def __init__(self, k = 10):
flag = "ntm_model.new_trained.freq=100.word=22548"
conf = Config(flag, "full", 200)
print(flag)
print(k)
self.k = k
self.data_path = "".join((home, "/Data/yelp/output/raw_review_restaurant.json"))
# generate dataset
self.train_path = "train80p1.json"
self.test_path = "test80p1.json"
self.prod_vector = conf.path_doc_w2c
self.prod2idx = {}
self.idx2prod = []
self.user2idx = {}
self.idx2user = []
lr = float(args[5])
print(args)
os.environ['MKL_NUM_THREADS'] = str(n_processer)
os.environ['GOTO_NUM_THREADS'] = str(n_processer)
os.environ['OMP_NUM_THREADS'] = str(n_processer)
os.environ['THEANO_FLAGS'] = 'device=cpu,blas.ldflags=-lblas -lgfortran'
import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID" # see issue #152
os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3"
config = tf.ConfigProto(log_device_placement=False, allow_soft_placement=True)
config.gpu_options.allow_growth = True
config.gpu_options.per_process_gpu_memory_fraction = 1
conf = Config(flag, args[2], int(args[3]))
print(flag)
print(theano.config.openmp)
# get data
dp = DataProvider(conf)
model, word_embed, prod_embed, tag_embed = build_attr_model(dp)
if os.path.exists(conf.path_checkpoint):
print("load previous checker")
model.load_weights(conf.path_checkpoint)
dp.generate_init()
model.fit_generator(generator=dp.generate_data(batch_size=conf.batch_size), nb_worker=n_processer,
nb_epoch=conf.n_epoch, samples_per_epoch=int(np.ceil(conf.sample_per_epoch / conf.batch_size)),