一直在尝试构建一个 CNN 来对 MFCC 数据进行分类，但该模型立即过拟合。
数据：
- 18 000 个文件（80% 训练，20% 测试）
- 5 个标签
数据中的 5 个类别都是等量的。这个模型的创建是为了处理比 18k 更多的文件，所以我被告知要尽我所能减少网络，这可能会有所帮助。
将过滤器从 (3,3) 减少到 (1,1)，尝试减少隐藏神经元数量甚至减少层数量。我只是卡住了，有人有什么想法吗？
无论发生什么，在使用测试数据测量准确度时，我的准确度都不会高于 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)
型号总结：
_________________________________________________________________
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
