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目前我正在一次将所有图像提供给 predict_generator。我希望能够提供一小部分存储在 validation_generator 中的图像并对它们进行预测，这样大型数据集就不会出现内存问题。我应该如何更改以下代码？

top_model_weights_path = '/home/rehan/ethnicity.071217.23-0.28.hdf5'
path = "/home/rehan/countries/pakistan/guys/"
img_width, img_height = 139, 139
confidence = 0.8
model = applications.InceptionResNetV2(include_top=False, weights='imagenet',
                                       input_shape=(img_width, img_height, 3))
print("base pretrained model loaded")


validation_generator = ImageDataGenerator(rescale=1./255).flow_from_directory(path, target_size=(img_width, img_height),
                                                        batch_size=32,shuffle=False)
print("validation_generator")
features = model.predict_generator(validation_generator,steps=10)
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1 回答 1

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我在对象上运行了一个循环，然后将数据存储在一个列表中以消除内存问题。

   validation_generator= ImageDataGenerator(rescale=1./255).flow_from_directory(path, target_size=(img_width, img_height),
                                                                     batch_size=32,shuffle=False)
    prediction_proba1=[]
    prediction_classes1=[]
    print("validation_generator")
    print(len(validation_generator))
    for i in range(len(validation_generator)):
        print (" array coming...")
        #print(validation_generator[i])
        kl = validation_generator[i]
        print(kl)
        print("numpy array")
        print(kl[0])
        features = model.predict_on_batch(kl[0])
        print("features")
        print(features)
        prediction_proba = model1.predict_proba(features)
        prediction_classes = model1.predict_classes(features)
        prediction_classes1.extend(prediction_classes)
        prediction_proba1.extend(prediction_proba)
        #print(prediction_proba1)
        print(prediction_classes1)
于 2018-01-19T14:06:25.923 回答