From 62819e2a3d847c722c8227e4dd4def0432ff3728 Mon Sep 17 00:00:00 2001
From: smilesboy <1563400360@qq.com>
Date: Fri, 27 Apr 2018 19:54:11 +0800
Subject: [PATCH] =?UTF-8?q?=E7=BF=BB=E8=AF=91=E4=BF=AE=E6=94=B9?=
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---
 .../zh/beginner_source/examples_autograd/tf_two_layer_net.py | 4 ++--
 .../examples_autograd/two_layer_net_autograd.py              | 4 ++--
 .../examples_autograd/two_layer_net_custom_function.py       | 4 ++--
 tutorial/zh/beginner_source/examples_nn/dynamic_net.py       | 4 ++--
 .../zh/beginner_source/examples_nn/two_layer_net_module.py   | 5 ++---
 tutorial/zh/beginner_source/examples_nn/two_layer_net_nn.py  | 4 ++--
 .../zh/beginner_source/examples_nn/two_layer_net_optim.py    | 4 ++--
 .../beginner_source/examples_tensor/two_layer_net_tensor.py  | 4 ++--
 8 files changed, 16 insertions(+), 17 deletions(-)

diff --git a/tutorial/zh/beginner_source/examples_autograd/tf_two_layer_net.py b/tutorial/zh/beginner_source/examples_autograd/tf_two_layer_net.py
index be69b4d4c..82cc4832e 100644
--- a/tutorial/zh/beginner_source/examples_autograd/tf_two_layer_net.py
+++ b/tutorial/zh/beginner_source/examples_autograd/tf_two_layer_net.py
@@ -21,8 +21,8 @@
 
 # 首先我们设置计算图:
 
-# N 批量大小; D_in是输入尺寸;
-# H是隐藏尺寸; D_out是输出尺寸.
+# N 是一个batch的样本数量; D_in是输入维度;
+# H 是隐藏层向量的维度; D_out是输出维度.
 N, D_in, H, D_out = 64, 1000, 100, 10
 
 # 为输入数据和目标数据创建占位符; 
diff --git a/tutorial/zh/beginner_source/examples_autograd/two_layer_net_autograd.py b/tutorial/zh/beginner_source/examples_autograd/two_layer_net_autograd.py
index c8064b535..4b80cbb86 100644
--- a/tutorial/zh/beginner_source/examples_autograd/two_layer_net_autograd.py
+++ b/tutorial/zh/beginner_source/examples_autograd/two_layer_net_autograd.py
@@ -24,8 +24,8 @@
 dtype = torch.FloatTensor
 # dtype = torch.cuda.FloatTensor # 取消注释以在GPU上运行
 
-# N 批量大小; D_in是输入尺寸;
-# H是隐藏尺寸; D_out是输出尺寸.
+# N 是一个batch的样本数量; D_in是输入维度;
+# H 是隐藏层向量的维度; D_out是输出维度.
 N, D_in, H, D_out = 64, 1000, 100, 10
 
 # 创建随机张量来保存输入和输出,并将它们包装在变量中.
diff --git a/tutorial/zh/beginner_source/examples_autograd/two_layer_net_custom_function.py b/tutorial/zh/beginner_source/examples_autograd/two_layer_net_custom_function.py
index 60bba7e01..def565aa5 100644
--- a/tutorial/zh/beginner_source/examples_autograd/two_layer_net_custom_function.py
+++ b/tutorial/zh/beginner_source/examples_autograd/two_layer_net_custom_function.py
@@ -47,8 +47,8 @@ def backward(ctx, grad_output):
 dtype = torch.FloatTensor
 # dtype = torch.cuda.FloatTensor # 取消注释以在GPU上运行
 
-# N 批量大小; D_in是输入尺寸;
-# H是隐藏尺寸; D_out是输出尺寸.
+# N 是一个batch的样本数量; D_in是输入维度;
+# H 是隐藏层向量的维度; D_out是输出维度.
 N, D_in, H, D_out = 64, 1000, 100, 10
 
 # 创建随机张量来保存输入和输出,并将它们包装在变量中.
diff --git a/tutorial/zh/beginner_source/examples_nn/dynamic_net.py b/tutorial/zh/beginner_source/examples_nn/dynamic_net.py
index 01e5fc89b..d2209915b 100644
--- a/tutorial/zh/beginner_source/examples_nn/dynamic_net.py
+++ b/tutorial/zh/beginner_source/examples_nn/dynamic_net.py
@@ -41,8 +41,8 @@ def forward(self, x):
         return y_pred
 
