update the design of variable

doc/design/var_desc.md

@@ -47,7 +47,7 @@ message LoDTensorDesc {
 
 ## Definition of Variable in Python
 
-In Python API, layer will take Variable as Input, and return Variable as Output.
+In Python API, layer will take Variable as Input, and return Variable as Output. There should be a class `Variable` in python to help create and manage Variable.
 
 ```python
 image = Variable(dims=[-1, 640, 480])
@@ -55,27 +55,43 @@ image = Variable(dims=[-1, 640, 480])
 fc1 = layer.fc(input=image, output_size=10)
 fc2 = layer.fc(input=fc1, output_size=20)
 ```
-
-There should be a class `Variable` in python to help create and manage Variable.
+### what should class `Variable` Have
+1. `name`.a name of string type is used to mark the value of the Variable.
+1. `initializer`. Since our Tensor does not have value. we will always use some Operator to fullfill it when run. So we should have a inialize method to help add the init operator.
+1. `operator`. Variable should record which operator produce itself. The reaon is:
+  - we use pd.eval(targets=[var1, var2]) to run the related ops to get the value of var1 and var2. var.op is used to trace the dependency of the current variable.
 
 ```python
 import VarDesc
 import LoDTensorDesc
 import framework
 
+def AddInitialOperator(variable, initializer):
+	# add an initialize Operator to graph to init this Variable
+
 class Variable(object):
-   def __init__(self, name, dims, type):
+   def __init__(self, name, dims, type, initializer):
+      self._graph = get_default_graph()
       self._name = name
       self.op = None
+
       tensor_desc = LoDTensorDesc(data_type=type, dims=dims)
       _var_desc = VarDesc(name=name, lod_tensor=tensor_desc)
       self._var = framework.CreateVar(_var_desc)
+      self._graph.add_var(self)
+
+      # add initial op according to initializer
+      if initializer is not None:
+          AddInitialOperator(self, initializer)
 
    def dims(self):
       return self._var.dims()
 
    def data_type(self):
        return self._var.data_type()
+
+   def to_proto(self):
+       pass
 ```
 
 Then we can use this Variable to create a fc layer in Python.
@@ -90,8 +106,8 @@ def flatten_size(X, num_flatten_dims):
   return prod
 
 def layer.fc(X, output_size, num_flatten_dims):
-  W = Variable(type=FP32, dims=[flatten_size(X, num_flatten_dims), output_size])
-  b = Variable(type=FP32, dims=[output_size])
+  W = Variable(pd.random_uniform(), type=FP32, dims=[flatten_size(X, num_flatten_dims), output_size])
+  b = Variable(pd.random_uniform(), type=FP32, dims=[output_size])
   out = Variable(type=FP32)
   y = operator.fc(X, W, b, output=out) # fc will put fc op input into out
   pd.InferShape(y)