remove unused imports, add lint

chapter_computer-vision/kaggle-gluon-cifar10.md

@@ -15,9 +15,8 @@ CIFAR-10是计算机视觉领域的一个重要的数据集。本节中，我们
 ```{.python .input}
 import datetime
 import gluonbook as gb
-from mxnet import autograd, gluon, init, nd
+from mxnet import autograd, gluon, init
 from mxnet.gluon import data as gdata, nn, loss as gloss
-import numpy as np
 import os
 import pandas as pd
 import shutil

chapter_computer-vision/kaggle-gluon-dog.md

@@ -21,7 +21,6 @@ import gluonbook as gb
 import math
 from mxnet import autograd, gluon, init, nd
 from mxnet.gluon import data as gdata, loss as gloss, model_zoo, nn
-import numpy as np
 import os
 import shutil
 import zipfile
@@ -71,9 +70,7 @@ def reorg_dog_data(data_dir, label_file, train_dir, test_dir, input_dir,
         lines = f.readlines()[1:]
         tokens = [l.rstrip().split(',') for l in lines]
         idx_label = dict(((idx, label) for idx, label in tokens))
-    labels = set(idx_label.values())
 
-    n_train = len(os.listdir(os.path.join(data_dir, train_dir)))
     # 训练集中数量最少一类的狗的样本数。
     min_n_train_per_label = (
         collections.Counter(idx_label.values()).most_common()[:-2:-1][0][1])

chapter_convolutional-neural-networks/batch-norm-gluon.md

@@ -4,7 +4,7 @@
 
 ```{.python .input  n=1}
 import gluonbook as gb
-from mxnet import nd, gluon, init
+from mxnet import gluon, init
 from mxnet.gluon import loss as gloss, nn
 
 net = nn.Sequential()

chapter_convolutional-neural-networks/channels.md

@@ -11,8 +11,7 @@
 接下来我们实现处理多输入通道的相关运算符。首先我们将前面小节实现的`corr2d`复制过来。
 
 ```{.python .input  n=2}
-from mxnet import nd, autograd
-from mxnet.gluon import nn
+from mxnet import nd
 
 def corr2d(X, K):
     n, m = K.shape

chapter_convolutional-neural-networks/conv-layer.md

@@ -13,7 +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 gluonbook as gb
 from mxnet import autograd, nd
 from mxnet.gluon import nn
 

chapter_deep-learning-basics/dropout-gluon.md

@@ -9,7 +9,7 @@
 
 ```{.python .input  n=5}
 import gluonbook as gb
-from mxnet import autograd, gluon, init, nd
+from mxnet import gluon, init
 from mxnet.gluon import loss as gloss, nn
 
 drop_prob1 = 0.2

chapter_deep-learning-basics/dropout.md

@@ -29,7 +29,7 @@ $$h_i = \frac{\xi_i}{1-p} \phi(x_1 w_1^{(i)} + x_2 w_2^{(i)} + x_3 w_3^{(i)} + x
 
 ```{.python .input}
 import gluonbook as gb
-from mxnet import autograd, gluon, nd
+from mxnet import autograd, nd
 from mxnet.gluon import loss as gloss
 
 def dropout(X, drop_prob):

chapter_deep-learning-basics/mlp-gluon.md

@@ -4,7 +4,7 @@
 
 ```{.python .input}
 import gluonbook as gb
-from mxnet import autograd, gluon, init, nd
+from mxnet import gluon, init
 from mxnet.gluon import loss as gloss, nn
 ```
 

chapter_deep-learning-basics/mlp-scratch.md

@@ -4,7 +4,7 @@
 
 ```{.python .input}
 import gluonbook as gb
-from mxnet import autograd, gluon, nd
+from mxnet import nd
 from mxnet.gluon import loss as gloss
 ```
 

chapter_deep-learning-basics/softmax-regression-gluon.md

@@ -6,7 +6,7 @@
 
 ```{.python .input  n=1}
 import gluonbook as gb
-from mxnet import autograd, gluon, init, nd
+from mxnet import gluon, init
 from mxnet.gluon import loss as gloss, nn
 ```
 

chapter_natural-language-processing/sentiment-analysis-cnn.md

@@ -28,10 +28,9 @@ TODO(@astonzhang): edits
 ```{.python .input  n=1}
 import collections
 import gluonbook as gb
-import mxnet as mx
 from mxnet import autograd, gluon, init, metric, nd
 from mxnet.contrib import text
-from mxnet.gluon import loss as gloss, nn, rnn
+from mxnet.gluon import loss as gloss, nn
 import os
 import random
 from time import time
@@ -183,7 +182,6 @@ glove_embedding = text.embedding.create(
 import sys
 sys.path.append('..')
 import gluonbook as gb
-from mxnet import autograd, nd
 from mxnet.gluon import nn
 
 def corr1d(X, K):

chapter_natural-language-processing/sentiment-analysis.md

@@ -13,7 +13,6 @@
 ```{.python .input  n=1}
 import collections
 import gluonbook as gb
-import mxnet as mx
 from mxnet import autograd, gluon, init, metric, nd
 from mxnet.contrib import text
 from mxnet.gluon import loss as gloss, nn, rnn

chapter_recurrent-neural-networks/rnn-gluon.md

@@ -11,7 +11,6 @@
 
 ```{.python .input  n=1}
 import gluonbook as gb
-import mxnet as mx
 from mxnet import autograd, gluon, init, nd
 from mxnet.gluon import loss as gloss, nn, rnn, utils as gutils
 import numpy as np