Generated samples are not as good as samples created while training

I have a dataset of around 110k images, all cropped to 200x200 resolution. They are a collection of instagram photos, very varied, with a large amount of 'randomness' in the dataset.

I'm really happy with the samples being generated during training, especially from around the 6th epoch onwards. However, generated samples don't look right, they look like a faded, smoothed out version of the samples generated around about the 2nd epoch during training.

I trained using this command to train:

`python main.py --dataset=instagram --is_train --input_height=200 --input_width=200 --output_height=200 --output_width=200`

and am generating samples with this command:

`python main.py --dataset=instagram --input_height=200 --input_width=200 --output_height=200 --output_width=200`

I have tried all of the visualisation options.

I'm not entirely sure how to interpret the loss function but I have attached some logs from the training output.

[TrainingLogs.txt](https://github.com/carpedm20/DCGAN-tensorflow/files/906123/TrainingLogs.txt)

My problem might be related to https://github.com/carpedm20/DCGAN-tensorflow/issues/139