def main():
parser = argparse.ArgumentParser()
parser.add_argument("--num_epochs", default=40, type=int)
parser.add_argument("--top_words", default=35000, type=int)
parser.add_argument("--max_sequence_length", default=500, type=int)
parser.add_argument("--batch_size", default=256, type=int)
parser.add_argument("--polyaxon_env", default=0, type=int)
arguments = parser.parse_args().__dict__
num_epochs = arguments.pop("num_epochs")
top_words = arguments.pop("top_words")
max_sequence_length = arguments.pop("max_sequence_length")
batch_size = arguments.pop("batch_size")
polyaxon_env = arguments.pop("polyaxon_env")
if polyaxon_env:
experiment = Experiment()
data_path = get_data_paths()["data-local"]
else:
data_path = "/data"
np.random.seed(7)
bbc_data_dir = data_path + "/bbc-topic-classification/bbc_data/"
glove_embedding_dir = (data_path +
"/bbc-topic-classification/glove.6B.300d.txt")
data = load_dataset(bbc_data_dir)
glove_embeddings = load_glove_embeddings(glove_embedding_dir)
preprocessing_pipeline = create_preprocessing_pipeline(
top_words, max_sequence_length)
train, test = train_test_split(data, test_size=0.25)
X_train = preprocessing_pipeline.fit_transform(train.text)
y_train = train["class"].values
embedding_matrix = create_embedding_matrix(glove_embeddings,
preprocessing_pipeline)
model = create_model(embedding_matrix)
model.fit(X_train,
y_train,
epochs=num_epochs,
batch_size=batch_size,
shuffle=True)
model.save("model.h5")
joblib.dump(preprocessing_pipeline, "preprocessing_pipeline.pkl")
X_test = preprocessing_pipeline.transform(test.text)
y_test = test["class"].values
metrics = model.evaluate(X_test, y_test)
if polyaxon_env:
experiment.outputs_store.upload_file("model.h5")
experiment.outputs_store.upload_file("preprocessing_pipeline.pkl")
experiment.log_metrics(loss=metrics[0], accuracy=metrics[1])
else:
print("loss: {}, accuracy: {}".format(metrics[0], metrics[1]))
def main(argv=sys.argv[1:]):
# Polyaxon experiment
experiment = Experiment()
argv.extend(['-f', get_outputs_path()])
cartpole_client.main(argv)
experiment.log_metrics(score=cartpole_client.RESULTS[0]['score'])
def train_polyaxon(args):
# Start polyaxon experiment
experiment = Experiment()
# Start training
cv_roc_auc, test_roc_auc, test_logloss = train(args)
# Save artifacts
experiment.outputs_store.upload_file(os.path.join(ARGS.model_dir, "model.pkl"))
experiment.log_metrics(
test_roc_auc=test_roc_auc, test_logloss=test_logloss, cv_roc_auc=cv_roc_auc
)
class Params:
"""
Description
----
This enables the code to use the polyaxon
"""
# This is to load the params from a file
input_thread = threading.Thread(target=get_file_inputs, args=(), daemon=True)
input_thread.start()
print("Fetching inputs", end=" ... -> ")
time.sleep(10)
print("done.")
temporal_context = 0
last_interval = None
# polyaxon params
experiment = Experiment()
plx = pp.get_parameters()
param_utils.set_params(plx)
param_utils.check_params(plx)
# output paths
file_path_mdl = define_prepare_mdl_path(plx)
logdir_tb = define_prepare_tb_path()
def run_experiment(data_path, glove_path):
# try:
log_level = get_log_level()
if not log_level:
log_level = logging.INFO
logger.info("Starting experiment")
experiment = Experiment()
logging.basicConfig(level=log_level)
logging.info("Loading data")
dpl = Datapipeline(data_path=data_path)
dpl.transform()
train, val = dpl.split_data()
logging.info("Data loaded")
model = twitter_model(glove_path=glove_path)
model.build_model(train.values)
model.get_train_data(train.values)
output_model = model.train()
filepath = os.path.join(get_outputs_path(), "trump_bot.h5")
# metrics = model.train(params)
#
# experiment.log_metrics(**metrics)
# save model
output_model.save(filepath)
logger.info("Experiment completed")
def __init__(self):
try:
from polyaxon_client.tracking import Experiment
except ImportError:
raise RuntimeError("This contrib module requires polyaxon-client to be installed. "
"Please install it with command: \n pip install polyaxon-client")
self.experiment = Experiment()
def __init__(self, *args: Any, **kwargs: Any):
try:
from polyaxon.tracking import Run
self.experiment = Run(*args, **kwargs)
except ImportError:
try:
from polyaxon_client.tracking import Experiment
self.experiment = Experiment(*args, **kwargs)
except ImportError:
raise RuntimeError(
"This contrib module requires polyaxon to be installed.\n"
"For Polyaxon v1.x please install it with command: \n pip install polyaxon\n"
"For Polyaxon v0.x please install it with command: \n pip install polyaxon-client"
)
def run_experiment(params):
try:
log_level = get_log_level()
if not log_level:
log_level = logging.INFO
logger.info("Starting experiment")
experiment = Experiment()
logging.basicConfig(level=log_level)
metrics = model.train(params)
experiment.log_metrics(**metrics)
logger.info("Experiment completed")
except Exception as e:
logger.error(f"Experiment failed: {str(e)}")
def __init__(self,
learn,
experiment=None,
monitor='val_loss',
mode='auto'):
super(PolyaxonFastai, self).__init__(learn, monitor=monitor, mode=mode)
self.experiment = experiment
if settings.IS_MANAGED:
self.experiment = self.experiment or Experiment()
def create_experiment(self,
name=None,
framework=None,
tags=None,
description=None,
config=None):
experiment = Experiment(project=self.project,
group_id=self.group_id,
client=self.client,
track_logs=self.track_logs,
track_code=self.track_code,
track_env=self.track_env,
outputs_store=self.outputs_store)
experiment.create(name=name,
framework=framework,
tags=tags,
description=description,
config=config,
base_outputs_path=self.base_outputs_path)
return experiment
def __init__(self,
tensors,
experiment=None,
every_n_iter=None,
every_n_secs=None):
super(PolyaxonLoggingTensorHook,
self).__init__(tensors=tensors,
every_n_iter=every_n_iter,
every_n_secs=every_n_secs)
self.experiment = experiment
if settings.IS_MANAGED:
self.experiment = self.experiment or Experiment()
def main(args):
""" Runs dataLayer processing scripts to turn raw dataLayer from (../raw) into
cleaned dataLayer ready to be analyzed (saved in ../processed).
