add gluonbook dep, rm sys.append(..)

wagnlinzh · Jul 23, 2018 · b29c626 · b29c626
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diff --git a/build/build.yml b/build/build.yml
@@ -13,4 +13,5 @@ dependencies:
   - recommonmark==0.4.0
   - https://github.com/mli/notedown/tarball/master
   - mxnet-cu80==1.2.0
+  - gluonbook==0.7.0
   - jieba==0.39
diff --git a/chapter_computational-performance/multiple-gpus-gluon.md b/chapter_computational-performance/multiple-gpus-gluon.md
@@ -5,8 +5,6 @@
 先导入本节实验需要的包或模块。同上一节，运行本节中的程序需要至少两块GPU。
 
 ```{.python .input}
-import sys
-sys.path.append('..')
 import gluonbook as gb
 import mxnet as mx
 from mxnet import autograd, gluon, init, nd

diff --git a/chapter_computational-performance/multiple-gpus.md b/chapter_computational-performance/multiple-gpus.md
@@ -20,8 +20,6 @@
 为了从零开始实现多GPU训练中的数据并行，让我们先导入需要的包或模块。
 
 ```{.python .input}
-import sys
-sys.path.append('..')
 import gluonbook as gb
 import mxnet as mx
 from mxnet import autograd, nd

diff --git a/chapter_computer-vision/anchor.md b/chapter_computer-vision/anchor.md
@@ -6,8 +6,6 @@
 
 ```{.python .input  n=1}
 %matplotlib inline
-import sys
-sys.path.insert(0, '..')
 import gluonbook as gb
 from mxnet import contrib, gluon, image, nd
 import numpy as np

diff --git a/chapter_computer-vision/bounding-box.md b/chapter_computer-vision/bounding-box.md
@@ -8,8 +8,6 @@
 
 ```{.python .input  n=1}
 %matplotlib inline
-import sys
-sys.path.insert(0, '..')
 import gluonbook as gb
 from mxnet import image
 ```

diff --git a/chapter_computer-vision/fcn.md b/chapter_computer-vision/fcn.md
@@ -6,12 +6,11 @@
 
 ```{.python .input  n=1}
 %matplotlib inline
-import sys
-sys.path.append('..')
 import gluonbook as gb
 from mxnet import gluon, init, nd, image
 from mxnet.gluon import data as gdata, loss as gloss, model_zoo, nn
 import numpy as np
+import sys
 ```
 
 ## 转置卷积层

diff --git a/chapter_computer-vision/fine-tuning.md b/chapter_computer-vision/fine-tuning.md
@@ -31,8 +31,6 @@
 
 ```{.python .input  n=4}
 %matplotlib inline
-import sys
-sys.path.insert(0, '..')
 import zipfile
 import gluonbook as gb
 from mxnet import gluon, init, nd

diff --git a/chapter_computer-vision/image-augmentation.md b/chapter_computer-vision/image-augmentation.md
@@ -6,8 +6,6 @@
 
 ```{.python .input  n=21}
 %matplotlib inline
-import sys
-sys.path.insert(0, '..')
 import gluonbook as gb
 import mxnet as mx
 from mxnet import autograd, gluon, image, init, nd 

diff --git a/chapter_computer-vision/kaggle-gluon-cifar10.md b/chapter_computer-vision/kaggle-gluon-cifar10.md
@@ -13,9 +13,6 @@ CIFAR-10是计算机视觉领域的一个重要的数据集。本节中，我们
 首先，导入实验所需的包或模块。
 
 ```{.python .input}
-import sys
-sys.path.append('..')
-
 import datetime
 import gluonbook as gb
 from mxnet import autograd, gluon, init, nd

diff --git a/chapter_computer-vision/kaggle-gluon-dog.md b/chapter_computer-vision/kaggle-gluon-dog.md
@@ -15,9 +15,6 @@
 首先，导入实验所需的包或模块。
 
 ```{.python .input}
-import sys
-sys.path.append('..')
-
 import collections
 import datetime
 import gluonbook as gb

diff --git a/chapter_computer-vision/neural-style.md b/chapter_computer-vision/neural-style.md
@@ -13,8 +13,6 @@
 
