def __init__(self, config, rng):
self.config = config
self.rng = rng
self.task = config.task
self.model_dir = config.model_dir
self.gpu_memory_fraction = config.gpu_memory_fraction
self.checkpoint_secs = config.checkpoint_secs
self.log_step = config.log_step
self.num_epoch = config.num_epochs
## import data Loader ##
data_dir = config.data_dir
dataset_name = config.task
batch_size = config.batch_size
num_time_steps = config.num_time_steps
num_node = config.num_node
self.data_loader = BatchLoader(data_dir, dataset_name, batch_size,
num_time_steps, num_node)
## Need to think about how we construct adj matrix(W)
W = self.data_loader.adj
laplacian = W / W.max()
laplacian = scipy.sparse.csr_matrix(laplacian, dtype=np.float32)
lmax = graph.lmax(laplacian)
#idx2char = batchLoader_.idx2char
#char2idx = batchLoader_.char2idx
#batch_x, batch_y = batchLoader_.next_batch(0) 0:train 1:valid 2:test
#batchLoader_.reset_batch_pointer(0)
## define model ##
self.model = Model(config, laplacian, lmax)
## model saver / summary writer ##
self.saver = tf.train.Saver()
self.model_saver = tf.train.Saver(self.model.model_vars)
self.summary_train_writer = tf.summary.FileWriter(self.model_dir +
'/train')
self.summary_test_writer = tf.summary.FileWriter(self.model_dir +
'/test')
sv = tf.train.Supervisor(logdir=self.model_dir,
is_chief=True,
saver=self.saver,
summary_op=None,
summary_writer=self.summary_train_writer,
save_summaries_secs=300,
save_model_secs=self.checkpoint_secs,
global_step=self.model.model_step)
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=self.gpu_memory_fraction,
allow_growth=True) # seems to be not working
sess_config = tf.ConfigProto(allow_soft_placement=True,
gpu_options=gpu_options)
self.sess = sv.prepare_or_wait_for_session(config=sess_config)
def __init__(self, config, rng):
self.config = config
self.rng = rng
self.task = config.task
self.model_dir = config.model_dir
self.gpu_memory_fraction = config.gpu_memory_fraction
self.checkpoint_secs = config.checkpoint_secs
self.log_step = config.log_step
self.num_epoch = config.num_epochs
## import data Loader ##
data_dir = config.data_dir
dataset_name = config.task
batch_size = config.batch_size
num_time_steps = config.num_time_steps
self.data_loader = BatchLoader(data_dir, dataset_name, batch_size,
num_time_steps)
## Need to think about how we construct adj matrix(W)
# Oh no. Are you kidding me??
W = self.data_loader.adj
laplacian = W / W.max() # 作了归一化
laplacian = scipy.sparse.csr_matrix(laplacian,
dtype=np.float32) # 将矩阵用CSR的方式存储
lmax = graph.lmax(laplacian) # Q:作用未知
## define model ##
self.model = Model(config, laplacian, lmax)
## model saver / summary writer ##
self.saver = tf.train.Saver()
self.model_saver = tf.train.Saver(self.model.model_vars)
self.summary_writer = tf.summary.FileWriter(self.model_dir)
sv = tf.train.Supervisor(logdir=self.model_dir,
is_chief=True,
saver=self.saver,
summary_op=None,
summary_writer=self.summary_writer,
save_summaries_secs=300,
save_model_secs=self.checkpoint_secs,
global_step=self.model.model_step)
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=self.gpu_memory_fraction,
allow_growth=True) # seems to be not working
sess_config = tf.ConfigProto(allow_soft_placement=True,
gpu_options=gpu_options)
self.sess = sv.prepare_or_wait_for_session(config=sess_config)