train_ratio=model_config.train_ratio,
device=device)
log_dir = os.path.join('saves', config.model)
# Initialize Logger class
logger = Logger(log_dir)
model_config.save(os.path.join(logger.log_dir, 'config.json'))
# Get the position and target of the evaluated item
eval_pos, eval_target = dataset.eval_data()
# Get the popularity item
item_popularity = dataset.item_popularity
# Initialize Evaluator class
evaluator = Evaluator(eval_pos, eval_target, item_popularity,
model_config.top_k)
# Build the model
model = build_model(config.model, model_config, dataset.num_users,
dataset.num_items, device)
# Get the model info and data info
logger.info(model_config)
logger.info(dataset)
# Initialize Trainer class
trainer = Trainer(dataset=dataset,
model=model,
evaluator=evaluator,
logger=logger,
conf=model_config)
# Train the model and get results
trainer.train(experiment)
# Dictionary to store model hyper-parameters
hyper_params = {
import tensorflow as tf
from Environment import *
from ModelBuilder import build_model
import os
tf.reset_default_graph()
layers = [729, 729, 729]
inputs, Q_out, predict = build_model(9, layers, 9)
sess = tf.Session()
env = Environment()
saver = tf.train.Saver()
init = tf.global_variables_initializer()
sess.run(init)
checkpoint = tf.train.get_checkpoint_state("model")
if checkpoint and checkpoint.model_checkpoint_path:
s = saver.restore(sess, checkpoint.model_checkpoint_path)
print("Successfully loaded the model:", checkpoint.model_checkpoint_path)
step = int(
os.path.basename(checkpoint.model_checkpoint_path).split('-')[1])
else:
print("Could not find old network weights")
player = env.O
turn = get_first_turn()
done = False
state = get_new_board()