diff --git a/ethosu/vela/graph_optimiser.py b/ethosu/vela/graph_optimiser.py
index 35932d4..4f473dd 100644
--- a/ethosu/vela/graph_optimiser.py
+++ b/ethosu/vela/graph_optimiser.py
@@ -333,7 +333,7 @@
                 # There is a preceding Reshape
                 # Compare input of prev_op and input of op, to see if prev_op can be removed
                 ifm_prev_op = prev_op.inputs[0]
-                if ifm_prev_op.shape == ifm.shape and check_quantized_tens_scaling_equal(ifm_prev_op, ifm.quantization):
+                if ifm_prev_op.shape == ifm.shape and check_quantized_tens_scaling_equal(ifm_prev_op, ifm):
                     # prev_op can be removed
                     op.set_input_tensor(ifm_prev_op, 0)
                 else:
diff --git a/ethosu/vela/tensor.py b/ethosu/vela/tensor.py
index 0d299e1..3601c92 100644
--- a/ethosu/vela/tensor.py
+++ b/ethosu/vela/tensor.py
@@ -236,11 +236,9 @@
         # it implies that the tensor it belongs to is not quantised. otherwise,
         # it depends upon whether the scale and zero point are equal
 
-        if other is None:
+        if not isinstance(other, QuantizationParameters):
             return False
 
-        assert isinstance(other, QuantizationParameters)
-
         return self.scale_f32 == other.scale_f32 and self.zero_point == other.zero_point
 
     def is_valid(self):
@@ -793,13 +791,10 @@
     def is_quantized(self):
         # a tensor is quantized if it has an integral type and it contains valid quantization params
 
-        if (self.dtype.type & BaseType.Int) == 0 or self.quantization is None:
+        if not isinstance(self.quantization, QuantizationParameters):
             return False
 
-        assert isinstance(self.quantization, QuantizationParameters)
-        assert self.quantization.is_valid()
-
-        return True
+        return (self.dtype.type & BaseType.Int) != 0 and self.quantization.is_valid()
 
     def __str__(self):
         return "<nng.Tensor '%s' shape=%s dtype=%s>" % (self.name, self.shape, self.dtype)
@@ -807,16 +802,7 @@
     __repr__ = __str__
 
 
-def check_tens_quantized(tens):
-    # checks that a tensor is quantized
-
-    return isinstance(tens, Tensor) and tens.is_quantized()
-
-
 def check_quantized_tens_scaling_equal(tens_a, tens_b):
     # checks that the scaling of two quantized tensors are equal
 
-    assert check_tens_quantized(tens_a)
-    assert check_tens_quantized(tens_b)
-
-    return tens_a.quantization.is_scaling_equal(tens_b.quantization)
+    return tens_a.is_quantized() and tens_b.is_quantized() and tens_a.quantization.is_scaling_equal(tens_b.quantization)