Fix some APIs' example (#17214)

paddle/fluid/API.spec

@@ -77,7 +77,7 @@ paddle.fluid.layers.dynamic_lstmp (ArgSpec(args=['input', 'size', 'proj_size', '
 paddle.fluid.layers.dynamic_gru (ArgSpec(args=['input', 'size', 'param_attr', 'bias_attr', 'is_reverse', 'gate_activation', 'candidate_activation', 'h_0', 'origin_mode'], varargs=None, keywords=None, defaults=(None, None, False, 'sigmoid', 'tanh', None, False)), ('document', '4ec4845fd7d991bcac822f8b0dfc101f'))
 paddle.fluid.layers.gru_unit (ArgSpec(args=['input', 'hidden', 'size', 'param_attr', 'bias_attr', 'activation', 'gate_activation', 'origin_mode'], varargs=None, keywords=None, defaults=(None, None, 'tanh', 'sigmoid', False)), ('document', 'e0e2439f7af069b57badca18a6ba60b8'))
 paddle.fluid.layers.linear_chain_crf (ArgSpec(args=['input', 'label', 'param_attr'], varargs=None, keywords=None, defaults=(None,)), ('document', '7c49ef4bbf0adfd4b9a1d98e2e5f3fea'))
-paddle.fluid.layers.crf_decoding (ArgSpec(args=['input', 'param_attr', 'label'], varargs=None, keywords=None, defaults=(None,)), ('document', '7642373ab65d3fc3b96d16d10fef1538'))
+paddle.fluid.layers.crf_decoding (ArgSpec(args=['input', 'param_attr', 'label'], varargs=None, keywords=None, defaults=(None,)), ('document', '462ddf2435e3392334e0c05ae57a01c4'))
 paddle.fluid.layers.cos_sim (ArgSpec(args=['X', 'Y'], varargs=None, keywords=None, defaults=None), ('document', 'd740824aa7316b807c4b4a3c6c8c0bbe'))
 paddle.fluid.layers.cross_entropy (ArgSpec(args=['input', 'label', 'soft_label', 'ignore_index'], varargs=None, keywords=None, defaults=(False, -100)), ('document', '025b364dafb4b7975c801eb33e7831a1'))
 paddle.fluid.layers.bpr_loss (ArgSpec(args=['input', 'label', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '30add751a0f99347a6257634c03ff254'))
@@ -124,7 +124,7 @@ paddle.fluid.layers.warpctc (ArgSpec(args=['input', 'label', 'blank', 'norm_by_t
 paddle.fluid.layers.sequence_reshape (ArgSpec(args=['input', 'new_dim'], varargs=None, keywords=None, defaults=None), ('document', 'a10ab9bf88d4a7e328882d411abb6fd1'))
 paddle.fluid.layers.transpose (ArgSpec(args=['x', 'perm', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'a1feac48b843d679db82312dc85885f4'))
 paddle.fluid.layers.im2sequence (ArgSpec(args=['input', 'filter_size', 'stride', 'padding', 'input_image_size', 'out_stride', 'name'], varargs=None, keywords=None, defaults=(1, 1, 0, None, 1, None)), ('document', '3ce01160ede80b1c26f776f8fef9340f'))
-paddle.fluid.layers.nce (ArgSpec(args=['input', 'label', 'num_total_classes', 'sample_weight', 'param_attr', 'bias_attr', 'num_neg_samples', 'name', 'sampler', 'custom_dist', 'seed', 'is_sparse'], varargs=None, keywords=None, defaults=(None, None, None, None, None, 'uniform', None, 0, False)), ('document', 'fddad4896dee5193e1cdf70882c2a347'))
+paddle.fluid.layers.nce (ArgSpec(args=['input', 'label', 'num_total_classes', 'sample_weight', 'param_attr', 'bias_attr', 'num_neg_samples', 'name', 'sampler', 'custom_dist', 'seed', 'is_sparse'], varargs=None, keywords=None, defaults=(None, None, None, None, None, 'uniform', None, 0, False)), ('document', '32b3c442da0f3df682b5fcac10468116'))
