我正在尝试使用LFW数据集微调VGG模型的最后两层，我已经通过删除原始图层来更改softmax图层尺寸，并在我的情况下添加了带有19个输出的softmax图层，因为有19个类我正在努力训练。 我还想微调最后一个完全连接的层，以便制作一个“自定义特征提取器”
我正在设置我想要不可训练的图层：
for layer in model.layers:
layer.trainable = False
使用gpu我需要每个时期1小时训练19节课，每节课最少40张。
由于我没有很多样本，这种训练表现有点奇怪。
任何人都知道为什么会这样？
这里是日志：
Image shape: (224, 224, 3)
Number of classes: 19
K.image_dim_ordering: th
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
input_1 (InputLayer) (None, 3, 224, 224) 0
____________________________________________________________________________________________________
conv1_1 (Convolution2D) (None, 64, 224, 224) 1792 input_1[0][0]
____________________________________________________________________________________________________
conv1_2 (Convolution2D) (None, 64, 224, 224) 36928 conv1_1[0][0]
____________________________________________________________________________________________________
pool1 (MaxPooling2D) (None, 64, 112, 112) 0 conv1_2[0][0]
____________________________________________________________________________________________________
conv2_1 (Convolution2D) (None, 128, 112, 112) 73856 pool1[0][0]
____________________________________________________________________________________________________
conv2_2 (Convolution2D) (None, 128, 112, 112) 147584 conv2_1[0][0]
____________________________________________________________________________________________________
pool2 (MaxPooling2D) (None, 128, 56, 56) 0 conv2_2[0][0]
____________________________________________________________________________________________________
conv3_1 (Convolution2D) (None, 256, 56, 56) 295168 pool2[0][0]
____________________________________________________________________________________________________
conv3_2 (Convolution2D) (None, 256, 56, 56) 590080 conv3_1[0][0]
____________________________________________________________________________________________________
conv3_3 (Convolution2D) (None, 256, 56, 56) 590080 conv3_2[0][0]
____________________________________________________________________________________________________
pool3 (MaxPooling2D) (None, 256, 28, 28) 0 conv3_3[0][0]
____________________________________________________________________________________________________
conv4_1 (Convolution2D) (None, 512, 28, 28) 1180160 pool3[0][0]
____________________________________________________________________________________________________
conv4_2 (Convolution2D) (None, 512, 28, 28) 2359808 conv4_1[0][0]
____________________________________________________________________________________________________
conv4_3 (Convolution2D) (None, 512, 28, 28) 2359808 conv4_2[0][0]
____________________________________________________________________________________________________
pool4 (MaxPooling2D) (None, 512, 14, 14) 0 conv4_3[0][0]
____________________________________________________________________________________________________
conv5_1 (Convolution2D) (None, 512, 14, 14) 2359808 pool4[0][0]
____________________________________________________________________________________________________
conv5_2 (Convolution2D) (None, 512, 14, 14) 2359808 conv5_1[0][0]
____________________________________________________________________________________________________
conv5_3 (Convolution2D) (None, 512, 14, 14) 2359808 conv5_2[0][0]
____________________________________________________________________________________________________
pool5 (MaxPooling2D) (None, 512, 7, 7) 0 conv5_3[0][0]
____________________________________________________________________________________________________
flatten (Flatten) (None, 25088) 0 pool5[0][0]
____________________________________________________________________________________________________
fc6 (Dense) (None, 4096) 102764544 flatten[0][0]
