我试图微调VGG 16模型以预测12个类别，而我所拥有的类别与imagenet不同，这就是为什么试图微调，该模型产生以下输出的损失非常高在第一个时期的准确性较低。我只是认为，到第10个时代结束时，准确性将无法确定。
我想知道这是正常现象还是做错了事
进度输出
1/362 [..............................] - ETA: 17:00 - loss: 4.2610 - acc: 0.0625
2/362 [..............................] - ETA: 16:06 - loss: 381.0046 - acc: 0.0312
3/362 [..............................] - ETA: 15:44 - loss: 298.8458 - acc: 0.0208
4/362 [..............................] - ETA: 15:35 - loss: 226.6889 - acc: 0.0156
5/362 [..............................] - ETA: 15:29 - loss: 182.2427 - acc: 0.1125
6/362 [..............................] - ETA: 15:22 - loss: 160.5883 - acc: 0.0938
7/362 [..............................] - ETA: 15:17 - loss: 138.1007 - acc: 0.1562
8/362 [..............................] - ETA: 15:13 - loss: 121.4596 - acc: 0.1367
9/362 [..............................] - ETA: 15:10 - loss: 108.2340 - acc: 0.1285
10/362 [..............................] - ETA: 15:05 - loss: 97.6966 - acc: 0.1156
11/362 [..............................] - ETA: 15:02 - loss: 89.1747 - acc: 0.1080
12/362 [..............................] - ETA: 14:58 - loss: 82.0693 - acc: 0.1016
13/362 [>.............................] - ETA: 14:54 - loss: 75.9421 - acc: 0.0962
14/362 [>.............................] - ETA: 14:50 - loss: 70.7751 - acc: 0.0915
15/362 [>.............................] - ETA: 14:47 - loss: 66.2048 - acc: 0.0875
16/362 [>.............................] - ETA: 14:42 - loss: 62.3336 - acc: 0.0820
17/362 [>.............................] - ETA: 14:37 - loss: 58.7955 - acc: 0.0809
18/362 [>.............................] - ETA: 14:32 - loss: 55.6291 - acc: 0.0990
19/362 [>.............................] - ETA: 14:27 - loss: 53.4754 - acc: 0.0938
20/362 [>.............................] - ETA: 14:22 - loss: 50.9502 - acc: 0.0922
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22/362 [>.............................] - ETA: 14:14 - loss: 49.6734 - acc: 0.0838
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25/362 [=>............................] - ETA: 14:03 - loss: 44.1554 - acc: 0.0887
26/362 [=>............................] - ETA: 14:01 - loss: 42.5939 - acc: 0.0853
27/362 [=>............................] - ETA: 13:59 - loss: 41.5915 - acc: 0.0822
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29/362 [=>............................] - ETA: 13:52 - loss: 39.3127 - acc: 0.0873
30/362 [=>............................] - ETA: 13:49 - loss: 38.1391 - acc: 0.0844
31/362 [=>............................] - ETA: 13:47 - loss: 37.0256 - acc: 0.0837
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33/362 [=>............................] - ETA: 13:42 - loss: 34.9967 - acc: 0.0795
34/362 [=>............................] - ETA: 13:39 - loss: 34.0358 - acc: 0.0846
35/362 [=>............................] - ETA: 13:37 - loss: 33.1187 - acc: 0.1000
36/362 [=>............................] - ETA: 13:34 - loss: 32.2891 - acc: 0.0998
37/362 [==>...........................] - ETA: 13:32 - loss: 31.4585 - acc: 0.1115
38/362 [==>...........................] - ETA: 13:29 - loss: 30.7113 - acc: 0.1086
39/362 [==>...........................] - ETA: 13:27 - loss: 30.3106 - acc: 0.1074
40/362 [==>...........................] - ETA: 13:24 - loss: 29.6147 - acc: 0.1047
41/362 [==>...........................] - ETA: 13:22 - loss: 28.9527 - acc: 0.1052
42/362 [==>...........................] - ETA: 13:20 - loss: 28.3151 - acc: 0.1042
43/362 [==>...........................] - ETA: 13:18 - loss: 27.8628 - acc: 0.1017
44/362 [==>...........................] - ETA: 13:16 - loss: 27.3071 - acc: 0.0994
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54/362 [===>..........................] - ETA: 12:51 - loss: 23.7437 - acc: 0.0972
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58/362 [===>..........................] - ETA: 12:41 - loss: 22.4216 - acc: 0.0911
59/362 [===>..........................] - ETA: 12:38 - loss: 22.1043 - acc: 0.0895
60/362 [===>..........................] - ETA: 12:36 - loss: 21.7761 - acc: 0.0938
61/362 [====>.........................] - ETA: 12:33 - loss: 21.4751 - acc: 0.0922
62/362 [====>.........................] - ETA: 12:30 - loss: 21.2513 - acc: 0.0907
63/362 [====>.........................] - ETA: 12:27 - loss: 20.9492 - acc: 0.0918
64/362 [====>.........................] - ETA: 12:24 - loss: 20.6726 - acc: 0.0903
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73/362 [=====>........................] - ETA: 11:58 - loss: 18.5105 - acc: 0.0967
74/362 [=====>........................] - ETA: 11:55 - loss: 18.2603 - acc: 0.1090
75/362 [=====>........................] - ETA: 11:52 - loss: 18.0168 - acc: 0.1208
76/362 [=====>........................] - ETA: 11:49 - loss: 18.4406 - acc: 0.1192
77/362 [=====>........................] - ETA: 11:46 - loss: 18.2507 - acc: 0.1185