 paddle.fluid.layers.sampled_softmax_with_cross_entropy (ArgSpec(args=['logits', 'label', 'num_samples', 'num_true', 'remove_accidental_hits', 'use_customized_samples', 'customized_samples', 'customized_probabilities', 'seed'], varargs=None, keywords=None, defaults=(1, True, False, None, None, 0)), ('document', '5db30b8a74e8c93687943a3e8d221da0'))
 paddle.fluid.layers.hsigmoid (ArgSpec(args=['input', 'label', 'num_classes', 'param_attr', 'bias_attr', 'name', 'path_table', 'path_code', 'is_custom', 'is_sparse'], varargs=None, keywords=None, defaults=(None, None, None, None, None, False, False)), ('document', '80641ee6810b1cdc3fd6e14fc89ecc9d'))
 paddle.fluid.layers.beam_search (ArgSpec(args=['pre_ids', 'pre_scores', 'ids', 'scores', 'beam_size', 'end_id', 'level', 'is_accumulated', 'name', 'return_parent_idx'], varargs=None, keywords=None, defaults=(0, True, None, False)), ('document', 'b350b9a30a18e7efd7e1bb740eef6996'))
@@ -135,8 +135,8 @@ paddle.fluid.layers.group_norm (ArgSpec(args=['input', 'groups', 'epsilon', 'par
 paddle.fluid.layers.spectral_norm (ArgSpec(args=['weight', 'dim', 'power_iters', 'eps', 'name'], varargs=None, keywords=None, defaults=(0, 1, 1e-12, None)), ('document', '3f536aafba30d793287b52d231baff1b'))
 paddle.fluid.layers.softmax_with_cross_entropy (ArgSpec(args=['logits', 'label', 'soft_label', 'ignore_index', 'numeric_stable_mode', 'return_softmax'], varargs=None, keywords=None, defaults=(False, -100, True, False)), ('document', 'bce1b75e3d95b75cacd1099655cbb3c3'))
 paddle.fluid.layers.smooth_l1 (ArgSpec(args=['x', 'y', 'inside_weight', 'outside_weight', 'sigma'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', 'c6b175d253c55baf4b9c0eca9b1dda88'))
-paddle.fluid.layers.one_hot (ArgSpec(args=['input', 'depth'], varargs=None, keywords=None, defaults=None), ('document', 'c87f620c15573442be7c84b50223b567'))
-paddle.fluid.layers.autoincreased_step_counter (ArgSpec(args=['counter_name', 'begin', 'step'], varargs=None, keywords=None, defaults=(None, 1, 1)), ('document', '3f6c828594720c9b2da89c464be94478'))
+paddle.fluid.layers.one_hot (ArgSpec(args=['input', 'depth'], varargs=None, keywords=None, defaults=None), ('document', '960fc799549c202da1e85d626cb2c962'))
+paddle.fluid.layers.autoincreased_step_counter (ArgSpec(args=['counter_name', 'begin', 'step'], varargs=None, keywords=None, defaults=(None, 1, 1)), ('document', '67afefa80b6cc38801bd5b631fed8a4a'))
 paddle.fluid.layers.reshape (ArgSpec(args=['x', 'shape', 'actual_shape', 'act', 'inplace', 'name'], varargs=None, keywords=None, defaults=(None, None, False, None)), ('document', '323c019f257e55ddea4a824a362de62f'))
 paddle.fluid.layers.squeeze (ArgSpec(args=['input', 'axes', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '3229d06517f794e86ca3da14c38b1465'))
 paddle.fluid.layers.unsqueeze (ArgSpec(args=['input', 'axes', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'bbd62da391b1df984a1909d069a759b2'))
@@ -152,7 +152,7 @@ paddle.fluid.layers.image_resize (ArgSpec(args=['input', 'out_shape', 'scale', '
 paddle.fluid.layers.image_resize_short (ArgSpec(args=['input', 'out_short_len', 'resample'], varargs=None, keywords=None, defaults=('BILINEAR',)), ('document', '099b9f051e6247ae661e4a7b4fd3f89a'))