 
-# N 批量大小; D_in是输入尺寸;
-# H是隐藏尺寸; D_out是输出尺寸.
+# N 是一个batch的样本数量; D_in是输入维度;
+# H 是隐藏层向量的维度; D_out是输出维度.
 N, D_in, H, D_out = 64, 1000, 100, 10
 
 # 创建随机张量来保存输入和输出,并将它们包装在变量中.
diff --git a/tutorial/zh/beginner_source/examples_nn/two_layer_net_module.py b/tutorial/zh/beginner_source/examples_nn/two_layer_net_module.py
index fd202844c..060cb7127 100644
--- a/tutorial/zh/beginner_source/examples_nn/two_layer_net_module.py
+++ b/tutorial/zh/beginner_source/examples_nn/two_layer_net_module.py
@@ -32,9 +32,8 @@ def forward(self, x):
         y_pred = self.linear2(h_relu)
         return y_pred
 
-
-# N 批量大小; D_in是输入尺寸;
-# H是隐藏尺寸; D_out是输出尺寸.
+# N 是一个batch的样本数量; D_in是输入维度;
+# H 是隐藏层向量的维度; D_out是输出维度.
 N, D_in, H, D_out = 64, 1000, 100, 10
 
 # 创建随机张量来保存输入和输出,并将它们包装在变量中. 
diff --git a/tutorial/zh/beginner_source/examples_nn/two_layer_net_nn.py b/tutorial/zh/beginner_source/examples_nn/two_layer_net_nn.py
index ab46795fb..ef619834e 100644
--- a/tutorial/zh/beginner_source/examples_nn/two_layer_net_nn.py
+++ b/tutorial/zh/beginner_source/examples_nn/two_layer_net_nn.py
@@ -16,8 +16,8 @@
 import torch
 from torch.autograd import Variable
 
-# N 批量大小; D_in是输入尺寸;
-# H是隐藏尺寸; D_out是输出尺寸.
+# N 是一个batch的样本数量; D_in是输入维度;
+# H 是隐藏层向量的维度; D_out是输出维度.
 N, D_in, H, D_out = 64, 1000, 100, 10
 
 # 创建随机张量来保存输入和输出,并将它们包装在变量中.
diff --git a/tutorial/zh/beginner_source/examples_nn/two_layer_net_optim.py b/tutorial/zh/beginner_source/examples_nn/two_layer_net_optim.py
index 15bccf486..6d64b58db 100644
--- a/tutorial/zh/beginner_source/examples_nn/two_layer_net_optim.py
+++ b/tutorial/zh/beginner_source/examples_nn/two_layer_net_optim.py
@@ -16,8 +16,8 @@
 import torch
 from torch.autograd import Variable
 
-# N 批量大小; D_in是输入尺寸;
-# H是隐藏尺寸; D_out是输出尺寸.
+# N 是一个batch的样本数量; D_in是输入维度;
+# H 是隐藏层向量的维度; D_out是输出维度.
 N, D_in, H, D_out = 64, 1000, 100, 10
 
 # 创建随机张量来保存输入和输出,并将它们包装在变量中.
diff --git a/tutorial/zh/beginner_source/examples_tensor/two_layer_net_tensor.py b/tutorial/zh/beginner_source/examples_tensor/two_layer_net_tensor.py
index 5d9538174..5d30f1137 100644
--- a/tutorial/zh/beginner_source/examples_tensor/two_layer_net_tensor.py
+++ b/tutorial/zh/beginner_source/examples_tensor/two_layer_net_tensor.py
@@ -24,8 +24,8 @@
 dtype = torch.FloatTensor
 # dtype = torch.cuda.FloatTensor # 取消注释以在GPU上运行
 
-# N 批量大小; D_in是输入尺寸;
-# H是隐藏尺寸; D_out是输出尺寸.
+# N 是一个batch的样本数量; D_in是输入维度;
+# H 是隐藏层向量的维度; D_out是输出维度.
 N, D_in, H, D_out = 64, 1000, 100, 10
 
 # 创建随机输入和输出数据