import sys
sys.path.insert(
0, '/Users/utxeee/Desktop/deep-learning-com-perfis/code/pyTorch/utils')
import torch
from torch.utils.data.dataset import Dataset
from torch.utils.data import DataLoader
import data_preprocessor as dp
import data_splitting as ds
import utils
# -----------------------------------------------------------------------------
# Force custom modules reloading otherwise changes in custom modules after
# loading will not be taken into account herein!
# -----------------------------------------------------------------------------
utils.reload_modules([dp, ds, utils])
# -----------------------------------------------------------------------------
# Class: TrainDataset
#
# Description: Dataloader for train dataset
# -----------------------------------------------------------------------------
class TrainDataset(Dataset):
def __init__(self):
#ratings_matrix = dp.build_preprocessed_ratings_matrix()
train_matrix = ds.get_train_matrix()
self.len = train_matrix.shape[0]
self.train_matrix = torch.from_numpy(train_matrix)
# By default the train matrix tensor is a double after converting it
# from numpy but the model is expecting a float, hence, we cast the
# type here!
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
# #
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
# #
#
# <pep8 compliant>
from utils import Settings, get_sources_path, reload_modules, print_title, isolated_print
settings = Settings(get_sources_path())
# reload modules if the option is enabled in the optimization_tools.ini file
reload_modules(settings)
import os
from pathlib import Path
from constants import *
from utils import check_configuration, ScriptError, build_package, pr_bg_green, pr_bg_red
from msfs_project import MsfsProject
def merge_sceneries(script_settings):
try:
# instantiate the msfsProject and create the necessary resources if it does not exist
msfs_project = MsfsProject(script_settings.projects_path,
script_settings.project_name,
script_settings.definition_file,
import sys
sys.path.insert(0, 'auto-rec')
sys.path.insert(0, 'utils')
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn as nn
import torch.optim as optim # Optimization package
import auto_rec_model as arm
import utils
#def init():
utils.reload_modules([utils, arm])
INPUT_SIZE = 5
HIDDEN_SIZE = 3
autorec = arm.AutoRecModel(INPUT_SIZE, HIDDEN_SIZE)
#utils.print_parameters(autorec)
input = torch.tensor([1.,-1.,3,-1,5], requires_grad=False)
loss_function = nn.MSELoss()
optimizer = optim.SGD(autorec.parameters(), lr=1e-1)
def add_grad_hooks(encoder_hook, decoder_hook):
for name, param in autorec.named_parameters():
if name == "encoder.weight":
encoder_handle = param.register_hook(encoder_hook)
if name == "decoder.weight":
import sys
sys.path.insert(
0, '/Users/utxeee/Desktop/deep-learning-com-perfis/code/pyTorch/utils')
import numpy as np
import raw_data_loader as rdl
#import data_splitting as ds
import utils
from sklearn import preprocessing
from sklearn.preprocessing import StandardScaler
# -----------------------------------------------------------------------------
# Force custom modules reloading otherwise changes in custom modules after
# loading will not be taken into account herein!
# -----------------------------------------------------------------------------
utils.reload_modules([rdl, utils])
# -----------------------------------------------------------------------------
# Scale rating to range [lower_bound, upper_bound] (via third-party libraries)
#
# Remark: Tipically, we scale ratings to [1.10] range because we want to get rid
# of 0 stars ratings as that rating will be allocated to missing ratings
# and besides that a range between [1,10] is more meaningful than [0,100]
# -----------------------------------------------------------------------------
def scale_ratings(lower_bound, upper_bound, ratings_matrix):
min_max_scaler = preprocessing.MinMaxScaler(feature_range=(lower_bound,
upper_bound))
reshaped_ratings_matrix = np.array(ratings_matrix).reshape(-1, 1)
#return zip(reshaped_ratings, min_max_scaler.fit_transform(reshaped_ratings))
return min_max_scaler.fit_transform(reshaped_ratings_matrix).reshape(
ratings_matrix.shape)
sys.path.insert(0, '/Users/utxeee/Desktop/deep-learning-com-perfis/code/pyTorch/data-manipulation') import torch import torch.nn as nn import torch.optim as optim import torch.nn.functional as F import auto_rec_model as arm import auto_rec_loss_function as arl import data_loader as dl import utils import math # ----------------------------------------------------------------------------- # Force custom modules reloading otherwise changes in custom modules after # loading will not be taken into account herein! # ----------------------------------------------------------------------------- utils.reload_modules([arm,dl,arl,utils]) # ----------------------------------------------------------------------------- # Model initialization # ----------------------------------------------------------------------------- D_in = D_out = 71 # number of activities H = 20 NUM_EPOCHS = 1 LEARNING_RATE = 1e-2 MOMENTUM = 0.9 WEIGHT_DECAY = 0 #0.5 # ----------------------------------------------------------------------------- # Model initialization # -----------------------------------------------------------------------------