from matplotlib import pyplot as plt
from matplotlib.patches import Ellipse
import torch
from torch.autograd import Variable
import torch.optim as optim
from Utils.common import set_random_seed
# -------------------------------------------------------------------------------------------
# Create data
# -------------------------------------------------------------------------------------------
# Random seed:
seed = 2
if not seed == 0:
set_random_seed(seed)
# -------------------------------------------------------------------------------------------
# Define scenario
# -------------------------------------------------------------------------------------------
n_dim = 2
data_type = 1 # 0 \ 1
if data_type == 0:
n_tasks = 2
# number of samples in each task:
n_samples_list = [10, 200]
# True means vector for each task [n_dim x n_tasks]:
true_mu = [[-1.0, -1.0], [+1.0, +1.0]]
parser.add_argument('--batch-size', type=int, help='input batch size for training',
default=128)
parser.add_argument('--num-epochs', type=int, help='number of epochs to train',
default=200) # 200
parser.add_argument('--lr', type=float, help='initial learning rate',
default=1e-3)
parser.add_argument('--test-batch-size',type=int, help='input batch size for testing',
default=1000)
prm = parser.parse_args()
prm.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
prm.data_path = get_data_path()
set_random_seed(prm.seed)
if prm.Experiment_Name == 'Permute_Labels':
prm.run_name = 'TwoTaskTransfer_permuted_labels'
prm.data_transform = 'Permute_Labels'
prm.model_name = 'ConvNet3'
freeze_description = 'freeze lower layers'
not_freeze_list = ['fc_out']
freeze_list = None
elif prm.Experiment_Name == 'Shuffled_Pixels':
n_pixels_shuffles = 200
prm.run_name = 'TwoTaskTransfer_shuffled_pixels' + str(n_pixels_shuffles) + '_v2'
prm.data_transform = 'Shuffled_Pixels'
prm.n_pixels_shuffles = n_pixels_shuffles
