修改了 示例代码错误 (#36041) (#36089)

python/paddle/fluid/data.py

@@ -73,8 +73,10 @@ def data(name, shape, dtype='float32', lod_level=0):
     Examples:
         .. code-block:: python
 
+          import paddle
           import paddle.fluid as fluid
           import numpy as np
+          paddle.enable_static()
 
           # Creates a variable with fixed size [3, 2, 1]
           # User can only feed data of the same shape to x

python/paddle/fluid/dataset.py

@@ -727,6 +727,7 @@ class InMemoryDataset(DatasetBase):
         Examples:
             .. code-block:: python
 
+              # required: skiptest
               import paddle.fluid as fluid
               dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
               filelist = ["a.txt", "b.txt"]
@@ -753,6 +754,7 @@ class InMemoryDataset(DatasetBase):
         Examples:
             .. code-block:: python
 
+              # required: skiptest
               import paddle.fluid as fluid
               dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
               filelist = ["a.txt", "b.txt"]
@@ -777,6 +779,7 @@ class InMemoryDataset(DatasetBase):
         Examples:
             .. code-block:: python
 
+              # required: skiptest
               import paddle.fluid as fluid
               dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
               filelist = ["a.txt", "b.txt"]
@@ -797,6 +800,7 @@ class InMemoryDataset(DatasetBase):
         Examples:
             .. code-block:: python
 
+              # required: skiptest
               import paddle.fluid as fluid
               dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
               filelist = ["a.txt", "b.txt"]
@@ -819,6 +823,7 @@ class InMemoryDataset(DatasetBase):
         Examples:
             .. code-block:: python
 
+              # required: skiptest
               import paddle.fluid as fluid
               from paddle.fluid.incubate.fleet.parameter_server.pslib import fleet
               dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
@@ -866,6 +871,7 @@ class InMemoryDataset(DatasetBase):
         Examples:
             .. code-block:: python
 
+              # required: skiptest
               import paddle.fluid as fluid
               from paddle.fluid.incubate.fleet.parameter_server.pslib import fleet
               dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
@@ -925,6 +931,7 @@ class InMemoryDataset(DatasetBase):
         Examples:
             .. code-block:: python
 
+              # required: skiptest
               import paddle.fluid as fluid
               from paddle.fluid.incubate.fleet.parameter_server.pslib import fleet
               dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
@@ -965,6 +972,7 @@ class InMemoryDataset(DatasetBase):
         Examples:
             .. code-block:: python
 
+              # required: skiptest
               import paddle.fluid as fluid
               from paddle.fluid.incubate.fleet.parameter_server.pslib import fleet
               dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")

python/paddle/fluid/input.py

@@ -98,7 +98,10 @@ def one_hot(input, depth, allow_out_of_range=False):
     Examples:
         .. code-block:: python
 
+            import paddle
             import paddle.fluid as fluid
+            paddle.enable_static()
+
             # Correspond to the first example above, where label.shape is 4 and one_hot_label.shape is [4, 4].
             label = fluid.data(name="label", shape=[4], dtype="int64")
             one_hot_label = fluid.one_hot(input=label, depth=4)

python/paddle/fluid/layers/nn.py

@@ -4521,7 +4521,10 @@ def reduce_mean(input, dim=None, keep_dim=False, name=None):
     Examples:
         .. code-block:: python
 
+            import paddle
             import paddle.fluid as fluid
+            paddle.enable_static()
+
             # x is a Tensor variable with following elements:
             #    [[0.2, 0.3, 0.5, 0.9]
             #     [0.1, 0.2, 0.6, 0.7]]
@@ -5160,7 +5163,10 @@ def matmul(x, y, transpose_x=False, transpose_y=False, alpha=1.0, name=None):
             # x: [M], y: [N]
             # fluid.layers.matmul(x, y, True, True)  # out: [M, N]
 
+            import paddle
             import paddle.fluid as fluid
+            paddle.enable_static()
+
             x = fluid.layers.data(name='x', shape=[2, 3], dtype='float32')
             y = fluid.layers.data(name='y', shape=[3, 2], dtype='float32')
             out = fluid.layers.matmul(x, y, True, True)
@@ -5999,7 +6005,10 @@ def one_hot(input, depth, allow_out_of_range=False):
     Examples:
         .. code-block:: python
 
+            import paddle
             import paddle.fluid as fluid
+            paddle.enable_static()
+
             # Correspond to the first example above, where label.shape is [4, 1] and one_hot_label.shape is [4, 4].
             label = fluid.data(name="label", shape=[4, 1], dtype="int64")
             one_hot_label = fluid.layers.one_hot(input=label, depth=4)
@@ -8363,7 +8372,10 @@ def gather(input, index, overwrite=True):
 
         .. code-block:: python
 
+            import paddle
             import paddle.fluid as fluid
+            paddle.enable_static()
+
             x = fluid.data(name='x', shape=[-1, 5], dtype='float32')
             index = fluid.data(name='index', shape=[-1, 1], dtype='int32')
             output = fluid.layers.gather(x, index)
@@ -8453,7 +8465,10 @@ def gather_nd(input, index, name=None):
 
         .. code-block:: python
 
+            import paddle
             import paddle.fluid as fluid
+            paddle.enable_static()
+
             x = fluid.data(name='x', shape=[3, 4, 5], dtype='float32')
             index = fluid.data(name='index', shape=[2, 2], dtype='int32')
             output = fluid.layers.gather_nd(x, index)
@@ -8488,6 +8503,7 @@ def scatter(input, index, updates, name=None, overwrite=True):
     Output is obtained by updating the input on selected indices based on updates.
 
