谷歌colab内存问题
我试图理解为什么以下代码会导致我的 Colab 会话崩溃。
import numpy as np
import tensorflow as tf
x1 = np.random.rand(90000)
x2 = tf.random.uniform((90000,1)).numpy()
print(x1.shape, type(x1))
print(x2.shape, type(x2))
x1 - x2
我可以看到内存正在爆炸，导致崩溃，但我希望有人能准确解释为什么会发生这种情况。我也知道这与 numpy 中的广播数组有关，我只是想知道这是否是预期的行为，以便我将来可以避免它。
修复方法是 np.squeze(x2, axis=1) ，因此向量具有相同的形状，但显然我不明白 numpy 正在做什么引擎盖。欢迎任何建议和澄清。
I am trying to understand why the following code crashes my Colab session.
import numpy as np
import tensorflow as tf
x1 = np.random.rand(90000)
x2 = tf.random.uniform((90000,1)).numpy()
print(x1.shape, type(x1))
print(x2.shape, type(x2))
x1 - x2
I can see that memory is exploding which causes the crash but I was hoping someone can explain exactly why this is happening. I also understand that this has to do with broadcasting arrays in numpy and I am just wondering if this is expected behavior so I can avoid it in the future.
The fix is to np.squeze(x2, axis=1) so the vectors have the same shape but clearly there's something I don't understand about what numpy is doing under the hood. Any suggestions and clarifications welcome.
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x1的形状为(90000,)。x2的形状为(90000, 1)。在表达式x1 - x2中，广播 发生（正如您所怀疑的），给出形状为(90000, 90000)的结果。这样的浮点值数组需要90000*90000*8 = 64800000000字节。x1has shape(90000,).x2has shape(90000, 1). In the expressionx1 - x2, broadcasting occurs (as you suspected), giving a result that has shape(90000, 90000). Such an array of floating point values requires90000*90000*8 = 64800000000bytes.