 paddle.fluid.layers.resize_bilinear (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape', 'align_corners', 'align_mode'], varargs=None, keywords=None, defaults=(None, None, None, None, True, 1)), ('document', '746bf58fdb1bd475f8c5f996b05b0e52'))
 paddle.fluid.layers.resize_nearest (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape', 'align_corners'], varargs=None, keywords=None, defaults=(None, None, None, None, True)), ('document', '9baf9288c862161ff850d45228047a5e'))
-paddle.fluid.layers.gather (ArgSpec(args=['input', 'index'], varargs=None, keywords=None, defaults=None), ('document', '98f1c86716b9b7f4dda83f20e2adeee2'))
+paddle.fluid.layers.gather (ArgSpec(args=['input', 'index'], varargs=None, keywords=None, defaults=None), ('document', '01a198d6fff38d5f0d8180a40b228085'))
 paddle.fluid.layers.scatter (ArgSpec(args=['input', 'index', 'updates', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '65f8e9d8ddfd0b412f940579c4faa342'))
 paddle.fluid.layers.sequence_scatter (ArgSpec(args=['input', 'index', 'updates', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '15b522457dfef103f0c20ca9d397678b'))
 paddle.fluid.layers.random_crop (ArgSpec(args=['x', 'shape', 'seed'], varargs=None, keywords=None, defaults=(None,)), ('document', 'c9ab9e460ef0a1823249935a30e82c66'))
@@ -162,7 +162,7 @@ paddle.fluid.layers.selu (ArgSpec(args=['x', 'scale', 'alpha', 'name'], varargs=
 paddle.fluid.layers.log (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '98247c59d1c9b40af6730001b2aea73d'))
 paddle.fluid.layers.crop (ArgSpec(args=['x', 'shape', 'offsets', 'name'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '883104791204d3127e24234bb630b2e7'))
 paddle.fluid.layers.rank_loss (ArgSpec(args=['label', 'left', 'right', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'c542e39ac6add24a6bef6e79bf5617e2'))
-paddle.fluid.layers.margin_rank_loss (ArgSpec(args=['label', 'left', 'right', 'margin', 'name'], varargs=None, keywords=None, defaults=(0.1, None)), ('document', '6d19dcc19917080b7ff3e03bde451bc8'))
+paddle.fluid.layers.margin_rank_loss (ArgSpec(args=['label', 'left', 'right', 'margin', 'name'], varargs=None, keywords=None, defaults=(0.1, None)), ('document', '99b3fee0daee04911d2bee8871b26435'))
 paddle.fluid.layers.elu (ArgSpec(args=['x', 'alpha', 'name'], varargs=None, keywords=None, defaults=(1.0, None)), ('document', '463258ee9f8b60760eb1e26357cc9bfa'))
 paddle.fluid.layers.relu6 (ArgSpec(args=['x', 'threshold', 'name'], varargs=None, keywords=None, defaults=(6.0, None)), ('document', '6f367339caf6c7124bc262fe1475df70'))
 paddle.fluid.layers.pow (ArgSpec(args=['x', 'factor', 'name'], varargs=None, keywords=None, defaults=(1.0, None)), ('document', 'a5117c1eb84aca2ac0b0abab337a4799'))
@@ -191,8 +191,8 @@ paddle.fluid.layers.elementwise_min (ArgSpec(args=['x', 'y', 'axis', 'act', 'nam
 paddle.fluid.layers.elementwise_pow (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '1aea4e197c552a284f83888a3c67a32e'))
