使用Theano后端的Keras CNN：batch_size等参数设置咨询

Hey there! Let's break down these parameters step by step, tailored to your 240,000-image training set and 60,000-image test set in Keras (Theano backend).

1. batch_size #

First, let's clarify what this does: it’s the number of images the model processes at once before updating its weights. There’s no perfect value, but here’s how to pick a solid starting point:

• Standard choices: 32, 64, or 128 are go-to values for most image tasks. The right size depends entirely on your GPU/CPU memory—if you hit out-of-memory errors, drop to a smaller number; if you’ve got plenty of headroom, you can try larger batches (they might speed up training slightly).
• For your dataset: Start with batch_size=64—it’s a balanced pick that works for most setups. If your hardware can handle it, bump it to 128; if not, scale down to 32.

2. steps_per_epoch #

This tells Keras how many batch iterations to run per training epoch. The formula is straightforward:
steps_per_epoch = total_training_samples // batch_size

• For your 240,000 training images:
  • If batch_size=64: 240000 // 64 = 3750
  • If batch_size=128: 240000 // 128 = 1875
  • If batch_size=32: 240000 // 32 = 7500
• Quick note: Using integer division (//) skips any partial batch at the end of the epoch. You could add 1 to include every single sample, but it’s rarely necessary—epochs are about exposing the model to your data repeatedly, not hitting every image perfectly each pass.

3. validation_steps #

This mirrors steps_per_epoch, but for your test/validation set. Calculate it with:
validation_steps = total_validation_samples // batch_size

• For your 60,000 test images:
  • If batch_size=64: 60000 // 64 = 937 (this leaves 32 unused samples; if you want to include them, set it to 938)
  • If batch_size=128: 60000 // 128 = 468 (leaves 96 samples unused)
  • If batch_size=32: 60000 // 32 = 1875 (exact division here—no samples left out!)

Pro Tips to Keep in Mind #

• Memory monitoring: Always keep an eye on your GPU memory usage when testing batch sizes. If you see errors like ResourceExhaustedError, immediately reduce the batch size.
• Adjust as you go: You can tweak these values mid-training. For example, if training feels too slow, try a larger batch size (if your hardware allows) to speed things up.
• Generator alignment: If you’re using ImageDataGenerator for loading images, make sure the generator’s batch_size matches the one you set for the model—this avoids mismatches in data flow.

内容的提问来源于stack exchange，提问作者Ermene