我已经使用keras训练了一个图像分类器，并且它的准确率非常高。我使用save()方法保存了模型，并以h5格式存储。如何使用这个模型进行预测呢？

代码如下：

from keras.models import Sequentialfrom keras.layers import Conv2Dfrom keras.layers import MaxPooling2Dfrom keras.layers import Flattenfrom keras.layers import Denseclassifier = Sequential()classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation =   'relu'))classifier.add(MaxPooling2D(pool_size = (2, 2)))classifier.add(Conv2D(32, (3, 3), activation = 'relu'))classifier.add(MaxPooling2D(pool_size = (2, 2)))classifier.add(Flatten())classifier.add(Dense(units = 128, activation = 'relu'))classifier.add(Dense(units = 1, activation = 'sigmoid'))classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])from keras.preprocessing.image import ImageDataGeneratortrain_datagen = ImageDataGenerator(rescale = 1./255,shear_range = 0.2,zoom_range = 0.2,horizontal_flip = True)test_datagen = ImageDataGenerator(rescale = 1./255)training_set = train_datagen.flow_from_directory('training_set',target_size = (64, 64),batch_size = 32,class_mode = 'binary')test_set = test_datagen.flow_from_directory('test_set',target_size = (64, 64),batch_size = 32,class_mode = 'binary')classifier.fit_generator(training_set,steps_per_epoch = 8000,epochs = 5,validation_data = test_set,validation_steps = 2000)classifier.save('classifier.h5')

提前感谢..!!

回答：

第一步是使用load_model方法导入你的模型。

from keras.models import load_modelmodel = load_model('my_model.h5')

然后你需要编译模型以便进行预测。

model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])

现在你可以对新的输入图像predict结果。

from keras.preprocessing import imagetest_image = image.load_img(imagePath, target_size = (64, 64)) test_image = image.img_to_array(test_image)test_image = np.expand_dims(test_image, axis = 0)#预测结果result = model.predict(test_image)