 paddle.fluid.layers.uniform_random_batch_size_like (ArgSpec(args=['input', 'shape', 'dtype', 'input_dim_idx', 'output_dim_idx', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', 0, 0, -1.0, 1.0, 0)), ('document', '129e0a3257f1d532a948eedf9d5bf671'))
 paddle.fluid.layers.gaussian_random (ArgSpec(args=['shape', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')), ('document', '389dafe36e099841b6a7fb18d11f1b4c'))
-paddle.fluid.layers.sampling_id (ArgSpec(args=['x', 'min', 'max', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')), ('document', '840fdac643d1341c1cae218d4511dbb9'))
-paddle.fluid.layers.gaussian_random_batch_size_like (ArgSpec(args=['input', 'shape', 'input_dim_idx', 'output_dim_idx', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0, 0, 0.0, 1.0, 0, 'float32')), ('document', '840026b4766613c5705e06563cd103b6'))
+paddle.fluid.layers.sampling_id (ArgSpec(args=['x', 'min', 'max', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')), ('document', '35428949368cad5121dd37f8522ef8b0'))
+paddle.fluid.layers.gaussian_random_batch_size_like (ArgSpec(args=['input', 'shape', 'input_dim_idx', 'output_dim_idx', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0, 0, 0.0, 1.0, 0, 'float32')), ('document', '9e520987168f8ddb7dd71ffd68aa352c'))
 paddle.fluid.layers.sum (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', 'a418e3ccb5e2ac21bd60f5cc221d5860'))
 paddle.fluid.layers.slice (ArgSpec(args=['input', 'axes', 'starts', 'ends'], varargs=None, keywords=None, defaults=None), ('document', '01dbb91e7c74cb11336cd531013de51a'))
 paddle.fluid.layers.shape (ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None), ('document', '17db0f814eb7bb5a3fac1ca6e60e16d8'))
@@ -211,12 +211,12 @@ paddle.fluid.layers.space_to_depth (ArgSpec(args=['x', 'blocksize', 'name'], var
 paddle.fluid.layers.affine_grid (ArgSpec(args=['theta', 'out_shape', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '51def402b8910e163cbace9d0c0526ed'))
 paddle.fluid.layers.sequence_reverse (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '77a6d80aa5551ca70324fc975c44507f'))
 paddle.fluid.layers.affine_channel (ArgSpec(args=['x', 'scale', 'bias', 'data_layout', 'name', 'act'], varargs=None, keywords=None, defaults=(None, None, 'NCHW', None, None)), ('document', 'ab84fdc6dc60f3ad9aa397e6007e3bf9'))
-paddle.fluid.layers.similarity_focus (ArgSpec(args=['input', 'axis', 'indexes', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '70e3b5182a18b40b47ecabd7c8490a35'))
+paddle.fluid.layers.similarity_focus (ArgSpec(args=['input', 'axis', 'indexes', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6f90d6ff76bf4f5e592332c1ef28494e'))
 paddle.fluid.layers.hash (ArgSpec(args=['input', 'hash_size', 'num_hash', 'name'], varargs=None, keywords=None, defaults=(1, None)), ('document', '9bb77f8dc002dd2ce75d4769eaaf5007'))
 paddle.fluid.layers.grid_sampler (ArgSpec(args=['x', 'grid', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'd256cba1c41a5ed92ce3f31e24a2ca6d'))
-paddle.fluid.layers.log_loss (ArgSpec(args=['input', 'label', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(0.0001, None)), ('document', '4b5a2341023afe63157a066c14254f98'))
+paddle.fluid.layers.log_loss (ArgSpec(args=['input', 'label', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(0.0001, None)), ('document', 'af541e9263be61ce0e40df58d1b69294'))