____________________________________________________________________________________________________
fc7 (Dense) (None, 4096) 16781312 fc6[0][0]
____________________________________________________________________________________________________
batchnormalization_1 (BatchNorma (None, 4096) 16384 fc7[0][0]
____________________________________________________________________________________________________
fc8 (Dense) (None, 19) 77843 batchnormalization_1[0][0]
====================================================================================================
Total params: 134,354,771
Trainable params: 16,867,347
Non-trainable params: 117,487,424
____________________________________________________________________________________________________
None
Train on 1120 samples, validate on 747 samples
Epoch 1/20
1120/1120 [==============================] - 7354s - loss: 2.9517 - acc: 0.0714 - val_loss: 2.9323 - val_acc: 0.2316
Epoch 2/20
1120/1120 [==============================] - 7356s - loss: 2.8053 - acc: 0.1732 - val_loss: 2.9187 - val_acc: 0.3614
Epoch 3/20
1120/1120 [==============================] - 7358s - loss: 2.6727 - acc: 0.2643 - val_loss: 2.9034 - val_acc: 0.3882
Epoch 4/20
1120/1120 [==============================] - 7361s - loss: 2.5565 - acc: 0.3071 - val_loss: 2.8861 - val_acc: 0.4016
Epoch 5/20
1120/1120 [==============================] - 7360s - loss: 2.4597 - acc: 0.3518 - val_loss: 2.8667 - val_acc: 0.4043
Epoch 6/20
1120/1120 [==============================] - 7363s - loss: 2.3827 - acc: 0.3714 - val_loss: 2.8448 - val_acc: 0.4163
Epoch 7/20
1120/1120 [==============================] - 7364s - loss: 2.3108 - acc: 0.4045 - val_loss: 2.8196 - val_acc: 0.4244
Epoch 8/20
1120/1120 [==============================] - 7377s - loss: 2.2463 - acc: 0.4268 - val_loss: 2.7905 - val_acc: 0.4324
Epoch 9/20
1120/1120 [==============================] - 7373s - loss: 2.1824 - acc: 0.4563 - val_loss: 2.7572 - val_acc: 0.4404
Epoch 10/20
1120/1120 [==============================] - 7373s - loss: 2.1313 - acc: 0.4732 - val_loss: 2.7190 - val_acc: 0.4471
Epoch 11/20
1120/1120 [==============================] - 7440s - loss: 2.0766 - acc: 0.5036 - val_loss: 2.6754 - val_acc: 0.4565
Epoch 12/20
1120/1120 [==============================] - 7414s - loss: 2.0323 - acc: 0.5170 - val_loss: 2.6263 - val_acc: 0.4565
Epoch 13/20
1120/1120 [==============================] - 7413s - loss: 1.9840 - acc: 0.5420 - val_loss: 2.5719 - val_acc: 0.4592
Epoch 14/20
1120/1120 [==============================] - 7414s - loss: 1.9467 - acc: 0.5464 - val_loss: 2.5130 - val_acc: 0.4592
Epoch 15/20
1120/1120 [==============================] - 7412s - loss: 1.9039 - acc: 0.5652 - val_loss: 2.4513 - val_acc: 0.4592
Epoch 16/20
1120/1120 [==============================] - 7413s - loss: 1.8716 - acc: 0.5723 - val_loss: 2.3906 - val_acc: 0.4578
Epoch 17/20
1120/1120 [==============================] - 7415s - loss: 1.8214 - acc: 0.5866 - val_loss: 2.3319 - val_acc: 0.4538
Epoch 18/20
1120/1120 [==============================] - 7416s - loss: 1.7860 - acc: 0.5982 - val_loss: 2.2789 - val_acc: 0.4538
Epoch 19/20
1120/1120 [==============================] - 7430s - loss: 1.7623 - acc: 0.5973 - val_loss: 2.2322 - val_acc: 0.4538
Epoch 20/20
1120/1120 [==============================] - 7856s - loss: 1.7222 - acc: 0.6170 - val_loss: 2.1913 - val_acc: 0.4538
Accuracy: 45.38%
结果并不好，因为我无法训练它获取更多数据，因为它需要太长时间。有什么想法吗？
答案 0 :(得分：2)
请注意，您要提供~ 19 * 40 < 800示例，以便训练16,867,347个参数。所以这基本上是2e6每个例子的参数。这根本无法正常工作。尝试删除所有FCN图层（顶部为Dense图层），并将较小的Dense放置在例如每个~50个神经元。在我看来，这应该有助于提高准确性和加快培训。