     .. code-block:: python
+
         import numpy as np
 
         #input:
@@ -8529,8 +8545,10 @@ def scatter(input, index, updates, name=None, overwrite=True):
 
         .. code-block:: python
 
+            import paddle
             import numpy as np
             import paddle.fluid as fluid
+            paddle.enable_static()
 
             input = fluid.layers.data(name='data', shape=[3, 2], dtype='float32', append_batch_size=False)
             index = fluid.layers.data(name='index', shape=[4], dtype='int64', append_batch_size=False)
@@ -8871,8 +8889,10 @@ def selu(x, scale=None, alpha=None, name=None):
 
         .. code-block:: python
 
+            import paddle
             import paddle.fluid as fluid
             import numpy as np
+            paddle.enable_static()
 
             inputs = fluid.layers.data(name="x", shape=[2, 2], dtype="float32")
             output = fluid.layers.selu(inputs)
@@ -10480,8 +10500,10 @@ def expand_as(x, target_tensor, name=None):
     Examples:
         .. code-block:: python
 
+            import paddle
             import paddle.fluid as fluid
             import numpy as np
+            paddle.enable_static()
 
             data = fluid.layers.data(name="data", shape=[-1,10], dtype='float64')
             target_tensor = fluid.layers.data(
@@ -10576,7 +10598,9 @@ def uniform_random_batch_size_like(input,
     Examples:
         .. code-block:: python
 
+            import paddle
             import paddle.fluid as fluid
+            paddle.enable_static()
 
             # example 1:
             input = fluid.data(name="input", shape=[1, 3], dtype='float32')
@@ -10649,7 +10673,9 @@ def gaussian_random(shape,
     Examples:
        .. code-block:: python
 
+            import paddle
             import paddle.fluid as fluid
+            paddle.enable_static()
 
             # example 1:
             # attr shape is a list which doesn't contain Tensor.
@@ -10678,6 +10704,7 @@ def gaussian_random(shape,
        .. code-block:: python
        
            # declarative mode
+           # required: skiptest
            import numpy as np
            from paddle import fluid
    
@@ -10816,7 +10843,10 @@ def gaussian_random_batch_size_like(input,
     Examples:
         .. code-block:: python
 
+            import paddle
             import paddle.fluid as fluid
+            paddle.enable_static()
+
             input = fluid.data(name="input", shape=[13, 11], dtype='float32')
 
             out = fluid.layers.gaussian_random_batch_size_like(
@@ -11422,7 +11452,9 @@ def size(input):
     Examples:
         .. code-block:: python
 
+            import paddle
             import paddle.fluid.layers as layers
+            paddle.enable_static()
 
             input = layers.data(
                 name="input", shape=[3, 100], dtype="float32", append_batch_size=False)
@@ -12525,7 +12557,10 @@ def mean(x, name=None):
     Examples:
         .. code-block:: python
 
+            import paddle
             import paddle.fluid as fluid
+            paddle.enable_static()
+
             input = fluid.layers.data(
                 name='data', shape=[2, 3], dtype='float32')
             mean = fluid.layers.mean(input)
@@ -15195,7 +15230,9 @@ def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0,
     Examples:
         .. code-block:: python
 
+            import paddle
             import paddle.fluid as fluid
+            paddle.enable_static()
 
             # example 1:
             # attr shape is a list which doesn't contain Tensor.

python/paddle/fluid/layers/rnn.py

@@ -2528,18 +2528,21 @@ def lstm(input,
     Examples:
         .. code-block:: python
             
+            import paddle
             import paddle.fluid as fluid
             import paddle.fluid.layers as layers
+            paddle.enable_static()
 
             emb_dim = 256
             vocab_size = 10000
             data = fluid.data(name='x', shape=[None, 100], dtype='int64')
             emb = fluid.embedding(input=data, size=[vocab_size, emb_dim], is_sparse=True)
-            batch_size = 20
+            batch_size = 100
             dropout_prob = 0.2
             input_size = 100
             hidden_size = 150
             num_layers = 1
+            max_len = 12
             init_h = layers.fill_constant( [num_layers, batch_size, hidden_size], 'float32', 0.0 )
             init_c = layers.fill_constant( [num_layers, batch_size, hidden_size], 'float32', 0.0 )
             rnn_out, last_h, last_c = layers.lstm( emb, init_h, init_c, \