modify some initializer api (#17301)

* test=develop modify some initializer api

* test=develop modify API.spec

* test=develop modify API.spec

* test=develop modify API.spec

* test=develop modify API.spec

paddle/fluid/API.spec

@@ -68,8 +68,8 @@ paddle.fluid.initializer.TruncatedNormalInitializer.__init__ (ArgSpec(args=['sel
 paddle.fluid.initializer.XavierInitializer.__init__ (ArgSpec(args=['self', 'uniform', 'fan_in', 'fan_out', 'seed'], varargs=None, keywords=None, defaults=(True, None, None, 0)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
 paddle.fluid.initializer.BilinearInitializer.__init__ (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'd389912dc079cbef432335a00017cec0'))
 paddle.fluid.initializer.MSRAInitializer.__init__ (ArgSpec(args=['self', 'uniform', 'fan_in', 'seed'], varargs=None, keywords=None, defaults=(True, None, 0)), ('document', '53c757bed9345f2ad3361902531e7cf5'))
-paddle.fluid.initializer.force_init_on_cpu (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', '6d0f3e22c90d9d500d36ff57daf056ee'))
-paddle.fluid.initializer.init_on_cpu (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', 'a6d7011ca3d8c0d454dac3a56eae0c29'))
+paddle.fluid.initializer.force_init_on_cpu (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', '2c6748c1dd1d85f800462869ea7a747f'))
+paddle.fluid.initializer.init_on_cpu (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', '280b581f5a77e746e47decbc57a7b30a'))
 paddle.fluid.initializer.NumpyArrayInitializer.__init__ (ArgSpec(args=['self', 'value'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
 paddle.fluid.layers.fc (ArgSpec(args=['input', 'size', 'num_flatten_dims', 'param_attr', 'bias_attr', 'act', 'is_test', 'name'], varargs=None, keywords=None, defaults=(1, None, None, None, False, None)), ('document', '424e898365195e3ccbc2e7dc8b63605e'))
 paddle.fluid.layers.embedding (ArgSpec(args=['input', 'size', 'is_sparse', 'is_distributed', 'padding_idx', 'param_attr', 'dtype'], varargs=None, keywords=None, defaults=(False, False, None, None, 'float32')), ('document', '6f9f96d2a1517cd1affebc960c3526f7'))

python/paddle/fluid/initializer.py

@@ -42,8 +42,9 @@ def force_init_on_cpu():
 
         .. code-block:: python
 
-            if force_init_on_cpu():
-                create_op('force_cpu': force_init_on_cpu())
+	    if fluid.initializer.force_init_on_cpu():
+    		step = fluid.layers.create_global_var(
+        	    shape=[2,3], value=1.0, dtype='float32')
 
     """
     return _force_init_on_cpu_
@@ -57,8 +58,9 @@ def init_on_cpu():
     Examples:
         .. code-block:: python
 
-            with init_on_cpu():
-                step = layers.create_global_var()
+	    with fluid.initializer.init_on_cpu():
+    		step = fluid.layers.create_global_var(
+        	    shape=[2,3], value=1.0, dtype='float32')
 
     """
     global _force_init_on_cpu_
@@ -131,8 +133,10 @@ class ConstantInitializer(Initializer):
     Examples:
         .. code-block:: python
 
-            fc = fluid.layers.fc(input=x, size=10,
-                param_attr=fluid.initializer.Constant(value=2.0))
+    	    x = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
+	    fc = fluid.layers.fc(input=x, size=10,
+    		param_attr=fluid.initializer.Constant(value=2.0))
+
     """
 
     def __init__(self, value=0.0, force_cpu=False):
@@ -208,7 +212,7 @@ class UniformInitializer(Initializer):
             import paddle.fluid as fluid
             x = fluid.layers.data(name='x', shape=[1], dtype='float32')
             fc = fluid.layers.fc(input=x, size=10,
-                param_attr=fluid.initializer.Uniform(low=-0.5, high=0.5))
+    		param_attr=fluid.initializer.Uniform(low=-0.5, high=0.5))
     """
 
     def __init__(self, low=-1.0, high=1.0, seed=0):
@@ -288,8 +292,10 @@ class NormalInitializer(Initializer):
     Examples:
         .. code-block:: python
 
-            fc = fluid.layers.fc(input=x, size=10,
-                param_attr=fluid.initializer.Normal(loc=0.0, scale=2.0))
+	    x = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
+	    fc = fluid.layers.fc(input=x, size=10,
+    		param_attr=fluid.initializer.Normal(loc=0.0, scale=2.0))
+
     """
 
     def __init__(self, loc=0.0, scale=1.0, seed=0):
@@ -601,10 +607,11 @@ class MSRAInitializer(Initializer):
 
     Examples:
         .. code-block:: python
+		
+	    x = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
+	    fc = fluid.layers.fc(input=x, size=10,
+    		param_attr=fluid.initializer.MSRA(uniform=False))
 
-            fc = fluid.layers.fc(
-                input=queries, size=10,
-                param_attr=fluid.initializer.MSRA(uniform=False))
     """
 
     def __init__(self, uniform=True, fan_in=None, seed=0):
@@ -703,19 +710,24 @@ class BilinearInitializer(Initializer):
 
         .. code-block:: python
 
-            factor = 2
-            w_attr = ParamAttr(learning_rate=0., regularizer=L2Decay(0.),
-                               initializer=Bilinear())
-            conv_up = fluid.layers.conv2d_transpose(
-                input,
-                num_filters=C,
-                output_size=None,
-                filter_size=2 * factor - factor % 2,
-                padding=ceil((factor - 1) / 2.),
-                stride=factor,
-                groups=C,
-                param_attr=w_attr,
-                bias_attr=False)
+	    factor = 2
+	    C = 2
+	    w_attr = fluid.initializer.ParamAttr(
+		learning_rate=0., 
+		regularizer=fluid.regularizer.L2Decay(0.),
+                initializer=fluid.initializer.Bilinear())
+	    x = fluid.layers.data(name="data", shape=[3, 32, 32], 
+				  dtype="float32")
+	    conv_up = fluid.layers.conv2d_transpose(
+    		input=x,
+    		num_filters=C,
+    		output_size=None,
+    		filter_size=2 * factor - factor % 2,
+    		padding=int(math.ceil((factor - 1) / 2.)),
+    		stride=factor,
+    		groups=C,
+    		param_attr=w_attr,
+    		bias_attr=False)
 
     Where, `num_filters=C` and `groups=C` means this is channel-wise transposed
     convolution. The filter shape will be (C, 1, K, K) where K is `filer_size`,