TensorFlow卷积性能异常：慢于Scipy与嵌套循环问题咨询

I recently ran a test comparing three different implementations of correlation (convolution) operations on a small tensor setup:

• Input tensor shape: 11x11x4
• 2 convolution kernels, each with shape 5x5x4

The three methods I tested were:

• scipy.signal.correlate
• tensorflow.nn.conv2d
• A manual nested loop implementation using 3D NumPy arrays with element-wise multiplication

After timing each approach, I found a surprising performance hierarchy:

time(scipy) < time(nested for loop) << time(tensorflow)

TensorFlow's performance was drastically worse than both the optimized SciPy function and even the handwritten loop code—this result was totally unexpected.

All the test code and timing visualization charts can be found in the associated Jupyter Notebook; just scroll to the Timing Analysis section to see the full details.

内容的提问来源于stack exchange，提问作者Ruthvik Vaila