diff --git a/test/py/param_test.py b/test/py/param_test.py
index 93d9203..fa4239a 100644
--- a/test/py/param_test.py
+++ b/test/py/param_test.py
@@ -155,18 +155,6 @@ def compile(c):
     return c
 
 
-@dataclass
-class Config:
-    K : int
-    CO: int
-    is_bias: bool
-    act_q: str
-    strides: (int,int) = (1,1)
-    pool_d: dict = None
-    flatten: bool = False
-    dense: bool = False
-
-
 @pytest.mark.parametrize("COMPILE", list(product_dict(
                                                 X_BITS     = [4    ], 
                                                 K_BITS     = [4    ], 
@@ -190,37 +178,25 @@ class Config:
                                             )))
 def test_dnn_engine(COMPILE):
     c = make_compile_params(COMPILE)
-
-    input_shape = (8,18,18,3) # (XN, XH, XW, CI)
-    model_config = [
-        Config(11, 8, True , f'quantized_relu({c.X_BITS},0,negative_slope=0)', (2,1), pool_d={'type':'max', 'size':(3,4), 'strides':(2,3), 'padding':'same', 'act_str':f'quantized_bits({c.X_BITS},0,False,False,1)'}),
-        Config(1 , 8, False, f'quantized_bits({c.X_BITS},0,False,False,1)'),
-        Config(7 , 8, True , f'quantized_bits({c.X_BITS},0,False,True,1)'),
-        Config(5 , 8, False, f'quantized_relu({c.X_BITS},0,negative_slope=0.125)'),
-        Config(3 , 24, True , f'quantized_relu({c.X_BITS},0,negative_slope=0)'),
-        Config(1 , 10 , False, f'quantized_relu({c.X_BITS},0,negative_slope=0.125)', flatten=True),
-        Config(1 , 10, True , f'quantized_relu({c.X_BITS},0,negative_slope=0.125)', dense= True),
-    ]
-
-    '''
-    Build Model
-    '''
     assert c.X_BITS in [1,2,4,8] and c.K_BITS in [1,2,4,8], "X_BITS and K_BITS should be in [1,2,4,8]"
     assert c.B_BITS in [8,16,32], "B_BITS should be in [8,16,32]"
     xq, kq, bq = f'quantized_bits({c.X_BITS},0,False,True,1)', f'quantized_bits({c.K_BITS},0,False,True,1)', f'quantized_bits({c.B_BITS},0,False,True,1)'
-    inp = {'bits':c.X_BITS, 'frac':c.X_BITS-1}
+    inp        = {'bits':c.X_BITS, 'frac':c.X_BITS-1}
 
+    '''
+    Build Model
+    '''
+    input_shape = (1,18,18,3) # (XN, XH, XW, CI)
     x = x_in = Input(input_shape[1:], name='input')
     x = QActivation(xq)(x)
-    for i, g in enumerate(model_config):
-        if g.dense:
-            d = {'core': {'type':'dense', 'units':g.CO, 'kernel_quantizer':kq, 'bias_quantizer':bq, 'use_bias':g.is_bias, 'act_str':g.act_q}}
-        else:
-            d = {
-                'core': {'type':'conv', 'filters':g.CO, 'kernel_size':(g.K,g.K), 'strides':g.strides, 'padding':'same', 'kernel_quantizer':kq, 'bias_quantizer':bq, 'use_bias':g.is_bias, 'act_str':g.act_q},
-                'pool': g.pool_d, 'flatten':g.flatten,
-                }
-        x = Bundle(**d)(x)
+
+    x = Bundle( core= {'type':'conv' , 'filters':8 , 'kernel_size':(11,11), 'strides':(2,1), 'padding':'same', 'kernel_quantizer':kq, 'bias_quantizer':bq, 'use_bias':True , 'act_str':f'quantized_relu({c.X_BITS},0,negative_slope=0)'    }, pool= {'type':'max', 'size':(3,4), 'strides':(2,3), 'padding':'same', 'act_str':f'quantized_bits({c.X_BITS},0,False,False,1)'})(x)
+    x = Bundle( core= {'type':'conv' , 'filters':8 , 'kernel_size':( 1, 1), 'strides':(1,1), 'padding':'same', 'kernel_quantizer':kq, 'bias_quantizer':bq, 'use_bias':True , 'act_str':f'quantized_bits({c.X_BITS},0,False,False,1)'       },)(x)
+    x = Bundle( core= {'type':'conv' , 'filters':8 , 'kernel_size':( 7, 7), 'strides':(1,1), 'padding':'same', 'kernel_quantizer':kq, 'bias_quantizer':bq, 'use_bias':False, 'act_str':f'quantized_bits({c.X_BITS},0,False,True,1)'        },)(x)
+    x = Bundle( core= {'type':'conv' , 'filters':8 , 'kernel_size':( 5, 5), 'strides':(1,1), 'padding':'same', 'kernel_quantizer':kq, 'bias_quantizer':bq, 'use_bias':True , 'act_str':f'quantized_relu({c.X_BITS},0,negative_slope=0.125)'},)(x)
+    x = Bundle( core= {'type':'conv' , 'filters':24, 'kernel_size':( 3, 3), 'strides':(1,1), 'padding':'same', 'kernel_quantizer':kq, 'bias_quantizer':bq, 'use_bias':True , 'act_str':f'quantized_relu({c.X_BITS},0,negative_slope=0)'    },)(x)
+    x = Bundle( core= {'type':'conv' , 'filters':10, 'kernel_size':( 1, 1), 'strides':(1,1), 'padding':'same', 'kernel_quantizer':kq, 'bias_quantizer':bq, 'use_bias':True , 'act_str':f'quantized_relu({c.X_BITS},0,negative_slope=0.125)'}, flatten= True)(x)
+    x = Bundle( core= {'type':'dense', 'units'  :10,                                                           'kernel_quantizer':kq, 'bias_quantizer':bq, 'use_bias':True , 'act_str':f'quantized_relu({c.X_BITS},0,negative_slope=0.125)'})(x)
 
     model = Model(inputs=x_in, outputs=x)