我在通过迁移学习的VGG16网络中进行预测时遇到问题。我有一个与亚当一起训练的模型,该模型共7个班级。使用imageDataGenerator用fit_generator()进行了训练。我正在使用以下方式加载模型:
# load the model we saved
model = load_model('models/vgg16_9.h5')
model.compile(loss='categorical_crossentropy',
optimizer=optimizers.Adam(lr=1e-4),
metrics=['acc'])
然后尝试做出预测。首先,我使用predict_generator()制作了一个带有结果的.CSV文件:
test_datagen = ImageDataGenerator(rescale = 1./255)
test_generator = test_datagen.flow_from_directory("dataset/test_set",
target_size=(227, 454),
batch_size=1,
class_mode=None,
shuffle=False,
seed=42)
test_generator.reset()
pred = model.predict_generator(test_generator, verbose = 1)
predicted_class_indices = np.argmax(pred, axis = 1)
labels = (valid_generator.class_indices)
labels = dict((v,k) for k,v in labels.items())
predictions = [labels[k] for k in predicted_class_indices]
filenames=test_generator.filenames
results=pd.DataFrame({"Filename":filenames,
"Predictions":predictions})
results.to_csv("results.csv",index=False)
可以,我得到的结果是:
...
Filename,Predictions
test\green.1191.png,Green
test\green.1195.png,Green
test\green.1196.png,Green
test\green.1197.png,Green
test\green.1198.png,OK
test\green.1199.png,Green
test\green.1200.png,Green
test\green.1201.png,Green
test\green.1202.png,OK
test\green.1203.png,Green
test\green.1204.png,OK
test\green.1205.png,Green
test\green.1206.png,Green
test\green.1207.png,Green
...
但是当我尝试使用以下方法进行单个图像预测时:
# predicting images
test_image = image.load_img('dataset/test_set/test/green.1230.png', target_size = (227, 454))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis = 0)
result = model.predict(test_image, verbose=1)
y_pred = np.argmax(result, axis = 1)
我的y_pred是[6],如果我正确阅读class_indices,则[6]完全是其他类(predict_generator()做得很好)。
类索引:
class_names = (valid_generator.class_indices)
class_names = dict((v,k) for k,v in class_names.items())
class_names_list = []
temp = []
for key, value in class_names.items():
temp = value
class_names_list.append(temp)
给我:
{0: 'Green', 1: 'Half', 2: 'Moldy', 3: 'NoEmbryo', 4: 'OK', 5: 'Organic', 6: 'Stones'}
我在做什么错了?
答案 0 :(得分:0)
您的问题可能源于python dict到列表转换。 当您的预测来自predict_generator()时,它就会从按键进行预测中选择。
在第二个示例中,您将其转换为列表并使用列表的索引,这将是其他结果。
我并没有真正的目的:
for key, value in class_names.items():
temp = value
class_names_list.append(temp)
但是,如果您从class_names_list获得类结果,则会得到错误的结果。所以:
y_pred = np.argmax(result, axis = 1)
class_names[y_pred]
应该给您正确的值。