 ```{.python .input}
 %matplotlib inline
-import sys
-sys.path.append('..')
 import gluonbook as gb
 from mxnet import autograd, gluon, image, nd
 from mxnet.gluon import model_zoo, nn
@@ -256,4 +254,4 @@ gb.plt.imsave('../img/neural-style-2.png', postprocess(z).asnumpy())
 
 ## 参考文献
 
-[1] Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. "Image style transfer using convolutional neural networks." CVPR. 2016.
+[1] Gatys, L. A., Ecker, A. S., & Bethge, M. (2016). Image style transfer using convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2414-2423).
diff --git a/chapter_computer-vision/object-detection-dataset.md b/chapter_computer-vision/object-detection-dataset.md
@@ -8,8 +8,6 @@
 
 ```{.python .input  n=1}
 %matplotlib inline
-import sys
-sys.path.insert(0, '..')
 import gluonbook as gb
 from mxnet import gluon, image
 from mxnet.gluon import utils as gutils

diff --git a/chapter_computer-vision/semantic-segmentation-and-dataset.md b/chapter_computer-vision/semantic-segmentation-and-dataset.md
@@ -15,12 +15,11 @@
 
 ```{.python .input  n=1}
 %matplotlib inline
-import sys
-sys.path.append('..')
 import gluonbook as gb
 from mxnet import gluon, image, nd
 from mxnet.gluon import data as gdata, utils as gutils
 import os
+import sys
 import tarfile
 ```
 

diff --git a/chapter_computer-vision/ssd.md b/chapter_computer-vision/ssd.md
@@ -4,8 +4,6 @@
 
 ```{.python .input  n=1}
 %matplotlib inline
-import sys
-sys.path.insert(0, '..')
 import gluonbook as gb
 from mxnet import autograd, contrib, gluon, image, init, nd
 from mxnet.gluon import loss as gloss, nn

diff --git a/chapter_convolutional-neural-networks/alexnet.md b/chapter_convolutional-neural-networks/alexnet.md
@@ -51,12 +51,11 @@ AlextNet与LeNet的设计理念非常相似。但也有非常显著的区别。
 下面我们实现（稍微简化过的）Alexnet：
 
 ```{.python .input  n=1}
-import sys
-sys.path.append('..')
 import gluonbook as gb
 from mxnet import nd, init, gluon
 from mxnet.gluon import data as gdata, loss as gloss, nn
 import os
+import sys
 
 net = nn.Sequential()
 net.add(

diff --git a/chapter_convolutional-neural-networks/batch-norm-gluon.md b/chapter_convolutional-neural-networks/batch-norm-gluon.md
@@ -3,8 +3,6 @@
 相比于前小节定义的BatchNorm类，`nn`模块定义的BatchNorm使用更加简单。它不需要指定输出数据的维度和特征维的大小，这些都将通过延后初始化来获取。我们实现同前小节一样的批量归一化的LeNet。
 
 ```{.python .input  n=1}
-import sys
-sys.path.append('..')
 import gluonbook as gb
 from mxnet import nd, gluon, init
 from mxnet.gluon import loss as gloss, nn

diff --git a/chapter_convolutional-neural-networks/batch-norm.md b/chapter_convolutional-neural-networks/batch-norm.md
@@ -30,8 +30,6 @@ $$y_i \leftarrow \gamma \hat{x}_i + \beta.$$
 下面我们通过NDArray来实现这个计算。
 
 ```{.python .input  n=72}
-import sys
-sys.path.insert(0, '..')
 import gluonbook as gb
 from mxnet import nd, gluon, init, autograd
 from mxnet.gluon import loss as gloss, nn

diff --git a/chapter_convolutional-neural-networks/conv-layer.md b/chapter_convolutional-neural-networks/conv-layer.md
@@ -13,8 +13,6 @@
 下面我们将上述过程实现在`corr2d`函数里，它接受`X`和`K`，输出`Y`。
 
