我使用的库（keras）依赖于具有numpy数组的特定形状。我从未遇到过以下问题：numpy是不是提出了正确的形状？
这是我输出我的ndarray时输出的内容
array([ array([[-0.038548 , 0.54251999, -0.21843 , ..., -0.38580999,
-0.029534 , 0.14425001],
[-0.084961 , 0.50199997, 0.0023823 , ..., -0.037066 ,
0.10436 , 0.17904 ],
[ 0.27204001, -0.06203 , -0.1884 , ..., -0.088243 ,
0.30318001, 0.087747 ],
...,
[ 0.089187 , 0.25792 , 0.26282001, ..., -0.23406 ,
-0.086024 , -0.50603998],
[-0.89548999, 0.38773 , 0.64984 , ..., -0.24102999,
-0.29991001, 0.2174 ],
[-0.086864 , 0.19160999, 0.10915 , ..., -0.47512001,
0.047597 , -0.12785 ]], dtype=float32),
array([[-0.038548 , 0.54251999, -0.21843 , ..., -0.38580999,
-0.029534 , 0.14425001],
[-0.084961 , 0.50199997, 0.0023823 , ..., -0.037066 ,
0.10436 , 0.17904 ],
[ 0.27204001, -0.06203 , -0.1884 , ..., -0.088243 ,
0.30318001, 0.087747 ],
...,
[-0.27142 , 0.047374 , -0.17278001, ..., 0.25861999,
0.06769 , 0.10761 ],
[-0.33340001, 0.45183 , 0.40684 , ..., 0.025225 ,
-0.10559 , -0.058053 ],
[-0.086864 , 0.19160999, 0.10915 , ..., -0.47512001,
0.047597 , -0.12785 ]], dtype=float32),
array([[-0.23205 , 0.47468001, -0.38264 , ..., -0.072927 ,
-0.32218999, -0.065715 ],
[-0.23857 , 0.35457 , -0.30219001, ..., -0.023289 ,
-0.29661 , -0.11824 ],
[ 0.18732999, 0.40595001, -0.51174003, ..., 0.197 ,
-0.48326999, 0.089523 ],
...,
[ 0.043798 , 0.024779 , -0.20937 , ..., 0.010196 ,
0.37641999, -0.01946 ],
[ 0.37004 , 0.089479 , 0.53771001, ..., -0.17144001,
-0.41093001, 0.97061002],
[-0.086864 , 0.19160999, 0.10915 , ..., -0.47512001,
0.047597 , -0.12785 ]], dtype=float32),
...,
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1.79040000e-01],
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array([[-0.038548 , 0.54251999, -0.21843 , ..., -0.38580999,
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[-0.084961 , 0.50199997, 0.0023823 , ..., -0.037066 ,
0.10436 , 0.17904 ],
[ 0.27204001, -0.06203 , -0.1884 , ..., -0.088243 ,
0.30318001, 0.087747 ],
...,
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[-0.086864 , 0.19160999, 0.10915 , ..., -0.47512001,
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基本上，它是一个数组列表的数组（传递给矩阵的pandas系列的行）。形状输出为：
(404290,)
为什么不能检测到我想要一个三维数组呢？
编辑：改变了一些东西，因为我是个白痴，但在这里有同样的问题。
Edit2：这就是我如何创建数组
train['question1_vector'] = train['question1_nlp'].apply(lambda x: np.array([k.vector[:100] for k in x]))
基本上，我会从每个句子的句子中提取所有单词向量（最多100个窗口）。
然后我打电话给
train['question1_vector'].values
尝试获取值。
形状应该像
(404290, len_of_sentence, 100)
答案 0 :(得分：0)
我们假设你有这样的数据：
a = pd.Series(np.arange(1,3))
b = a.apply(lambda n: n * np.arange(3*4).reshape(4,-1))
b.values现在是：
[ array([[ 0, 1, 2],
[ 3, 4, 5],
[ 6, 7, 8],
[ 9, 10, 11]])
array([[ 0, 2, 4],
[ 6, 8, 10],
[12, 14, 16],
[18, 20, 22]])]
你想＆＃34;堆叠＆＃34;这两个2D阵列可以制作单个3D阵列。例如，11应该在22的顶部，并且与那些相邻，10应该在20之上。
使用np.stack(b, 0)：
[ array([[ 0, 1, 2],
[ 3, 4, 5],
[ 6, 7, 8],
[ 9, 10, 11]])
array([[ 0, 2, 4],
[ 6, 8, 10],
[12, 14, 16],
[18, 20, 22]])]