从你的问题来看，你通过了多少张图片，以及你的问题中有多少个分类类别，这并不清楚。我将试着在一般意义上回答。
假设你将一批4个图像传递给模型restnet50和resnet152，并且它们都在5个类别上训练，则每个模型将给予具有形状（4，5）的预测。

>> predictions1.shape
(4,5)
>> predictions3.shape
(4,5)

要将它们组合起来以获得多数票，您应该编写如下代码：

>>predictions = np.dstack((predictions1, predictions3)) # stack along the third axis.
>>predictions.shape
(4,5,2)
>> mean_predictions = np.mean(predictions, axis=2) # find the mean probabilities of both predictions in each category.
>> mean_predictions.shape
(4,5)
>> class_index = np.argmax(mean_predictions,axis=1) # Find the index having highest probability.
>> class_index.shape
(4,)

最后的class_index给出了4张图片中每一张图片所属的类或类别的索引。我希望这能有所帮助。

更好的方式您可以使用更好的方式在tensorflow 中创建集合模型，如下所示：

from tensorflow.keras.applications.resnet50 import preprocess_input as process_resnet50
from tensorflow.keras.applications.resnet150 import preprocess_input as process_resnet150
from tensorflow.keras.layers import Lambda,Dense,GlobalMaxPool2D,Concatenate
from tensorflow.keras import Input, Model
from keras.models import load_model

resnet50 = keras.models.load_model('/content/drive/MyDrive/HDF5 Files/RestNet 50 best_model.hdf5')
resnet152 = keras.models.load_model('/content/drive/MyDrive/HDF5 Files/best_model_4.hdf5')

category_count = 5 # you can give your own category count.
inp = Input(input_shape)
resnet_prep50 = tf.keras.layers.Lambda(process_resnet50)(inp)
res_net50 = resnet50(resnet_prep50)
x_resnet50 = GlobalMaxPool2D()(res_net50)
x_resnet50 = Dense(128, activation='relu')(x_resnet50)

resnet_prep150 = tf.keras.layers.Lambda(process_resnet150)(inp)
res_net150 = resnet152(resnet_prep150)
x_resnet150 = GlobalMaxPool2D()(res_net150)
x_resnet150 = Dense(128, activation='relu')(x_resnet150)

x = Concatenate()([x_resnet50, x_resnet150])
out = Dense(category_count, activation='softmax')(x)

ensemble_model = Model(inputs=inp, outputs = out)
predictions = ensemble_model.predict(images)
predictions = np.argmax(predictions, axis=1)