 ```{.python .input}
-import sys
-sys.path.append('..')
 import gluonbook as gb
 from mxnet import autograd, nd
 from mxnet.gluon import nn

diff --git a/chapter_convolutional-neural-networks/densenet.md b/chapter_convolutional-neural-networks/densenet.md
@@ -13,8 +13,6 @@ DenseNet的主要构建模块是稠密块和过渡块，前者定义了输入和
 DeseNet使用了ResNet改良版的“批量归一化、激活和卷积”结构（参见上一节习题），我们首先在`conv_block`函数里实现这个结构。
 
 ```{.python .input  n=1}
-import sys
-sys.path.append('..')
 import gluonbook as gb
 from mxnet import nd, gluon, init
 from mxnet.gluon import loss as gloss, nn

diff --git a/chapter_convolutional-neural-networks/googlenet.md b/chapter_convolutional-neural-networks/googlenet.md
@@ -13,8 +13,6 @@ GoogLeNet中的基础卷积块叫做Inception，得名于同名电影《盗梦
 Inception块中可以自定义的超参数是每个层的输出通道数，我们以此来控制模型复杂度。
 
 ```{.python .input  n=1}
-import sys
-sys.path.insert(0, '..')
 import gluonbook as gb
 from mxnet import nd, init, gluon
 from mxnet.gluon import loss as gloss, nn

diff --git a/chapter_convolutional-neural-networks/lenet.md b/chapter_convolutional-neural-networks/lenet.md
@@ -16,8 +16,6 @@ LeNet分为卷积层块和全连接层块两个部分。卷积层块里的基本
 卷积层块把每个样本输出拉升成向量输入到全连接层块中。全连接层块由两个输出大小分别为120和84的全连接层，然后接上输出大小为10（因为数字的类别一共为10）的输出层构成。下面我们通过Sequential类来实现LeNet。
 
 ```{.python .input}
-import sys
-sys.path.append('..')
 import gluonbook as gb
 import mxnet as mx
 from mxnet import autograd, nd, gluon, init

diff --git a/chapter_convolutional-neural-networks/nin.md b/chapter_convolutional-neural-networks/nin.md
@@ -11,8 +11,6 @@
 NiN中的一个基础块由一个卷积层外加两个充当全连接层的$1\times 1$卷积层构成。第一个卷积层我们可以设置它的超参数，而第二和第三卷积层则使用固定超参数。
 
 ```{.python .input  n=2}
-import sys
-sys.path.insert(0, '..')
 import gluonbook as gb
 from mxnet import nd, gluon, init
 from mxnet.gluon import loss as gloss, nn

diff --git a/chapter_convolutional-neural-networks/resnet.md b/chapter_convolutional-neural-networks/resnet.md
@@ -17,8 +17,6 @@ ResNet沿用了VGG全$3\times 3$卷积层设计。残差块里首先是两个有
 残差块的实现如下。它可以设定输出通道数，是否使用额外的卷积层来修改输入通道数，以及卷积层的步幅大小。我们将`Residual`类定义在`gluonbook`包中供后面章节调用。
 
 ```{.python .input  n=1}
-import sys
-sys.path.append('..')
 import gluonbook as gb
 from mxnet import nd, gluon, init
 from mxnet.gluon import loss as gloss, nn

diff --git a/chapter_convolutional-neural-networks/vgg.md b/chapter_convolutional-neural-networks/vgg.md
@@ -9,8 +9,6 @@ AlexNet在LeNet的基础上增加了三个卷积层。但作者对它们的卷
 VGG模型的基础组成规律是：连续使用数个相同的填充为1的$3\times 3$卷积层后接上一个步幅为2的$2\times 2$最大池化层。卷积层保持输入高宽，而池化层则对其减半。我们使用`vgg_block`函数来实现这个基础块，它可以指定使用卷积层的数量和其输出通道数。
 
