add type compatible check for ParamAttr (#113)

* add type compatible check for ParamAttr

python/paddle/trainer_config_helpers/attrs.py

@@ -17,6 +17,42 @@ __all__ = ['ParamAttr', 'ExtraAttr', 'ParameterAttribute',
            'ExtraLayerAttribute']
 
 
+def convert_and_compare(x, Type):
+    """                                                                                                                                                                                                
+    Convert x to be the same type as Type and then convert back to                                                                                                                                      
+    check whether there is a loss of information                                                                                                                                                        
+    :param x: object to be checked                                                                                                                                                                      
+    :param Type: target type to check x over                                                                                                                                                           
+    
+    """
+    return type(x)(Type(x))==x
+
+def is_compatible_with(x, Type):
+    """                                                                                                                                                                                                
+    Check if x has a type compatible with Type                                                                                                                                                         
+    :param x: object to be checked                                                                                                                                                                     
+    :param Type: target type to check x over                                                                                                                                                           
+    
+    """
+    if type(x) == Type:
+        return True
+    try:
+        if float == Type or int == Type:
+        # avoid those types that can be converted to float/int but not very                                                                                                                            
+        # meaningful and  could potentially lead to error                                                                                                                                              
+        # i.e., str and bool typed value should not be used for initializing float/int variable                                                                                                        
+            if not isinstance(x, str) and not isinstance(x, bool):
+                return convert_and_compare(x, Type)
+        elif bool == Type:
+            # should not use string type to initialize bool variable                                                                                                                                   
+            if not isinstance(x, str):
+                return convert_and_compare(x, Type)
+        else:
+            return False
+    except:
+        return False
+
+
 class ParameterAttribute(object):
     """
     Parameter Attributes object. To fine-tuning network training process, user
@@ -65,14 +101,18 @@ class ParameterAttribute(object):
         elif initial_std is None and initial_mean is None and initial_max \
                 is None and initial_min is None:
             self.attr = {'initial_smart': True}
-        elif isinstance(initial_std, float) or isinstance(initial_mean, float):
+        elif is_compatible_with(initial_std, float) or \
+             is_compatible_with(initial_mean, float):
             self.attr = dict()
             if initial_std is not None:
                 self.attr['initial_std'] = initial_std
             if initial_mean is not None:
                 self.attr['initial_mean'] = initial_mean
             self.attr['initial_strategy'] = 0  # Gauss Random
-        elif isinstance(initial_max, float) and isinstance(initial_min, float):
+        elif is_compatible_with(initial_max, float) and \
+             is_compatible_with(initial_min, float):
+            initial_max = initial_max
+            initial_min = initial_min
             assert initial_min < initial_max
             initial_mean = (initial_max + initial_min) / 2
             initial_std = initial_mean - initial_min
@@ -83,16 +123,16 @@ class ParameterAttribute(object):
         else:
             raise RuntimeError("Unexpected branch.")
 
-        if not is_static and isinstance(l1_rate, float):
+        if not is_static and is_compatible_with(l1_rate, float):
             self.attr['decay_rate_l1'] = l1_rate
 
-        if not is_static and isinstance(l2_rate, float):
+        if not is_static and is_compatible_with(l2_rate, float):
             self.attr['decay_rate'] = l2_rate
 
-        if not is_static and isinstance(learning_rate, float):
+        if not is_static and is_compatible_with(learning_rate, float):
             self.attr['learning_rate'] = learning_rate
 
-        if not is_static and isinstance(momentum, float):
+        if not is_static and is_compatible_with(momentum, float):
             self.attr['momentum'] = momentum
 
         if name is not None:

python/paddle/trainer_config_helpers/tests/layers_test_config.py

@@ -39,10 +39,20 @@ print_layer(input=[out])
 outputs(classification_cost(out, data_layer(name="label", size=num_classes)))
 
 dotmul = mixed_layer(input=[dotmul_operator(x=x1, y=y1),
-	                        dotmul_projection(input=y1)])
+                            dotmul_projection(input=y1)])
+
+proj_with_attr_init = mixed_layer(input=full_matrix_projection(input=y1,
+                                                               param_attr=ParamAttr(learning_rate = 0,
+                                                                                 initial_mean = 0,
+                                                                                 initial_std = 0)),
+                               bias_attr = ParamAttr(initial_mean=0, initial_std=0, learning_rate=0),
+                               act = LinearActivation(),
+                               size = 5,
+                               name='proj_with_attr_init')
+
 
 # for ctc
-tmp = fc_layer(input=[x1, dotmul],
+tmp = fc_layer(input=[x1, dotmul, proj_with_attr_init],
                size=num_classes + 1,
                act=SoftmaxActivation())
 ctc = ctc_layer(input=tmp,