从加载的 Keras 模型调用“predict_generator”时出现属性错误

我已经训练了一个双输入神经网络来对图像进行分类并将权重保存到一个 hdf5 文件中。

我现在正在尝试加载这个网络并使用“predict_generator”来查看它在我的测试集上的表现。但是，在调用“predict_generator”时，出现以下错误：

Traceback (most recent call last):
   File "load_cnn.py", line 75, in <module>
     pred = loaded_model.predict_generator(test_gen, steps=36, verbose=1)
   File 
"/home/ppxjm4/anaconda3/envs/ML/lib/python3.7/site-packages/keras/legacy/interfaces.py", 
line 91, in wrapper
     return func(*args, **kwargs)
   File 
"/home/ppxjm4/anaconda3/envs/ML/lib/python3.7/site-packages/keras/engine/training.py", 
line 1772, in predict_generator
     verbose=verbose)
   File 
"/home/ppxjm4/anaconda3/envs/ML/lib/python3.7/site-packages/keras/engine/training_generator.py", 
line 503, in predict_generator
     batch_size = x[0].shape[0]
AttributeError: 'list' object has no attribute 'shape'

我很困惑为什么它在这里抱怨，在训练和调用 'fit_generator()' 似乎工作得很好时。

这是我生成错误的代码：

import os
import pandas as pd
import numpy as np

import keras
from keras import optimizers
from keras.utils import to_categorical
from keras.layers import Input, Dense, Flatten, Dropout, Conv2D, MaxPooling2D, GlobalAveragePooling2D, Activation, concatenate
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.normalization import BatchNormalization
from keras.models import Model
from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau

from keras_preprocessing.image import ImageDataGenerator

train_df = pd.read_pickle("collated_data.pkl")
train_df = train_df.sample(frac=1)

data_gen = ImageDataGenerator(rescale=1./255)

from keras.models import model_from_json

json_file = open('model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()

loaded_model = model_from_json(loaded_model_json)
loaded_model.load_weights("weights.best.hdf5")

print("loaded model from disk!")

loaded_model.compile(loss='categorical_crossentropy', optimizer='Adadelta', metrics=['accuracy'])

def generator_multiple(generator, dataframe, mode, batch_size, img_height, img_width):

    if mode == 'train':

        genX1 = generator.flow_from_dataframe(
            dataframe=dataframe[:79000], x_col='reconstruction', y_col='label', class_mode='categorical',
            batch_size=batch_size, shuffle=False, target_size=(img_height, img_width), color_mode='grayscale')

        genX2 = generator.flow_from_dataframe(
            dataframe=dataframe[:79000], x_col='observation', y_col='label', class_mode='categorical',
            batch_size=batch_size, shuffle=False, target_size=(img_height, img_width), color_mode='grayscale')

    if mode == 'validate':

        genX1 = generator.flow_from_dataframe(
            dataframe=dataframe[79000:85000], x_col='reconstruction', y_col='label', class_mode='categorical',
            batch_size=batch_size, shuffle=False, target_size=(img_height, img_width), color_mode='grayscale')

        genX2 = generator.flow_from_dataframe(
            dataframe=dataframe[79000:85000], x_col='observation', y_col='label', class_mode='categorical',
            batch_size=batch_size, shuffle=False, target_size=(img_height, img_width), color_mode='grayscale')

    if mode == 'test':

        genX1 = generator.flow_from_dataframe(
            dataframe=dataframe[85000:], x_col='reconstruction', y_col=None, class_mode=None,
            batch_size=batch_size, shuffle=False, target_size=(img_height, img_width), color_mode='grayscale')

        genX2 = generator.flow_from_dataframe(
            dataframe=dataframe[85000:], x_col='observation', y_col=None, class_mode=None,
            batch_size=batch_size, shuffle=False, target_size=(img_height, img_width), color_mode='grayscale')

    while True:
        X1i = genX1.next()
        X2i = genX2.next()
        yield [[X1i[0], X2i[0]], X2i[1]]


test_gen = generator_multiple(data_gen, dataframe=train_df, mode='test', batch_size=36,
                               img_height=100, img_width=100)

pred = loaded_model.predict_generator(test_gen, steps=36, verbose=1)

predicted_class_indices = np.argmax(pred, axis=1)
labels = (train_gen.class_indices)
labels = dict((v,k) for k,v in labels.items())
predictions = [labels[k] for k in predicted_class_indices]

filenames=test_gen.filenames
results=pd.DataFrame({"Filename":filenames,
                      "Predictions":predictions})
results.to_csv("results.csv",index=False)