在 keras 中加载测试图像

Question

我正在训练一个包含 10 个类的 CNN。训练文件夹每个类有 40 个图像，验证文件夹每个类有 10 个图像。我有一个包含 100 个测试图像的文件夹。如何加载它们（通过使用 imagedatagenerator），然后使用我训练的模型进行预测？每次我为相同的测试数据运行 model.predict() 时，我都会得到不同的预测。这是我正在使用的数据集的链接 https://www.kaggle.com/s214316001/datasets214 。这是我的代码

import tensorflow as tf
import matplotlib.pyplot as plt
import cv2
import os
import numpy as np
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.preprocessing import image
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D,BatchNormalization,Dropout

train = ImageDataGenerator(rescale = 1/255)
train_dataset = train.flow_from_directory('../input/datasets214/train/train', 
                                     target_size = (200,200),batch_size = 5,
                                     class_mode = 'categorical')
validation_dataset=train.flow_from_directory('../input/datasets214/validation/validation', 
                                     target_size = (200,200),batch_size = 5,
                                     class_mode = 'categorical')


model = Sequential()

model.add(Conv2D(16, (3, 3), input_shape=(200,200,3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dense(10, activation='softmax'))



model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
model.fit(train_dataset,steps_per_epoch=5,epochs=300,validation_data=validation_dataset)
#prediction
datagen = ImageDataGenerator(rescale=1/255)
gen =datagen.flow_from_directory('../input/datasets214/gnr_test/gnr_test',shuffle = 'False',batch_size=100,
                              target_size = (200,200),classes = ['test'])
predict = model.predict(gen)
print('      fileID','label')
for file,i in enumerate(gen.filenames):

   j = predict[file]
   k = list(j).index(max(j)) 




   print( i,k)

Answer 1

在 gen 你设置了 shuffle=False