def __init__(self, sess, args, datasets):
self.sess = sess
self.isbatch_normal = args.isBatch_normal
self.isNormal = args.isNormal
self.checkpoint_dir = args.checkpoint_dir
self.result_dir = args.result_dir
self.log_dir = args.log_dir
self.dataset_name = args.dataset_name
self.run_type = args.run_type
self.lr = args.lr
self.epoch = args.epoch
self.batch_size = args.batch_size
self.n_inputs = args.n_inputs # MNIST data input (img shape: 28*28)
self.n_steps = args.n_steps # time steps
self.missing_rate = args.missing_rate
self.n_hidden_units = args.n_hidden_units # neurons in hidden layer
self.n_classes = args.n_classes # MNIST classes (0-9 digits)
self.run_type = args.run_type
self.result_path = args.result_path
self.model_path = args.model_path
self.pretrain_epoch = args.pretrain_epoch
self.isSlicing = args.isSlicing
self.g_loss_lambda = args.g_loss_lambda
self.model_name += "_" + str(args.missing_rate)
self.datasets = datasets
self.z_dim = args.z_dim # dimension of noise-vector
self.use_grui = args.use_grui
print(self.n_inputs)
# WGAN_GP parameter
self.lambd = 0.25 # The higher value, the more stable, but the slower convergence
self.disc_iters = args.disc_iters # The number of critic iterations for one-step of generator
# train
self.learning_rate = args.lr
self.beta1 = args.beta1
if "1.5" in tf.__version__ or "1.7" in tf.__version__:
self.grui_cell_g1 = mygru_cell.MyGRUCell15(self.n_hidden_units)
self.grui_cell_g2 = mygru_cell.MyGRUCell15(self.n_hidden_units)
self.grui_cell_d = mygru_cell.MyGRUCell15(self.n_hidden_units)
elif "1.4" in tf.__version__:
self.grui_cell_g1 = mygru_cell.MyGRUCell4(self.n_hidden_units)
self.grui_cell_g2 = mygru_cell.MyGRUCell4(self.n_hidden_units)
self.grui_cell_d = mygru_cell.MyGRUCell14(self.n_hidden_units)
elif "1.2" in tf.__version__:
self.grui_cell_d = mygru_cell.MyGRUCell2(self.n_hidden_units)
self.grui_cell_g1 = mygru_cell.MyGRUCell2(self.n_hidden_units)
self.grui_cell_g2 = mygru_cell.MyGRUCell12(self.n_hidden_units)
# test
self.sample_num = 64 # number of generated images to be saved
self.num_batches = len(
datasets.aq_train_data) // (self.batch_size * 48)
def __init__(self, sess, args, datasets):
self.sess = sess
self.isbatch_normal = args.isBatch_normal
self.checkpoint_dir = args.checkpoint_dir
self.log_dir = args.log_dir
self.lr = args.lr
self.epoch = args.epoch
self.batch_size = args.batch_size
self.n_inputs = args.n_inputs # MNIST data input (img shape: 28*28)
self.n_steps = args.n_steps
self.n_hidden_units = args.n_hidden_units # neurons in hidden layer
self.pretrain_epoch = args.pretrain_epoch
self.impute_iter = args.impute_iter
self.g_loss_lambda = args.g_loss_lambda
self.missing_rate = args.missing_rate
self.model_name += str(self.missing_rate)
self.datasets = datasets
self.z_dim = args.z_dim # dimension of noise-vector
# WGAN_GP parameter
self.disc_iters = args.disc_iters # The number of critic iterations for one-step of generator
# train
self.learning_rate = args.lr
self.beta1 = args.beta1
if "1.5" in tf.__version__ or "1.7" in tf.__version__:
self.grud_cell_d = mygru_cell.MyGRUCell15(self.n_hidden_units)
self.grud_cell_g = mygru_cell.MyGRUCell15(self.n_hidden_units)
elif "1.4" in tf.__version__:
self.grud_cell_d = mygru_cell.MyGRUCell4(self.n_hidden_units)
self.grud_cell_g = mygru_cell.MyGRUCell4(self.n_hidden_units)
elif "1.2" in tf.__version__:
self.grud_cell_d = mygru_cell.MyGRUCell2(self.n_hidden_units)
self.grud_cell_g = mygru_cell.MyGRUCell2(self.n_hidden_units)
# test
self.sample_num = 64 # number of generated images to be saved
self.num_batches = len(datasets.m) // self.batch_size