更新时间:2023-12-02 10:17:16
(根据OP对这个问题的评论进行了编辑,他们在其中发布了此链接:
(Edited, according to OP's comment on this question, where they posted this link: https://github.com/fchollet/keras/issues/1920)
您的X
不是单个numpy数组,而是一个数组数组. (否则,其形状将为X.shape=(35730,513,15)
.
Your X
is not a single numpy array, it's an array of arrays. (Otherwise its shape would be X.shape=(35730,513,15)
.
对于fit
方法,它必须是单个numpy数组.由于长度是可变的,因此无法拥有包含所有数据的单个numpy数组,因此必须将其划分为较小的数组,每个数组包含的数据长度均相同.
It must be a single numpy array for the fit
method. Since you have a variable length, you cannot have a single numpy array containing all your data, you will have to divide it in smaller arrays, each array containing data with the same length.
为此,您应该按形状创建字典,然后手动循环字典(可能还有其他更好的方法...):
For that, you should maybe create a dictionary by shape, and loop the dictionary manually (there may be other better ways to do this...):
#code in python 3.5
xByShapes = {}
yByShapes = {}
for itemX,itemY in zip(X,Y):
if itemX.shape in xByShapes:
xByShapes[itemX.shape].append(itemX)
yByShapes[itemX.shape].append(itemY)
else:
xByShapes[itemX.shape] = [itemX] #initially a list, because we're going to append items
yByShapes[itemX.shape] = [itemY]
最后,您循环这本词典进行培训:
At the end, you loop this dictionary for training:
for shape in xByShapes:
model.fit(
np.asarray(xByShapes[shape]),
np.asarray(yByShapes[shape]),...
)
或者,您可以填充数据,以使所有样本都具有相同的长度(使用零或一些虚拟值).
Alternatively, you can pad your data so all samples have the same length, using zeros or some dummy value.
然后在模型中的任何内容之前,可以添加一个Masking
层,该层将忽略这些填充的段. (警告:某些类型的图层不支持遮罩)
Then before anything in your model you can add a Masking
layer that will ignore these padded segments. (Warning: some types of layer don't support masking)