# 'LR': 0.0001, # learning rate

# 'D2G_LR': 0.1, # discriminator/generator learning rate ratio

# 'BETA1': 0.0, # adam optimizer beta1

# 'BETA2': 0.9, # adam optimizer beta2

# 'L1_LOSS_WEIGHT': 1, # l1 loss weight

# 'FM_LOSS_WEIGHT': 10, # feature-matching loss weight

config.MAX_STEPS = 1500

config.EVAL_INTERVAL = 80

config.MAX_EPOCHES = 1

# config.MAX_STEPS = 3

experiments = 50

for i in range(experiments):

# sample from a Uniform distribution on a log-scale

# config.LR = 10 ** np.random.uniform(-3, -5) # Sample learning rate candidates in the range (0.001 to 0.00001)

# config.D2G_LR = 10 ** np.random.uniform(-2,

# 0) # Sample regularization candidates in the range (0.01 to 0.0001)

# config.LR = 0.0001

# config.D2G_LR =0.1

# # config.LR=0.0001

# config.PATH = './checkpoints/places2_tune_%d_%f%f_' % (i, config.LR, config.D2G_LR)

# logdir= config.PATH+('/log_%s_%s' % (config.LR , config.D2G_LR))

create_dir(config.PATH)

if TRAIN_LOSS:

# if config.MODEL == 1:

# config.L1_LOSS_WEIGHT = 10 ** np.random.uniform(-1,

# 1) # Sample regularization candidates in the range (1 to 200)

# config.FM_LOSS_WEIGHT = 10 ** np.random.uniform(-1,

# 1.5) # Sample regularization candidates in the range (1 to 200)

# config.ADV_LOSS_WEIGHT = 10 ** np.random.uniform(-1,

# 1) # Sample regularization candidates in the range (1 to 200)

if config.MODEL != 1:

# config.L1_LOSS_WEIGHT = 10 ** np.random.uniform(-1,

# 1) # Sample regularization candidates in the range (1 to 200)

# config.FM_LOSS_WEIGHT = 10 ** np.random.uniform(-1,

# 1.5) # Sample regularization candidates in the range (1 to 200)

config.STYLE_LOSS_WEIGHT =np.random.uniform(10,400) # Sample regularization candidates in the range (1 to 200)

# config.CONTENT_LOSS_WEIGHT = 2 * 10 ** np.random.uniform(0,

# 2) # Sample regularization candidates in the range (1 to 200)

# config.INPAINT_ADV_LOSS_WEIGHT = 10 ** np.random.uniform(-1,

# 1) # Sample regularization candidates in the range (1 to 200)

model = EdgeConnect(config)

model.load()

# config.print()

print('\nEx %d: learning_rate:%f D_Learning_rate: %f:' % (i, config.LR, config.D2G_LR))

if TRAIN_LOSS:

if config.MODEL == 1:

print('Ex %d: L1:%f FM: %f ADV: %f:' % (

i, config.L1_LOSS_WEIGHT, config.FM_LOSS_WEIGHT, config.ADV_LOSS_WEIGHT))

if config.MODEL != 1:

print('Ex %d: L1:%f FM: %f STYLE: %f CONTENT: %f ADV: %f:' % (i, config.L1_LOSS_WEIGHT,

config.FM_LOSS_WEIGHT,

config.STYLE_LOSS_WEIGHT,

config.CONTENT_LOSS_WEIGHT,

config.INPAINT_ADV_LOSS_WEIGHT))

model.train()