<jupyter_start><jupyter_text>Set parameters  All the parameters can be found in `config.py`<jupyter_code>cfg.beta1 = 0.
cfg.beta2 = 0.99
cfg.batch_size = 16
cfg.save_period = 1000
cfg.display_period = 100
cfg.n_iters = 20000
cfg.n_critic = 1
cfg.learning_rate = 0.001
cfg.norm_g = 'pixel_norm'
cfg.norm_d = None
cfg.weight_scale = True
cfg.drift_loss = True
cfg.loss_mode = 'wgan_gp'
cfg.use_tanh = True
cfg.save_images = True<jupyter_output><empty_output><jupyter_text>Step-1 : Train at 8x8 resolutionIn the paper, they start with 4x4 resolution but it doesn't matter<jupyter_code>cfg.resolution = 8
cfg.transition = False
cfg.load_model = None

model = DCGAN(cfg)
model.train()<jupyter_output><empty_output><jupyter_text>Step-2 : Transition from 8x8 to 16x16 resolution (fading)Note that this step will automatically load model from 8x8 resolution (`models/8x8`). So, you can leave `cfg.load_model = None`.<jupyter_code>cfg.resolution = 16
cfg.transition = True
cfg.load_model = None

model = DCGAN(cfg)
model.train()<jupyter_output><empty_output><jupyter_text>Step-3 : Train at  16x16 resolutionNow, you must load model from previous step to continue. Model from previous step is saved in `models/16x16_transition`, so we set `cfg.load_model = '16x16_transition`.<jupyter_code>cfg.resolution = 16
cfg.transition = False
cfg.load_model = '16x16_transition'

model = DCGAN(cfg)
model.train()<jupyter_output><empty_output><jupyter_text>Step-4 : Transition from 16x16 to 32x32 resolution (fading)Again, this step will automatically load model from `models/16x16`.<jupyter_code>cfg.resolution = 32
cfg.transition = True
cfg.load_model = None

model = DCGAN(cfg)
model.train()<jupyter_output><empty_output><jupyter_text>Step-5 : Train at  32x32 resolutionNow, we load model from previous step saved in `models/32x32_transition`<jupyter_code>cfg.resolution = 32
cfg.transition = False
cfg.load_model = '32x32_transition'

model = DCGAN(cfg)
model.train()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10252525252525252, 'fraction_numerical': 0.04696969696969697, 'mean_word_length': 6.1516245487364625, 'pattern_counts': {'":': 0, '<': 25, '<?xml version=': 0, '>': 25, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '6', 'dir': 'jupyter-structured-clean-dedup', 'id': '576285', 'license': 'MIT', 'n_tokens_mistral': 771, 'n_tokens_neox': 697, 'n_words': 172, 'path': 'training_walkthrough.ipynb', 'repo_name': 'preritj/progressive_growing_GANs', 'size': '1980'}