 ```{.python .input  n=1}
-import sys
-sys.path.append('..')
 import gluonbook as gb
 from mxnet import nd, init, gluon
 from mxnet.gluon import loss as gloss, nn

diff --git a/chapter_deep-learning-basics/dropout-gluon.md b/chapter_deep-learning-basics/dropout-gluon.md
@@ -8,8 +8,6 @@
 在多层感知机中Gluon实现的基础上，我们只需要在全连接层后添加Dropout层并指定丢弃概率。在训练模型时，Dropout层将以指定的丢弃概率随机丢弃上一层的输出元素；在测试模型时，Dropout层并不发挥作用。
 
 ```{.python .input  n=5}
-import sys
-sys.path.append('..')
 import gluonbook as gb
 from mxnet import autograd, gluon, init, nd
 from mxnet.gluon import loss as gloss, nn

diff --git a/chapter_deep-learning-basics/dropout.md b/chapter_deep-learning-basics/dropout.md
@@ -28,8 +28,6 @@ $$h_i = \frac{\xi_i}{1-p} \phi(x_1 w_1^{(i)} + x_2 w_2^{(i)} + x_3 w_3^{(i)} + x
 根据丢弃法的定义，我们可以很容易地实现它。下面的`dropout`函数将以`drop_prob`的概率丢弃NDArray输入`X`中的元素。
 
 ```{.python .input}
-import sys
-sys.path.append('..')
 import gluonbook as gb
 from mxnet import autograd, gluon, nd
 from mxnet.gluon import loss as gloss

diff --git a/chapter_deep-learning-basics/kaggle-house-price.md b/chapter_deep-learning-basics/kaggle-house-price.md
@@ -38,8 +38,6 @@ Kaggle（网站地址：https://www.kaggle.com ）是一个著名的供机器学
 
 ```{.python .input  n=1}
 %matplotlib inline
-import sys
-sys.path.append('..')
 import gluonbook as gb
 from mxnet import autograd, init, gluon, nd
 from mxnet.gluon import data as gdata, loss as gloss, nn

diff --git a/chapter_deep-learning-basics/mlp-gluon.md b/chapter_deep-learning-basics/mlp-gluon.md
@@ -3,8 +3,6 @@
 下面我们使用Gluon来实现上一节中的多层感知机。首先我们导入所需的包或模块。
 
 ```{.python .input}
-import sys
-sys.path.append('..')
 import gluonbook as gb
 from mxnet import autograd, gluon, init, nd
 from mxnet.gluon import loss as gloss, nn

diff --git a/chapter_deep-learning-basics/mlp-scratch.md b/chapter_deep-learning-basics/mlp-scratch.md
@@ -3,8 +3,6 @@
 我们已经从上一章里了解了多层感知机的原理。下面，我们一起来动手实现一个多层感知机。首先导入实现所需的包或模块。
 
 ```{.python .input}
-import sys
-sys.path.append('..')
 import gluonbook as gb
 from mxnet import autograd, gluon, nd
 from mxnet.gluon import loss as gloss

diff --git a/chapter_deep-learning-basics/mlp.md b/chapter_deep-learning-basics/mlp.md
@@ -45,8 +45,6 @@ $$\text{relu}(x) = \max(x, 0).$$
 ```{.python .input}
 # 将图打印在 Jupyter notebook 的文本之间。
 %matplotlib inline
-import sys
-sys.path.append('..')
 import gluonbook as gb
 from mxnet import nd
 

diff --git a/chapter_deep-learning-basics/softmax-regression-gluon.md b/chapter_deep-learning-basics/softmax-regression-gluon.md
@@ -5,8 +5,6 @@
 首先导入本节实现所需的包或模块。
 
 ```{.python .input  n=1}
-import sys
-sys.path.append('..')
 import gluonbook as gb
 from mxnet import autograd, gluon, init, nd
 from mxnet.gluon import loss as gloss, nn

diff --git a/chapter_deep-learning-basics/softmax-regression-scratch.md b/chapter_deep-learning-basics/softmax-regression-scratch.md
@@ -3,11 +3,10 @@
 下面我们来动手实现Softmax回归。首先，导入实验所需的包或模块。
 
