我正在按照[这个教程](https://towardsdatascience.com/keras-transfer-learning-for-beginners-6c9b8b7143e)使用迁移学习方法来使用Keras中的预训练VGG19模型。教程展示了如何训练模型，但没有展示如何为预测准备测试图像。
在评论部分中提到：
获取一张图像，使用相同的
preprocess_image函数预处理图像，然后调用model.predict(image)。这将给出模型对该图像的预测。使用argmax(prediction)，你可以找到图像所属的类别。
我无法在代码中找到名为preprocess_image的函数。我进行了一些搜索，并考虑使用这个教程提出的方法。
但这导致了一个错误，提示如下：
decode_predictions expects a batch of predictions (i.e. a 2D array of shape (samples, 1000)). Found array with shape: (1, 12)我的数据集有12个类别。以下是训练模型的完整代码以及我如何得到这个错误的：
import pandas as pdimport numpy as npimport osimport kerasimport matplotlib.pyplot as pltfrom keras.layers import Dense, GlobalAveragePooling2Dfrom keras.applications.vgg19 import VGG19from keras.preprocessing import imagefrom keras.applications.vgg19 import preprocess_inputfrom keras.preprocessing.image import ImageDataGeneratorfrom keras.models import Modelfrom keras.optimizers import Adambase_model = VGG19(weights='imagenet', include_top=False)x=base_model.output x=GlobalAveragePooling2D()(x) x=Dense(1024,activation='relu')(x) x=Dense(1024,activation='relu')(x) x=Dense(512,activation='relu')(x) preds=Dense(12,activation='softmax')(x) model=Model(inputs=base_model.input,outputs=preds) # view the layer architecture# for i,layer in enumerate(model.layers):# print(i,layer.name)for layer in model.layers: layer.trainable=Falsefor layer in model.layers[:20]: layer.trainable=Falsefor layer in model.layers[20:]: layer.trainable=Truetrain_datagen=ImageDataGenerator(preprocessing_function=preprocess_input)train_generator=train_datagen.flow_from_directory('dataset', target_size=(96,96), # 224, 224 color_mode='rgb', batch_size=64, class_mode='categorical', shuffle=True)model.compile(optimizer='Adam',loss='categorical_crossentropy',metrics=['accuracy'])step_size_train=train_generator.n//train_generator.batch_sizemodel.fit_generator(generator=train_generator, steps_per_epoch=step_size_train, epochs=5)# model.predict(new_image)IPython:
In [3]: import classify_tl Found 4750 images belonging to 12 classes.Epoch 1/574/74 [==============================] - 583s 8s/step - loss: 2.0113 - acc: 0.4557Epoch 2/574/74 [==============================] - 576s 8s/step - loss: 0.8222 - acc: 0.7170Epoch 3/574/74 [==============================] - 563s 8s/step - loss: 0.5875 - acc: 0.7929Epoch 4/574/74 [==============================] - 585s 8s/step - loss: 0.3897 - acc: 0.8627Epoch 5/574/74 [==============================] - 610s 8s/step - loss: 0.2689 - acc: 0.9071In [6]: model = classify_tl.model In [7]: print(model) <keras.engine.training.Model object at 0x7fb3ad988518>In [8]: from keras.preprocessing.image import load_img In [9]: image = load_img('examples/0021e90e4.png', target_size=(96,96)) In [10]: from keras.preprocessing.image import img_to_array In [11]: image = img_to_array(image) In [12]: image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2])) In [13]: from keras.applications.vgg19 import preprocess_input In [14]: image = preprocess_input(image) In [15]: yhat = model.predict(image) In [16]: print(yhat) [[1.3975363e-06 3.1069856e-05 9.9680350e-05 1.7175063e-03 6.2767825e-08 2.6133494e-03 7.2859187e-08 6.0187017e-07 2.0794137e-06 1.3714411e-03 9.9416250e-01 2.6067207e-07]]In [17]: from keras.applications.vgg19 import decode_predictions In [18]: label = decode_predictions(yhat) IPython提示中的最后一行导致了以下错误：
ValueError: `decode_predictions` expects a batch of predictions (i.e. a 2D array of shape (samples, 1000)). Found array with shape: (1, 12)我应该如何正确地输入我的测试图像并获得预测结果？
回答：
decode_predictions用于解码模型在ImageNet数据集上的预测，该数据集有1000个类别。然而，你的微调模型只有12个类别。因此，这里使用decode_predictions没有意义。当然，你一定知道这12个类别的标签。因此，只需在预测中找到最大分数的索引并找到其标签：
# 创建一个包含类别标签的列表class_labels = ['class1', 'class2', 'class3', ...., 'class12']# 找到最大分数类别的索引pred = np.argmax(class_labels, axis=-1)# 打印最大分数类别的标签print(class_labels[pred[0]])