 paddle.fluid.layers.add_position_encoding (ArgSpec(args=['input', 'alpha', 'beta', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '4b9c2e8af5817937d831820874b5aa77'))
-paddle.fluid.layers.bilinear_tensor_product (ArgSpec(args=['x', 'y', 'size', 'act', 'name', 'param_attr', 'bias_attr'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'aa7540a0fa73ff69a02e11b4091aab75'))
+paddle.fluid.layers.bilinear_tensor_product (ArgSpec(args=['x', 'y', 'size', 'act', 'name', 'param_attr', 'bias_attr'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'cd0bd55ef1e1762aca25ec972d34d378'))
 paddle.fluid.layers.merge_selected_rows (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'dc63315b84f591ac79ecca0c3632027a'))
 paddle.fluid.layers.get_tensor_from_selected_rows (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '7ffc849e71f31dfe29030ff94e662de6'))
 paddle.fluid.layers.lstm (ArgSpec(args=['input', 'init_h', 'init_c', 'max_len', 'hidden_size', 'num_layers', 'dropout_prob', 'is_bidirec', 'is_test', 'name', 'default_initializer', 'seed'], varargs=None, keywords=None, defaults=(0.0, False, False, None, None, -1)), ('document', 'd5e6c494ac35100e2ed4d4bd9a1ed932'))
@@ -432,7 +432,7 @@ paddle.fluid.transpiler.RoundRobin.reset (ArgSpec(args=['self'], varargs=None, k
 paddle.fluid.transpiler.DistributeTranspilerConfig.__init__ 
 paddle.fluid.nets.simple_img_conv_pool (ArgSpec(args=['input', 'num_filters', 'filter_size', 'pool_size', 'pool_stride', 'pool_padding', 'pool_type', 'global_pooling', 'conv_stride', 'conv_padding', 'conv_dilation', 'conv_groups', 'param_attr', 'bias_attr', 'act', 'use_cudnn'], varargs=None, keywords=None, defaults=(0, 'max', False, 1, 0, 1, 1, None, None, None, True)), ('document', 'e0f67f35abf27f666f81003113b90244'))
 paddle.fluid.nets.sequence_conv_pool (ArgSpec(args=['input', 'num_filters', 'filter_size', 'param_attr', 'act', 'pool_type', 'bias_attr'], varargs=None, keywords=None, defaults=(None, 'sigmoid', 'max', None)), ('document', '48c434dd7bb827f69d90e5135d77470f'))
-paddle.fluid.nets.glu (ArgSpec(args=['input', 'dim'], varargs=None, keywords=None, defaults=(-1,)), ('document', '08c1c57e1db6b20bf87b264cb7cf3ca8'))
+paddle.fluid.nets.glu (ArgSpec(args=['input', 'dim'], varargs=None, keywords=None, defaults=(-1,)), ('document', '6486b2595300fc3305b5a1f0ac363dce'))
 paddle.fluid.nets.scaled_dot_product_attention (ArgSpec(args=['queries', 'keys', 'values', 'num_heads', 'dropout_rate'], varargs=None, keywords=None, defaults=(1, 0.0)), ('document', '921714c9bfb351b41403418265393203'))
 paddle.fluid.nets.img_conv_group (ArgSpec(args=['input', 'conv_num_filter', 'pool_size', 'conv_padding', 'conv_filter_size', 'conv_act', 'param_attr', 'conv_with_batchnorm', 'conv_batchnorm_drop_rate', 'pool_stride', 'pool_type', 'use_cudnn'], varargs=None, keywords=None, defaults=(1, 3, None, None, False, 0.0, 1, 'max', True)), ('document', '3802be78fbfb206dae64a2d9f8480970'))
 paddle.fluid.optimizer.SGDOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'regularization', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))

