If you use a projection size that doesn't align with the upsampling path, the generator output won't match the expected image size, which can cause downstream errors (such as negative variance or shape mismatches).
Each transposedConv2dLayer with Stride=2 doubles the spatial resolution. The number of upsampling layers and the initial projection size must align so that after all upsampling, you reach your desired output size.The general rule is that if your initial projection size is [h, w, c] and you have n upsampling layers (each with Stride=2), your output size will be [h*2^n, w*2^n, outputChannels]. Therefore, for
1. 256x256x3 Output -
- Start with: [4, 4, ...] projection size
- Number of upsampling layers: 4
- Calculation: 4 → 8 → 16 → 32 → 64 → 128 → 256 (for 6 layers, but typically 4 layers from 4 to 64, then up to 256)
- But: 4 upsampling layers from [4,4] gives [64,64]`\, so you need 6 layers to go from 4 to 256.
- However, your code uses 4 upsampling layers, so your projection should be [16,16, ...] for 256x256 output: 16 → 32 → 64 → 128 → 256 (4 layers, 16*2^4 = 256)
2. 224x224x3 Output -
- 224 is not a power of 2, so you need to start with a projection size that, after upsampling, results in 224.
- 224 = 14 * 2^4
- So, start with [14,14, ...] and 4 upsampling layers.