 ```{.python .input  n=1}
-import sys
-sys.path.append('..')
 import gluonbook as gb
 from mxnet import autograd, nd
 from mxnet.gluon import data as gdata
+import sys
 ```
 
 ## 获取Fashion-MNIST数据集

diff --git a/chapter_deep-learning-basics/underfit-overfit.md b/chapter_deep-learning-basics/underfit-overfit.md
@@ -41,8 +41,6 @@ $$\hat{y} = b + \sum_{k=1}^K x^k w_k$$
 
 ```{.python .input}
 %matplotlib inline
-import sys
-sys.path.append('..')
 import gluonbook as gb
 from mxnet import autograd, gluon, nd
 from mxnet.gluon import data as gdata, loss as gloss, nn

diff --git a/chapter_deep-learning-basics/weight-decay-gluon.md b/chapter_deep-learning-basics/weight-decay-gluon.md
@@ -4,8 +4,6 @@
 
 ```{.python .input  n=1}
 %matplotlib inline
-import sys
-sys.path.append('..')
 import gluonbook as gb
 from mxnet import autograd, gluon, init, nd
 from mxnet.gluon import data as gdata, loss as gloss, nn

diff --git a/chapter_deep-learning-basics/weight-decay.md b/chapter_deep-learning-basics/weight-decay.md
@@ -38,8 +38,6 @@ $$y = 0.05 + \sum_{i = 1}^p 0.01x_i +  \epsilon,$$
 
 ```{.python .input  n=2}
 %matplotlib inline
-import sys
-sys.path.append('..')
 import gluonbook as gb
 from mxnet import autograd, gluon, nd
 

diff --git a/chapter_natural-language-processing/sentiment-analysis-cnn.md b/chapter_natural-language-processing/sentiment-analysis-cnn.md
@@ -1,5 +1,7 @@
 # 文本分类：情感分析
 
+TODO(@astonzhang): edits
+
 在之前的章节中介绍了卷积神经网络用于计算机视觉领域。
 在本节将介绍如何将卷积神经网络应用于自然语言处理领域。以及参考textCNN模型使用Gluon创建一个卷积神经网络用于文本情感分类。
 
@@ -24,8 +26,6 @@
 在实验开始前，导入所需的包或模块。
 
 ```{.python .input  n=1}
-import sys
-sys.path.append('..')
 import collections
 import gluonbook as gb
 import mxnet as mx

diff --git a/chapter_natural-language-processing/sentiment-analysis.md b/chapter_natural-language-processing/sentiment-analysis.md
@@ -11,8 +11,6 @@
 在实验开始前，导入所需的包或模块。
 
 ```{.python .input  n=1}
-import sys
-sys.path.append('..')
 import collections
 import gluonbook as gb
 import mxnet as mx

diff --git a/chapter_optimization/adadelta-gluon.md b/chapter_optimization/adadelta-gluon.md
@@ -6,8 +6,6 @@
 
 ```{.python .input}
 %matplotlib inline
-import sys
-sys.path.append('..')
 import gluonbook as gb
 from mxnet import gluon, init, nd
 from mxnet.gluon import nn

diff --git a/chapter_optimization/adadelta.md b/chapter_optimization/adadelta.md
@@ -47,8 +47,6 @@ def adadelta(params, sqrs, deltas, rho, batch_size):
 
 ```{.python .input}
 %matplotlib inline
-import sys
-sys.path.append('..')
 import gluonbook as gb
 from mxnet import autograd, nd
 import numpy as np

diff --git a/chapter_optimization/adagrad-gluon.md b/chapter_optimization/adagrad-gluon.md
@@ -7,8 +7,6 @@
 
 ```{.python .input}
 %matplotlib inline
-import sys
-sys.path.append('..')
 import gluonbook as gb
 from mxnet import gluon, init, nd
 from mxnet.gluon import nn