paramter_combinations.py

from itertools import product
from tqdm.notebook import tqdm


def get_learner(emb_szs=emb_szs, layers=[1000,500], ps=[0.02,0.04], emb_drop=0.08):
	return (tabular_learner(data,
	 layers=layers,
	 ps=ps,
	 emb_drop=emb_drop,
	 y_range=y_range,
	 emb_szs=emb_szs,
	 metrics=exp_rmspe))


lrs = []
losses = []
wds = []
ps = []
layers = []
iter_count = 600 # anything over 300 seems to work well.
curr_wd = 1e-3
layers = [1000,500]
ps = [0.002,0.02]
emb_drop = 0.04
params = {
 'wd':[i for i in np.linspace(0,0.6,7)]
}


parameter_combinations = []

for i in tqdm(list(product(*params.values()))):
	
	curr_wd = i[0]
	print("curr_wd = {}".format(i[0])
				
	learner = get_learner(emb_szs=emb_szs, layers = layers, ps = ps, emb_drop = emb_drop)
				
	learner.lr_find(wd=curr_wd, num_it=iter_count)
				
	lrs.append(learner.recorder.lrs)
	losses.append(learner.recorder.losses)
				
	combination = [[curr_wd]]
	parameter_combinations += combination