python/paddle/fluid/layers/nn.py

@@ -1288,8 +1288,13 @@ def crf_decoding(input, param_attr, label=None):
     Examples:
         .. code-block:: python
 
-           crf_decode = layers.crf_decoding(
-                input=hidden, param_attr=ParamAttr(name="crfw"))
+           images = fluid.layers.data(name='pixel', shape=[784], dtype='float32')
+           label = fluid.layers.data(name='label', shape=[1], dtype='int32')
+           hidden = fluid.layers.fc(input=images, size=2)
+           crf = fluid.layers.linear_chain_crf(input=hidden, label=label, 
+                     param_attr=fluid.ParamAttr(name="crfw"))
+           crf_decode = fluid.layers.crf_decoding(input=hidden, 
+                     param_attr=fluid.ParamAttr(name="crfw"))
     """
     helper = LayerHelper('crf_decoding', **locals())
     transition = helper.get_parameter(param_attr.name)
@@ -5475,38 +5480,40 @@ def nce(input,
     Examples:
         .. code-block:: python
 
-            window_size = 5
-            words = []
-            for i in xrange(window_size):
-                words.append(layers.data(
-                    name='word_{0}'.format(i), shape=[1], dtype='int64'))
 
-            dict_size = 10000
-            label_word = int(window_size / 2) + 1
+	    import numpy as np
 
-            embs = []
-            for i in xrange(window_size):
-                if i == label_word:
-                    continue
+	    window_size = 5
+	    words = []
+	    for i in xrange(window_size):
+		words.append(fluid.layers.data(
+		    name='word_{0}'.format(i), shape=[1], dtype='int64'))
+
+	    dict_size = 10000
+	    label_word = int(window_size / 2) + 1
 
-                emb = layers.embedding(input=words[i], size=[dict_size, 32],
-                                       param_attr='emb.w', is_sparse=True)
-                embs.append(emb)
+	    embs = []
+	    for i in xrange(window_size):
+		if i == label_word:
+		    continue
 
-            embs = layers.concat(input=embs, axis=1)
-            loss = layers.nce(input=embs, label=words[label_word],
-                          num_total_classes=dict_size, param_attr='nce.w',
-                          bias_attr='nce.b')
+		emb = fluid.layers.embedding(input=words[i], size=[dict_size, 32],
+				   param_attr='embed', is_sparse=True)
+		embs.append(emb)
 
-            #or use custom distribution
-            dist = fluid.layers.assign(input=np.array([0.05,0.5,0.1,0.3,0.05]).astype("float32"))
-            loss = layers.nce(input=embs, label=words[label_word],
-                          num_total_classes=5, param_attr='nce.w',
-                          bias_attr='nce.b',
-                          num_neg_samples=3,
-                          sampler="custom_dist",
-                          custom_dist=dist)
+	    embs = fluid.layers.concat(input=embs, axis=1)
+	    loss = fluid.layers.nce(input=embs, label=words[label_word],
+		      num_total_classes=dict_size, param_attr='nce.w_0',
+		      bias_attr='nce.b_0')
 
+	    #or use custom distribution
+	    dist = np.array([0.05,0.5,0.1,0.3,0.05])
+	    loss = fluid.layers.nce(input=embs, label=words[label_word],
+		      num_total_classes=5, param_attr='nce.w_1',
+		      bias_attr='nce.b_1',
+		      num_neg_samples=3,
+		      sampler="custom_dist",
+		      custom_dist=dist)
     """
     helper = LayerHelper('nce', **locals())
     assert isinstance(input, Variable)
@@ -5544,7 +5551,7 @@ def nce(input,
         assert custom_dist is not None
         # assert isinstance(custom_dist, Variable)
 
-        custom_dist_len = len(custom_dist)
+        custom_dist_len = num_total_classes
         alias_probs_ = [0] * custom_dist_len
         alias_ = [0] * custom_dist_len
         bigs = []
@@ -6394,8 +6401,8 @@ def one_hot(input, depth):
     Examples:
         .. code-block:: python
 
-            label = layers.data(name="label", shape=[1], dtype="int64")
-            one_hot_label = layers.one_hot(input=label, depth=10)
+            label = fluid.layers.data(name="label", shape=[1], dtype="int64")
+            one_hot_label = fluid.layers.one_hot(input=label, depth=10)
     """
     helper = LayerHelper("one_hot", **locals())
     one_hot_out = helper.create_variable_for_type_inference(dtype='float32')
@@ -6426,7 +6433,7 @@ def autoincreased_step_counter(counter_name=None, begin=1, step=1):
         .. code-block:: python
 
            global_step = fluid.layers.autoincreased_step_counter(
-               counter_name='@LR_DECAY_COUNTER@', begin=begin, step=1)
+               counter_name='@LR_DECAY_COUNTER@', begin=0, step=1)
     """
     helper = LayerHelper('global_step_counter')
     if counter_name is None:
@@ -7650,6 +7657,8 @@ def gather(input, index):
 
         .. code-block:: python
 
+            x = fluid.layers.data(name='x', shape=[-1, 5], dtype='float32')
+            index = fluid.layers.data(name='index', shape=[-1, 1], dtype='int32')
             output = fluid.layers.gather(x, index)
     """
     helper = LayerHelper('gather', **locals())
@@ -8288,9 +8297,9 @@ def margin_rank_loss(label, left, right, margin=0.1, name=None):
 
