我已经使用 keras
训练了一个图像分类器,它给出了非常好的准确度。我使用 save()
保存模型并使用 h5
格式保存它。如何使用该模型进行预测?
代码是:
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
classifier = 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 ImageDataGenerator
train_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')
提前致谢..!!
最佳答案
第一步是使用 load_model
方法导入您的模型。
from keras.models import load_model
model = load_model('my_model.h5')
然后您必须编译模型才能进行预测。
model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
现在您可以预测
新输入图像的结果。
from keras.preprocessing import image
test_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)
#predict the result
result = model.predict(test_image)
关于python - 如何从 Keras 中保存的模型进行预测?,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/50227925/