我在尝试在keras中自定义一个特殊的注意力层。但是尝试了很多方法后，我仍然很困惑为什么总是会出现这个错误。
Traceback (most recent call last): File "D:/Users/LawLi/PyCharmProjects/fixed_talentDNA/adx.py", line 52, in <module> print(model.predict([tensor1, tensor2, indices])) # (bs1, sl1, sl2) File "D:\Users\LawLi\Anaconda3\lib\site-packages\keras\engine\training.py", line 1172, in predict steps=steps) File "D:\Users\LawLi\Anaconda3\lib\site-packages\keras\engine\training_arrays.py", line 293, in predict_loop ins_batch = slice_arrays(ins, batch_ids) File "D:\Users\LawLi\Anaconda3\lib\site-packages\keras\utils\generic_utils.py", line 507, in slice_arrays return [None if x is None else x[start] for x in arrays] File "D:\Users\LawLi\Anaconda3\lib\site-packages\keras\utils\generic_utils.py", line 507, in <listcomp> return [None if x is None else x[start] for x in arrays]IndexError: index 4 is out of bounds for axis 0 with size 4这是我关于自定义层的测试代码，代码中包含了一个预测的示例。可以直接运行。
from keras.layers import *from keras.models import Modelfrom keras.utils import to_categoricalfrom keras.layers.merge import *class CustomLayer(Layer): def __init__(self, **kwargs): self.supports_masking = True super(CustomLayer, self).__init__(**kwargs) def build(self, input_shape): assert len(input_shape) == 3 super(CustomLayer, self).build(input_shape) def compute_mask(self, inputs, mask=None): return None def call(self, x, mask=None): tensor1, tensor2, ind = x[0], x[1], x[2] # (bs1, sl1, wd) (bs2, sl2, wd) (bs1, bs2. sl1, sl2) tensor2 = K.permute_dimensions(tensor2, [0, 2, 1]) align = K.dot(tensor1, tensor2) align = K.permute_dimensions(align, [0, 2, 1, 3]) # (bs1, bs2, sl1, sl2) align = align + ind align = K.max(align, axis=1) align = K.sum(align, axis=2) align = K.softmax(align, axis=1) weighted_ans = tensor1 * K.expand_dims(align, 2) return K.sum(weighted_ans, axis=1) def compute_output_shape(self, input_shape): t1_shape, t2_shape = input_shape[0], input_shape[1] return t1_shape[0], t1_shape[1], t1_shape[2]# model examplet1 = Input(shape=(7, 3))t2 = Input(batch_shape=(4, 6, 3))t3 = Input(shape=(4, 7, 6))output = CustomLayer()([t1, t2, t3])model = Model([t1, t2, t3], output)# data exampletensor1 = np.random.rand(10, 7, 3) # (bs1, sl1, wd)tensor2 = np.random.rand(4, 6, 3) # (bs2, sl2, wd)indices = np.array([0, 1, 3, 2, 0, 1, 2, 2, 3, 1]) # (bs1, 1)indices = to_categorical(indices, num_classes=4) * 999 - 999 # (bs1, bs2)indices = np.expand_dims(indices, axis=2)indices = np.expand_dims(indices, axis=3)indices = np.repeat(indices, 7, axis=2).repeat(6, axis=3)print(model.predict([tensor1, tensor2, indices])) # (bs1, sl1, wd)谢谢你的帮助。
回答：
预测函数无法接受第0轴大小不同的输入。因此，我尝试了另一个名为predict_on_batch()的函数
