tensor(obj, dtype)
     except (TypeError, ValueError):
+        obj = (
+            obj if isinstance(obj, tf.Tensor) else tf.convert_to_tensor(obj, tf.float64)
+        )
         ret = tf.cast(obj, dtype)
     return tf.identity(ret) if copy else ret
 
diff --git a/ivy/functional/frontends/numpy/mathematical_functions/sums_products_differences.py b/ivy/functional/frontends/numpy/mathematical_functions/sums_products_differences.py
index 361a55c5c0dd0..a863284396fd9 100644
--- a/ivy/functional/frontends/numpy/mathematical_functions/sums_products_differences.py
+++ b/ivy/functional/frontends/numpy/mathematical_functions/sums_products_differences.py
@@ -8,6 +8,7 @@
     from_zero_dim_arrays_to_scalar,
     handle_numpy_out,
 )
+import ivy.functional.frontends.numpy as np_frontend
 
 
 @handle_numpy_out
@@ -114,9 +115,10 @@ def prod(
     initial=None,
     where=True,
 ):
-    if ivy.is_array(where):
+    if where is not True:
         x = ivy.where(where, x, ivy.default(out, ivy.ones_like(x)), out=out)
     if initial is not None:
+        initial = np_frontend.array(initial, dtype=dtype).tolist()
         if axis is not None:
             s = ivy.to_list(ivy.shape(x, as_array=True))
             s[axis] = 1