78/362 [=====>........................] - ETA: 11:43 - loss: 18.0736 - acc: 0.1170
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84/362 [=====>........................] - ETA: 11:26 - loss: 17.0346 - acc: 0.1135
85/362 [======>.......................] - ETA: 11:23 - loss: 17.0110 - acc: 0.1132
86/362 [======>.......................] - ETA: 11:20 - loss: 16.9285 - acc: 0.1119
87/362 [======>.......................] - ETA: 11:18 - loss: 16.7656 - acc: 0.1171
88/362 [======>.......................] - ETA: 11:15 - loss: 16.8805 - acc: 0.1158
89/362 [======>.......................] - ETA: 11:13 - loss: 16.7179 - acc: 0.1145
90/362 [======>.......................] - ETA: 11:10 - loss: 16.6223 - acc: 0.1135
91/362 [======>.......................] - ETA: 11:08 - loss: 16.4856 - acc: 0.1123
92/362 [======>.......................] - ETA: 11:06 - loss: 16.3405 - acc: 0.1131
93/362 [======>.......................] - ETA: 11:03 - loss: 16.2040 - acc: 0.1119
94/362 [======>.......................] - ETA: 11:00 - loss: 16.0733 - acc: 0.1107
95/362 [======>.......................] - ETA: 10:58 - loss: 15.9401 - acc: 0.1109
96/362 [======>.......................] - ETA: 10:55 - loss: 15.8051 - acc: 0.1136
97/362 [=======>......................] - ETA: 10:53 - loss: 15.6705 - acc: 0.1169
98/362 [=======>......................] - ETA: 10:50 - loss: 15.5265 - acc: 0.1234
99/362 [=======>......................] - ETA: 10:48 - loss: 15.4323 - acc: 0.1222
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101/362 [=======>......................] - ETA: 10:43 - loss: 15.2053 - acc: 0.1197
102/362 [=======>......................] - ETA: 10:41 - loss: 15.0870 - acc: 0.1192
103/362 [=======>......................] - ETA: 10:38 - loss: 14.9731 - acc: 0.1186
104/362 [=======>......................] - ETA: 10:36 - loss: 14.8524 - acc: 0.1175
105/362 [=======>......................] - ETA: 10:34 - loss: 14.7380 - acc: 0.1208
106/362 [=======>......................] - ETA: 10:31 - loss: 14.6252 - acc: 0.1197
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108/362 [=======>......................] - ETA: 10:26 - loss: 14.4181 - acc: 0.1183
109/362 [========>.....................] - ETA: 10:24 - loss: 14.3249 - acc: 0.1173
110/362 [========>.....................] - ETA: 10:21 - loss: 14.2265 - acc: 0.1162
111/362 [========>.....................] - ETA: 10:18 - loss: 14.1234 - acc: 0.1151
112/362 [========>.....................] - ETA: 10:16 - loss: 14.0295 - acc: 0.1150
113/362 [========>.....................] - ETA: 10:14 - loss: 13.9287 - acc: 0.1139
114/362 [========>.....................] - ETA: 10:11 - loss: 13.8335 - acc: 0.1132
115/362 [========>.....................] - ETA: 10:09 - loss: 13.7322 - acc: 0.1174
116/362 [========>.....................] - ETA: 10:06 - loss: 13.6475 - acc: 0.1164
117/362 [========>.....................] - ETA: 10:04 - loss: 13.6022 - acc: 0.1154
118/362 [========>.....................] - ETA: 10:01 - loss: 13.5110 - acc: 0.1144
模型
image_size = 150
train_batchsize = 32
val_batchsize = 32
class_weights = {}
from collections import Counter
def create_model():
vgg_conv = VGG16(include_top=False, weights='imagenet', input_shape=(image_size, image_size, 3))
# Freeze the layers except the last 4 layers
for layer in vgg_conv.layers[:-4]:
layer.trainable = False
model = Sequential()
model.add(vgg_conv)
# model.add(Flatten())
model.add(GlobalAveragePooling2D())
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(12, activation='softmax'))
return model
def train_top_model():
# STEP 1 : GENERATE MODEL
model = create_model()
# Show a summary of the model. Check the number of trainable parameters
model.summary()
data_gen = ImageDataGenerator(
rescale=1. / 255,
)
train_generator = data_gen.flow_from_directory(
train_data_dir,
batch_size=train_batchsize,
target_size=(image_size, image_size),
class_mode='categorical',
shuffle=False)
validation_generator = data_gen.flow_from_directory(
validation_data_dir,
batch_size=val_batchsize,
target_size=(image_size, image_size),
class_mode='categorical',
shuffle=False)
class_weights = get_class_weights(train_generator)
# Compile the model
model.compile(loss='categorical_crossentropy',
optimizer=k.optimizers.RMSprop(lr=1e-4),
metrics=['acc'])
# Train the model
history = model.fit_generator(
train_generator,
steps_per_epoch=train_generator.samples / train_generator.batch_size,
epochs=10,
validation_data=validation_generator,
validation_steps=validation_generator.samples / validation_generator.batch_size,
class_weight=class_weights,
verbose=1)
答案 0 :(得分：1)
快速解答：您应将rescale=1. / 255行替换为preprocessing_function=lambda x: x - np.array([103.939, 116.779, 123.68])。您的代码的输入规范化不正确。有关更多信息，请检查Loading Data into a Table。
让我列出您在微调/转移学习中应该寻找的内容的清单。突出显示的项目是导致您出现问题的项目：