         .. code-block:: python
 
-           label = fluid.layers.data(name="label", shape=[4, 1], dtype="float32")
-           left = fluid.layers.data(name="left", shape=[4, 1], dtype="float32")
-           right = fluid.layers.data(name="right", shape=[4, 1], dtype="float32")
+           label = fluid.layers.data(name="label", shape=[-1, 1], dtype="float32")
+           left = fluid.layers.data(name="left", shape=[-1, 1], dtype="float32")
+           right = fluid.layers.data(name="right", shape=[-1, 1], dtype="float32")
            out = fluid.layers.margin_rank_loss(label, left, right)
     """
     helper = LayerHelper('margin_rank_loss', **locals())
@@ -9188,13 +9197,13 @@ def sampling_id(x, min=0.0, max=1.0, seed=0, dtype='float32'):
     Examples:
         .. code-block:: python
 
-            x = layers.data(
+            x = fluid.layers.data(
                 name="X",
                 shape=[13, 11],
                 dtype='float32',
                 append_batch_size=False)
 
-            out = layers.sampling_id(x)
+            out = fluid.layers.sampling_id(x)
     """
 
     helper = LayerHelper('sampling_id', **locals())
@@ -9238,9 +9247,9 @@ def gaussian_random_batch_size_like(input,
     Examples:
         .. code-block:: python
 
-            input = layers.data(name="input", shape=[13, 11], dtype='float32')
+            input = fluid.layers.data(name="input", shape=[13, 11], dtype='float32')
 
-            out = layers.gaussian_random_batch_size_like(
+            out = fluid.layers.gaussian_random_batch_size_like(
                 input, shape=[-1, 11], mean=1.0, std=2.0)
     """
 
@@ -10116,9 +10125,8 @@ def similarity_focus(input, axis, indexes, name=None):
         .. code-block:: python
 
             data = fluid.layers.data(
-              name='data', shape=[2, 3, 2, 2], dtype='float32')
-            x = fluid.layers.layer_norm(input=data, axis=1, indexes=[0])
-
+                name='data', shape=[-1, 3, 2, 2], dtype='float32')
+            fluid.layers.similarity_focus(input=data, axis=1, indexes=[0])
     """
     helper = LayerHelper('similarity_focus', **locals())
     # check attrs
@@ -10326,7 +10334,8 @@ def log_loss(input, label, epsilon=1e-4, name=None):
     Examples:
         .. code-block:: python
 
-          prob = fluid.layers.sigmoid(net)
+          label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+          prob = fluid.layers.data(name='prob', shape=[10], dtype='float32')
           cost = fluid.layers.log_loss(input=prob, label=label)
     """
     helper = LayerHelper('log_loss', **locals())
@@ -10479,7 +10488,9 @@ def bilinear_tensor_product(x,
     Examples:
         .. code-block:: python
 
-          tensor = bilinear_tensor_product(x=layer1, y=layer2, size=1000)
+          layer1 = fluid.layers.data("t1", shape=[-1, 5], dtype="float32")
+          layer2 = fluid.layers.data("t2", shape=[-1, 4], dtype="float32")
+          tensor = fluid.layers.bilinear_tensor_product(x=layer1, y=layer2, size=1000)
     """
     helper = LayerHelper('bilinear_tensor_product', **locals())
     dtype = helper.input_dtype('x')

python/paddle/fluid/nets.py

@@ -327,8 +327,10 @@ def glu(input, dim=-1):
     Examples:
         .. code-block:: python
 
-            data = fluid.layers.data(name="words", shape=[3, 6, 9], dtype="float32")
-            output = fluid.nets.glu(input=data, dim=1)  # shape of output: [3, 3, 9]
+            data = fluid.layers.data(
+                name="words", shape=[-1, 6, 3, 9], dtype="float32")
+            # shape of output: [-1, 3, 3, 9]
+            output = fluid.nets.glu(input=data, dim=1)
     """
 
     a, b = layers.split(input, num_or_sections=2, dim=dim)