CNN训练精度停滞求助：训练与验证精度6-7轮后不再变化

Hey there, let's break down why your CNN's training and validation accuracy are plateauing after just 6-7 epochs, and walk through actionable fixes:

1. Your Model Structure Is Too Shallow #

A single Conv2D layer followed directly by Dense is way too simple for extracting meaningful image features—even with black-and-white images. That structure can only capture very basic patterns (like edges), not the complex features needed to distinguish your 3 classes.

• Fixes:
  • Stack multiple convolutional layers with pooling: Try a structure like Input Image -> Conv2D -> Relu -> MaxPooling2D -> Conv2D -> Relu -> MaxPooling2D -> Flatten -> Dense -> Dropout -> Dense -> Softmax
  • Don’t forget the Flatten() layer! Convolutional layers output 2D feature maps, which can’t feed directly into Dense layers—this step is critical to convert those maps into a 1D vector for the fully connected layers. I suspect you might have missed this, which could be a hidden issue.

2. Your Dataset Is Too Small #

180 total images (60 per class) is a tiny dataset. While you initially thought overfitting, the fact that both training and validation accuracy stall suggests the model doesn’t have enough data to learn generalizable features, not just memorize samples.

• Fixes:
  • Add data augmentation: For black-and-white images, use techniques like random horizontal/vertical flips, small rotations (±15°), random zoom, or mild Gaussian noise to generate new training samples on the fly. Tools like Keras’ ImageDataGenerator make this easy.
  • Try transfer learning: Use a pre-trained model (e.g., VGG16 adapted for grayscale input) by freezing the bottom layers (which already learn universal image features) and only training the top layers tailored to your 3 classes. This leverages pre-learned features to get better results with small datasets.

3. Training Hyperparameters Are Off #

• Learning rate issues: If your learning rate is too high, the model bounces around the optimal solution; if it’s too low, it gets stuck in a local minimum early. Try using the Adam optimizer (it has adaptive learning rate) instead of SGD, or add learning rate decay over epochs.
• Batch size: A batch size that’s too large (e.g., 60) means the model updates weights too infrequently; too small (e.g., 8) introduces noisy gradients. Aim for 16 or 32 as a starting point.
• Loss function matching: Double-check that your loss function aligns with your label format. Use SparseCategoricalCrossentropy if your labels are integers, or CategoricalCrossentropy if they’re one-hot encoded.

4. Inadequate Feature Extraction #

If your single Conv2D layer only uses a small number of filters (e.g., 32), it can’t capture enough distinct features. Increase the number of filters as you go deeper—start with 64 in the first Conv2D layer, then 128 in the second, to build more complex feature representations.

5. Check for Overfitting (Even If It Doesn’t Look Like It) #

While your accuracy plateau doesn’t fit the classic overfitting pattern (training accuracy high, validation low), adding a Dropout layer between Dense layers can help the model learn more robust features and prevent it from memorizing minor details in the small dataset.

内容的提问来源于stack exchange，提问作者Bao Tran