g_loss consistently close to zero while training with my own data

I understand that this must be a problem with my data but need help in understanding how to fix it.
I'm completely stuck at this time.

Below a short slice of the output I get while training:
```
Epoch: [14] [ 0/ 4] time: 226.4298, d_loss: 14.95790005, g_loss: 0.00002277
Epoch: [14] [ 1/ 4] time: 230.2907, d_loss: 14.68263435, g_loss: 0.00022513
Epoch: [14] [ 2/ 4] time: 234.2050, d_loss: 9.30468655, g_loss: 0.00297309
Epoch: [14] [ 3/ 4] time: 238.1187, d_loss: 6.54414463, g_loss: 0.24060325
Epoch: [15] [ 0/ 4] time: 242.0421, d_loss: 14.79878426, g_loss: 0.00003257
Epoch: [15] [ 1/ 4] time: 245.9673, d_loss: 15.27751350, g_loss: 0.00005784
```

While d_loss, slowly and bouncing, decreases, g_loss stays consistently very close to zero (often it is 0.0000).
Moreover, at the end I get very noisy (almost only noisy) train_* images.
I wonder whether this is an issue with my input, or if the error hides elsewhere in my toolchain.

I should add that, for my purposes, the "B" channel of the RGB input images is always 0 (so, in other words, my images have only two meaningful channels, the third is blanked).

This is my command line:
`python main.py --dataset=images-256-256-full --input_height=256 --input_width=256 --output_height=256 --output_width=256 --input_fname_pattern="*.png" --is_train --is_crop --c_dim 3 --epoch=1000 --gf_dim=256 --df_dim=256`

Start by reproducing the reported command through main.py with the supplied image dataset and training settings. Inspect the input channel configuration and training output, then determine whether the behavior comes from the data or the toolchain; done means identifying a specific cause and a verified resolution.

Written by the indexing model from the issue text.