From c69f3be75873d786465e9884a3651dd2607809ff Mon Sep 17 00:00:00 2001
From: Yanjun Peng <pengyanjun1@huawei.com>
Date: Fri, 17 Apr 2020 15:40:16 +0800
Subject: [PATCH] fix dataset api description

---
 mindspore/dataset/engine/datasets.py | 6 +++---
 mindspore/dataset/engine/samplers.py | 3 +--
 mindspore/train/model.py             | 4 ++++
 3 files changed, 8 insertions(+), 5 deletions(-)

diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py
index 1c6ac634f2..df05da7032 100644
--- a/mindspore/dataset/engine/datasets.py
+++ b/mindspore/dataset/engine/datasets.py
@@ -556,7 +556,7 @@ class Dataset:
 
         Note:
             If device is Ascend, features of data will be transferred one by one. The limitation
-            of data transferation per time is 256M.
+            of data transmission per time is 256M.
 
         Return:
             TransferDataset, dataset for transferring.
@@ -572,7 +572,7 @@ class Dataset:
 
         Note:
             If device is Ascend, features of data will be transferred one by one. The limitation
-            of data transferation per time is 256M.
+            of data transmission per time is 256M.
 
         Returns:
             TransferDataset, dataset for transferring.
@@ -1897,7 +1897,7 @@ class GeneratorDataset(SourceDataset):
         >>>     for i in range(maxid):
         >>>         yield (np.array([i]), np.array([[i, i + 1], [i + 2, i + 3]]))
         >>> # create multi_column_generator_dataset with GeneratorMC and column names "col1" and "col2"
-        >>> multi_column_generator_dataset = de.GeneratorDataset(generator_mc, ["col1, col2"])
+        >>> multi_column_generator_dataset = de.GeneratorDataset(generator_mc, ["col1", "col2"])
         >>> # 3) Iterable dataset as iterable input
         >>> class MyIterable():
         >>>     def __iter__(self):
diff --git a/mindspore/dataset/engine/samplers.py b/mindspore/dataset/engine/samplers.py
index f9c74f151d..0bba559210 100644
--- a/mindspore/dataset/engine/samplers.py
+++ b/mindspore/dataset/engine/samplers.py
@@ -112,8 +112,7 @@ class RandomSampler():
 
     Args:
         replacement (bool, optional): If True, put the sample ID back for the next draw (default=False).
-        num_samples (int, optional): Number of elements to sample (default=None, all elements). This
-            argument should be specified only when 'replacement' is "True".
+        num_samples (int, optional): Number of elements to sample (default=None, all elements).
 
     Examples:
         >>> import mindspore.dataset as ds
diff --git a/mindspore/train/model.py b/mindspore/train/model.py
index 46e4f421f7..851cd63af9 100755
--- a/mindspore/train/model.py
+++ b/mindspore/train/model.py
@@ -362,6 +362,8 @@ class Model:
             If dataset_sink_mode is True, epoch of training should be equal to the count of repeat
             operation in dataset processing. Otherwise, errors could occur since the amount of data
             is not the amount training requires.
+            If dataset_sink_mode is True, data will be sent to device. If device is Ascend, features
+            of data will be transferred one by one. The limitation of data transmission per time is 256M.
 
         Args:
             epoch (int): Total number of iterations on the data.
@@ -485,6 +487,8 @@ class Model:
 
         Note:
             CPU is not supported when dataset_sink_mode is true.
+            If dataset_sink_mode is True, data will be sent to device. If device is Ascend, features
+            of data will be transferred one by one. The limitation of data transmission per time is 256M.
 
         Args:
             valid_dataset (Dataset): Dataset to evaluate the model.