这个问题涉及到数组的广播(broadcasting)操作。广播是指在进行元素级别的运算时,将不同形状的数组自动调整为相同形状的过程。
在这个问题中,输入数组的形状为(164,14),而目标形状为(164)。根据广播规则,当两个数组的维度不同时,较小的数组会在其维度上进行扩展,使其与较大数组的维度相匹配。在这种情况下,输入数组会在第二个维度上进行扩展,使其形状变为(164,1)。
这样,输入数组的形状就可以与目标形状匹配,可以进行元素级别的运算。具体的操作可以使用NumPy库来实现,以下是一个示例代码:
import numpy as np
input_array = np.random.rand(164, 14) # 输入数组,形状为(164, 14)
target_shape = (164,) # 目标形状为(164)
# 使用广播操作将输入数组的形状调整为目标形状
broadcasted_array = np.reshape(input_array, target_shape)
# 进行其他操作,如元素级别的运算等
# ...
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