drops = np.array(range(len(data_set.column_valid)))[~data_set.column_valid]
net.drop_inputs(drops)
net1.drop_inputs(drops)
n_batch = 16
n_epochs = 10
n_al_epochs = 10
only_real = True
y_columns = list(range(data_set.y.shape[1]))
y_columns = [2, 11, 23]
net.build_multi_reg_k(len(y_columns),
activation=tf.keras.layers.LeakyReLU())
net1.build_multi_reg_k(len(y_columns),
activation=tf.keras.layers.LeakyReLU())
data_set.split_dataset_random(train_perc=0.0,
val_perc=0.1,
notused_perc=0.9)
data_set.make_dataset_tensors(y_columns=y_columns, only_real=only_real)
val_sample_size = data_set.val_labels.shape[0]
validation_steps = val_sample_size // n_batch
val_gen = data_set.dataset_generator_multi(data_set.val_datas,
data_set.val_labels, n_batch)
for al in range(n_al_epochs):
al_res = dataset_predict(net.pre_model, data_set, role=0)[:, 1]
al_res = al_res.reshape((-1, len(y_columns)))
al_res1 = dataset_predict(net1.pre_model, data_set, role=0)[:, 1]
al_res1 = al_res1.reshape((-1, len(y_columns)))
delta = np.abs(al_res - al_res1).mean(axis=1)
al_as = delta.argsort()[::-1]
new_train_num = min(np.sum(delta > 0.02), 200)
y_columns = list(range(data_set.y.shape[1]))
if args.res_type == 'cct':
targets = ['东北.青北一线', '东北.燕董一线', '东北.丰徐二线']
y_columns = data_set.get_y_indices(targets)
elif args.res_type == 'sst':
y_columns = [0]
column_names = data_set.y.columns[y_columns]
print("targets:", column_names)
net.build_multi_reg(len(y_columns), activation=tf.keras.layers.LeakyReLU())
n_batch = 16
n_epochs = 10
only_real = True
if args.task_type == 'train':
if args.dataset_split == 'random':
ids = data_set.split_dataset_random(ratios=[0.9, 0.05, 0.05])
else:
dt_train_begin = datetime.datetime(2018, 8, 1)
dt_train_end = datetime.datetime(2018, 8, 26) - datetime.timedelta(seconds=1)
dt_test_begin = datetime.datetime(2018, 8, 26)
dt_test_end = datetime.datetime(2018, 9, 1) - datetime.timedelta(seconds=1)
ids = data_set.split_dataset_dt(dt_train_begin=dt_train_begin,
dt_train_end=dt_train_end,
dt_test_begin=dt_test_begin,
dt_test_end=dt_test_end,
val_perc=0.1)
assert (0 in ids) and (1 in ids)
data_set.make_dataset_tensors(y_columns=y_columns, only_real=only_real)
train_data, train_labels, _ = data_set.get_dataset(0)
train_sample_size = train_labels.shape[0]
steps_per_epoch = train_sample_size // n_batch