我使用Keras建立了CNN，对2种不同类别的图像进行分类。我遇到的问题是我在训练后似乎无法得到正确的预测。
一点背景...... 数据集大78750例（大约95％Cat.1和5％Cat.2），这可能是罪魁祸首，因为我认为Cat的过度拟合正在发生。 1（我认为这是问题所在，但由于其他一些原因，更改数据集大小很难）
为了解决这个问题，我在每个卷积层上添加了正则化，但无济于事。
我的问题是......我是否绝对需要更改我的类别大小，或者我还能做些什么来对抗Cat的过度拟合。 1？
以下是CNN的代码：
model = Sequential()
model.add(Conv2D(filters=25,
kernel_size=(10, 10),
strides=(1, 1),
activation='relu',
input_shape=input_shape,
padding="VALID",
kernel_initializer=random_normal(mean=0, stddev=.1),
kernel_regularizer=l2(.001)))
model.add(MaxPooling2D(pool_size=(2, 2),
strides=(2, 2)))
model.add(Conv2D(filters=25,
kernel_size=(7, 7),
strides=(1, 1),
activation='relu',
padding="VALID",
kernel_initializer=random_normal(mean=0, stddev=.1),
kernel_regularizer=l2(.001)))
model.add(MaxPooling2D(pool_size=(2, 2),
strides=(2, 2)))
model.add(Conv2D(filters=25,
kernel_size=(5, 5),
strides=(2, 2),
activation='relu',
padding="VALID",
kernel_initializer=random_normal(mean=0, stddev=.1),
kernel_regularizer=l2(.001)))
model.add(MaxPooling2D(pool_size=(2, 2),
strides=(1, 1)))
model.add(Conv2D(filters=25,
kernel_size=(5, 5),
strides=(2, 2),
activation='relu',
padding="VALID",
kernel_initializer=random_normal(mean=0, stddev=.1),
kernel_regularizer=l2(.001)))
model.add(Flatten())
model.add(Dense(2, activation='relu', kernel_initializer=random_normal(mean=0, stddev=.1), kernel_regularizer=l2(.001)))
model.add(Dense(2, activation='softmax'))
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.sgd(lr=.001, momentum=0.9),
metrics=['accuracy'])
编辑1
以下是1个纪元的跑步训练的输出......
Epoch 1/2
500/78750 [..............................] - ETA: 664s - loss: 1.3999 - acc: 0.9460
1000/78750 [..............................] - ETA: 652s - loss: 1.3713 - acc: 0.9500
1500/78750 [..............................] - ETA: 648s - loss: 1.3897 - acc: 0.9460
2000/78750 [..............................] - ETA: 648s - loss: 1.3970 - acc: 0.9420
2500/78750 [..............................] - ETA: 646s - loss: 1.3965 - acc: 0.9376
3000/78750 [>.............................] - ETA: 640s - loss: 1.3972 - acc: 0.9373
3500/78750 [>.............................] - ETA: 636s - loss: 1.3886 - acc: 0.9377
4000/78750 [>.............................] - ETA: 628s - loss: 1.3886 - acc: 0.9403
4500/78750 [>.............................] - ETA: 625s - loss: 1.3857 - acc: 0.9400
5000/78750 [>.............................] - ETA: 619s - loss: 1.3813 - acc: 0.9416
5500/78750 [=>............................] - ETA: 612s - loss: 1.3773 - acc: 0.9436
6000/78750 [=>............................] - ETA: 608s - loss: 1.3756 - acc: 0.9447
6500/78750 [=>............................] - ETA: 606s - loss: 1.3735 - acc: 0.9454
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7500/78750 [=>............................] - ETA: 597s - loss: 1.3709 - acc: 0.9481
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8500/78750 [==>...........................] - ETA: 589s - loss: 1.3672 - acc: 0.9485
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10000/78750 [==>...........................] - ETA: 576s - loss: 1.3629 - acc: 0.9497
10500/78750 [===>..........................] - ETA: 571s - loss: 1.3625 - acc: 0.9494
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37000/78750 [=============>................] - ETA: 346s - loss: 1.3455 - acc: 0.9504
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38000/78750 [=============>................] - ETA: 337s - loss: 1.3452 - acc: 0.9506
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40500/78750 [==============>...............] - ETA: 317s - loss: 1.3447 - acc: 0.9509
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76500/78750 [============================>.] - ETA: 18s - loss: 1.3400 - acc: 0.9519
77000/78750 [============================>.] - ETA: 14s - loss: 1.3399 - acc: 0.9519
77500/78750 [============================>.] - ETA: 10s - loss: 1.3399 - acc: 0.9519
78000/78750 [============================>.] - ETA: 6s - loss: 1.3398 - acc: 0.9518
78500/78750 [============================>.] - ETA: 2s - loss: 1.3398 - acc: 0.9518
78750/78750 [==============================] - 855s - loss: 1.3397 - acc: 0.9518 - val_loss: 1.3321 - val_acc: 0.9523
这里是model.summary（）...
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 72, 72, 25) 2525
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 36, 36, 25) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 30, 30, 25) 30650
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 15, 15, 25) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 6, 6, 25) 15650
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 5, 5, 25) 0
_________________________________________________________________
conv2d_4 (Conv2D) (None, 1, 1, 25) 15650
_________________________________________________________________
flatten_1 (Flatten) (None, 25) 0
_________________________________________________________________
dense_1 (Dense) (None, 2) 52
_________________________________________________________________
dense_2 (Dense) (None, 2) 6
=================================================================
Total params: 64,533
Trainable params: 64,533
Non-trainable params: 0
答案 0 :(得分：2)
您的数据集非常不平衡，因此模型将第二类视为噪声并将所有内容归类为类别1.平衡数据集的最简单方法是对第二类的示例进行过采样，使模型更多地看到类别2经常。
这可能会解决类输出的问题，但这种模型的泛化性很差。为了提高泛化能力，您可以尝试data augmentation，应用于图像的随机转换。
答案 1 :(得分：1)
尝试将学习率降低到1e-5一个或两个时期，看看准确度是否提高。如果它不起作用，请提供model.summary（）。