Keras CNN立即过度拟合，而不是数据集问题

Question

一直试图构建一个CNN来对MFCC数据进行分类，但该模型立即过拟合。

数据：

• 18000个文件(80%训练，20%测试(
• 5个标签

数据中的5个类的数量都相等。创建这个模型是为了处理比18k多得多的文件，所以我被告知要尽我所能减少网络，这可能会有所帮助。

将滤波器从(3,3(减少到(1,1(，尝试减少隐藏神经元的数量，甚至减少层的数量。我只是被卡住了，有人有什么想法吗？

无论发生什么，当用测试数据测量准确度时，我的准确度永远不会超过60-65%。

型号代码：

time_start_train = time.time()
i = Input(shape=(feature_count,feature_count,1))
m = Conv2D(16, d, activation='elu', padding='same')(i)
m = MaxPooling2D()(m)
m = Conv2D(32, d, activation='elu', padding='same')(m)
m = MaxPooling2D()(m)
m = Conv2D(64, d, activation='elu', padding='same')(m)
m = MaxPooling2D()(m)
m = Conv2D(128, d, activation='elu', padding='same')(m)
m = MaxPooling2D()(m)
m = Conv2D(256, d, activation='elu', padding='same')(m)
m = MaxPooling2D()(m)
m = Flatten()(m)
m = Dense(512, activation='elu')(m)
m = Dropout(0.2)(m)
o = Dense(out_dim, activation='softmax')(m)
model = Model(inputs=i, outputs=o)
model.compile(loss='categorical_crossentropy', optimizer=Nadam(lr=1e-3), metrics=['accuracy'])
history = model.fit(data_train[0], data_train[1], epochs=10, verbose=1, validation_split = 0.1, shuffle=True)

型号摘要：

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         (None, 192, 192, 1)       0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 192, 192, 16)      32        
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 96, 96, 16)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 96, 96, 32)        544       
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 48, 48, 32)        0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 48, 48, 64)        2112      
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 24, 24, 64)        0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 24, 24, 128)       8320      
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 12, 12, 128)       0         
_________________________________________________________________
conv2d_5 (Conv2D)            (None, 12, 12, 256)       33024     
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 6, 6, 256)         0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 9216)              0         
_________________________________________________________________
dense_1 (Dense)              (None, 512)               4719104   
_________________________________________________________________
dropout_1 (Dropout)          (None, 512)               0         
_________________________________________________________________
dense_2 (Dense)              (None, 5)                 2565      
=================================================================
Total params: 4,765,701
Trainable params: 4,765,701
Non-trainable params: 0

MFCC示例(192x192(

模型精度

模型损失

Answer 1

尝试应用L1/L2正则化。

Answer 2

如果您对ML/DL模型没有深入的了解，请使用AUTOML而不是KERAS。在AUTOML中，不需要太多考虑不同的参数。