DIR_DEPTH = 3
for _ in range(DIR_DEPTH + 1):
ROOT = os.path.dirname(ROOT)
sys.path.insert(0, ROOT)
from tframe import console, SaveMode
from tframe.trainers import SmartTrainerHub
from tframe import Classifier
import mn_du as du
from_root = lambda path: os.path.join(ROOT, path)
# -----------------------------------------------------------------------------
# Initialize config and set data/job dir
# -----------------------------------------------------------------------------
th = SmartTrainerHub(as_global=True)
th.data_dir = from_root('tframe/examples/00-MNIST/data/')
th.job_dir = from_root('tframe/examples/00-MNIST')
# -----------------------------------------------------------------------------
# Device configurations
# -----------------------------------------------------------------------------
th.allow_growth = False
th.gpu_memory_fraction = 0.30
# -----------------------------------------------------------------------------
# Set information about the data set
# -----------------------------------------------------------------------------
th.input_shape = [28, 28, 1]
th.num_classes = 10
sys.path.insert(0, ROOT)
import numpy as np
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
from tframe import console, SaveMode
from tframe import Classifier
from tframe.trainers import SmartTrainerHub as Config
import fi_du as du
from_root = lambda path: os.path.join(ROOT, path)
# -----------------------------------------------------------------------------
# Initialize config and set data/job dir
# -----------------------------------------------------------------------------
th = Config(as_global=True)
th.data_dir = from_root("FILOB/data/")
th.job_dir = from_root("FILOB")
# -----------------------------------------------------------------------------
# Device configurations
# -----------------------------------------------------------------------------
th.allow_growth = False
th.gpu_memory_fraction = 0.3
# -----------------------------------------------------------------------------
# Set information about the data set
# -----------------------------------------------------------------------------
th.max_level = 10
th.volume_only = True
th.developer_code = "use_log"
for _ in range(DIR_DEPTH + 1):
ROOT = os.path.dirname(ROOT)
sys.path.insert(0, ROOT)
# Do some importing
from tframe import console, SaveMode
from tframe.trainers import SmartTrainerHub
from tframe import Classifier
import cf10_du as du
from_root = lambda path: os.path.join(ROOT, path)
# -----------------------------------------------------------------------------
# Initialize config and set data/job dir
# -----------------------------------------------------------------------------
th = SmartTrainerHub(as_global=True)
th.data_dir = from_root('tframe/examples/01-CIFAR10/data/')
th.job_dir = from_root('tframe/examples/01-CIFAR10')
# -----------------------------------------------------------------------------
# Device configurations
# -----------------------------------------------------------------------------
th.allow_growth = False
th.gpu_memory_fraction = 0.30
# -----------------------------------------------------------------------------
# Set information about the data set
# -----------------------------------------------------------------------------
th.input_shape = [32, 32, 3]
th.num_classes = 10
# -----------------------------------------------------------------------------
# Set common trainer configs
ROOT = os.path.dirname(ROOT)
sys.path.insert(0, ROOT)
from tframe.utils.misc import mark_str
from tframe.data.sequences.signals.signal import Signal
from tframe import console, SaveMode
from tframe.trainers import SmartTrainerHub
from tframe import Classifier
import data_utils as du
from gpat import init_methods
from gpat.gpat_bigdata import GPATBigData
from gpat.gpat_signal_set import GPATSignalSet
from_root = lambda path: os.path.join(ROOT, path)
th = SmartTrainerHub(as_global=True)
th.data_dir = from_root('99-GPAT/data')
from_gpat = lambda path: os.path.join(from_root('99-GPAT'), path)
# region : Paths
data_root = th.data_dir
from_data_root = lambda path: os.path.join(data_root, path)
input_dir = from_data_root('input')
output_dir = from_data_root('output')
from_input = lambda path: os.path.join(input_dir, path)
