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loader.py
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loader.py
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import tflearn
import glob
import os
import sys
from models import *
import global_vars_go as gvg
import re
def load_model(model_name):
try:
model = eval(model_name)
if hasattr(model, "get_network"):
return tflearn.DNN(model.get_network(), tensorboard_verbose=2, checkpoint_path="checkpoints/{}.tflearn".format(model_name))
else:
print("ERROR! model, {}, is not defined or does not contain a \"get_network()\" function".format(model_name))
except NameError:
print("ERROR! {} is not a valid module".format(model_name))
def load_model_from_file(model_name):
f = None
for filename in glob.glob(os.path.join(gvg.checkpoint_path, "*.index")):
try: #Substring for Windows
base_filename = filename[filename.index('\\')+1:filename.index('\\')+len(model_name)+1]
except ValueError: #Substring for Linux
base_filename = filename[filename.index('/')+1:filename.index('/')+len(model_name)+1]
if base_filename == model_name and get_int(filename) > get_int(f):
f = filename[:-6]
f = f.replace('\\', '/')
if f == None:
print("ERROR! There were no saved {} models".format(model_name))
else:
model = load_model(model_name)
model.load(f)
return model
def get_int(line):
if line is None:
return 0
endl = ""
for c in line:
if c.isnumeric():
endl += c
return int(endl)