from_output = lambda path: os.path.join(output_dir, path)
label_sheet_path = from_data_root('labels.csv')
raw_train_data_path = from_input('raw_data/audio_train')
raw_train_csv_path = from_input('train.csv')
ROOT = os.path.abspath(__file__)
# Specify the directory depth with respect to the root of your project here
# (The project root usually holds your data folder and has a depth of 0)
DIR_DEPTH = 3
for _ in range(DIR_DEPTH + 1):
ROOT = os.path.dirname(ROOT)
sys.path.insert(0, ROOT)
from tframe import console, SaveMode
from tframe.trainers import SmartTrainerHub
from data_utils import load_data, evaluate
from tframe import Predictor
from_root = lambda path: os.path.join(ROOT, path)
th = SmartTrainerHub(as_global=True)
th.data_dir = from_root('tframe/examples/whbm/data/')
th.job_dir = from_root('tframe/examples/whbm')
th.allow_growth = False
th.gpu_memory_fraction = 0.4
th.save_mode = SaveMode.ON_RECORD
th.warm_up_thres = 1
th.at_most_save_once_per_round = True
th.early_stop = True
th.patience = 10
def activate():
for _ in range(DIR_DEPTH + 1):
ROOT = os.path.dirname(ROOT)
sys.path.insert(0, ROOT)
from tframe import console
from tframe.models.sl.classifier import Classifier
from tframe.enums import SaveMode
from tframe.trainers import SmartTrainerHub as Config
import pm_du as du
from_root = lambda path: os.path.join(ROOT, path)
# -----------------------------------------------------------------------------
# Initialize config and set data/job dir
# -----------------------------------------------------------------------------
th = Config(as_global=True)
th.data_dir = from_root('04-pMNIST/data')
th.job_dir = from_root('04-pMNIST')
# -----------------------------------------------------------------------------
# Some device configurations
# -----------------------------------------------------------------------------
th.allow_growth = False
th.gpu_memory_fraction = 0.3
# -----------------------------------------------------------------------------
# Set information about the data set
# -----------------------------------------------------------------------------
th.input_shape = [1]
th.output_dim = 10
th.permute = True
import sys, os
ROOT = os.path.abspath(__file__)
# Specify the directory depth with respect to the root of your project here
# (The project root usually holds your data folder and has a depth of 0)
DIR_DEPTH = 3
for _ in range(DIR_DEPTH + 1):
ROOT = os.path.dirname(ROOT)
sys.path.insert(0, ROOT)
from tframe import console, SaveMode
from tframe.trainers import SmartTrainerHub
from data_utils import load_data
from tframe import Classifier
from_root = lambda path: os.path.join(ROOT, path)
th = SmartTrainerHub(as_global=True)
th.data_dir = from_root('tframe/examples/cifar-10/data/')
th.job_dir = from_root('tframe/examples/cifar-10')
th.input_shape = [32, 32, 3]
th.num_classes = 10
th.allow_growth = False
th.gpu_memory_fraction = 0.4
th.save_mode = SaveMode.ON_RECORD
th.warm_up_thres = 1
th.at_most_save_once_per_round = False
th.early_stop = True
th.patience = 10
# (The project root usually holds your data folder and has a depth of 0)
DIR_DEPTH = 1
for _ in range(DIR_DEPTH + 1):
ROOT = os.path.dirname(ROOT)
sys.path.insert(0, ROOT)
from tframe import console, SaveMode
from tframe.models.sl.classifier import Classifier
from tframe.trainers import SmartTrainerHub
import timit_du as du
from_root = lambda path: os.path.join(ROOT, path)
# -----------------------------------------------------------------------------
# Initialize config and set data/job dir
# -----------------------------------------------------------------------------
th = SmartTrainerHub(as_global=True)
th.data_dir = from_root('05-TIMIT/data')
th.job_dir = from_root('05-TIMIT')
# -----------------------------------------------------------------------------
# Some device configurations
# -----------------------------------------------------------------------------
th.allow_growth = False
th.gpu_memory_fraction = 0.3
# -----------------------------------------------------------------------------
# Set information about the data set
# -----------------------------------------------------------------------------
th.input_shape = [13]
th.output_dim = 25
th.last_only = True