"""
## Talk to Rune about how dataLayer is handle.
config = TrainingConfig()
config = update_config(args, config)
## For polyaxon
if config.run_polyaxon:
input_root_path = Path(get_data_paths()['data']) #'data'
output_root_path = Path(get_outputs_path())
inpainting_data_path = input_root_path / 'inpainting'
os.environ['TORCH_HOME'] = str(input_root_path / 'pytorch_cache')
config.data_path = inpainting_data_path
config.output_path = output_root_path
config.polyaxon_experiment = Experiment()
pathToData = str(input_root_path /
'/workspace/data_landset8/testImages')
else:
pathToData = Path(r"C:\Users\Morten From\PycharmProjects\testDAta")
logger = logging.getLogger(__name__)
logger.info('making final dataLayer set from raw dataLayer')
logger.info(pathToData)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
B_size = 1
beta_test_path_list = glob(str(pathToData) + "/*/")
ImageDict = get_dataset(beta_test_path_list, batch_size=B_size)
train = ImageDict['train_dataloader']
test = ImageDict['test_dataloader']
genPath = r'C:\Users\Morten From\PycharmProjects\Speciale\Master_Satelite_Image_Inpainting\models\New_400.pth'
outputPathImages = Path(
r'C:\Users\Morten From\PycharmProjects\Speciale\Master_Satelite_Image_Inpainting\images'
)
testGen = UnetGenerator(3, 3, 8)
testGen.load_state_dict(torch.load(genPath))
testGen = testGen.to(device)
testGen.eval()
iterater = 0
for real, SAR in tqdm(train, position=0, leave=True, disable=True):
batchOfImages = real.to(device)
batchOfImagesSAR = SAR.to(device)
outputs = testGen(batchOfImagesSAR)
modelHelper.save_tensor_batchSAR(
batchOfImages, batchOfImagesSAR, outputs, B_size,
Path.joinpath(outputPathImages, 'iter' + str(iterater)))
iterater = iterater + 1
def _plx_log_params(params_dict):
from polyaxon_client.tracking import Experiment
plx_exp = Experiment()
plx_exp.log_params(
**{"pytorch version": torch.__version__, "ignite version": ignite.__version__,}
)
plx_exp.log_params(**params_dict)
class Params:
"""
Description
----
This enables the code to use winslow. Most of this is copied from Params (for Polyaxon).
"""
# This is to load the params from a file
input_thread = threading.Thread(target=get_file_inputs,
args=(),
daemon=True)
input_thread.start()
print("Fetching inputs", end=" ... -> ")
time.sleep(10)
print("done.")
temporal_context = 0
last_interval = None
# polyaxon params
experiment = Experiment()
plx = pp.get_parameters()
param_utils.set_params(plx)
param_utils.check_params(plx)
# if the environment is within winslow
if 'WINSLOW_PIPELINE_NAME' in os.environ:
# output paths
log_dir_mdl = "/workspace/mdl_chkpts/"
if not os.path.exists(log_dir_mdl):
os.mkdir(log_dir_mdl)
print("Directory ", log_dir_mdl, " Created ")
else:
print("Directory ", log_dir_mdl, " already exists")
file_path_mdl = "/workspace/mdl_chkpts/" + plx.get(
'mdl_architecture') + '_' + plx.get('eng_kind') + ".hdf5"
logdir_tb = "/workspace/tf_logs/scalars" + datetime.now().strftime(
"%Y%m%d-%H%M%S")
file_path_raw_mdl = "/workspace/mdl_chkpts/" + plx.get(
'mdl_architecture') + '_' + 'untrained' + ".hdf5"
else:
# output paths
file_path_mdl, file_path_raw_mdl = define_prepare_mdl_path(plx)
logdir_tb = define_prepare_tb_path()
def _run(self, config):
logger = config.get_logger('train')
# setup data_loader instances
data_loader = config.init_obj('data_loader', module_data)
valid_data_loader = data_loader.split_validation()
# build model architecture, then print to console
model = config.init_obj('arch', module_arch)
logger.info(model)
# get function handles of loss and metrics
criterion = getattr(module_loss, config['loss'])
metrics = [getattr(module_metric, met) for met in config['metrics']]
# build optimizer, learning rate scheduler. delete every lines containing lr_scheduler for disabling scheduler
trainable_params = filter(lambda p: p.requires_grad,
model.parameters())
optimizer = config.init_obj('optimizer', torch.optim, trainable_params)
lr_scheduler = config.init_obj('lr_scheduler',
torch.optim.lr_scheduler, optimizer)
experiment = Experiment()
experiment.set_name(config['name'])
description = [
config['trainer']['epochs'], config['arch']['type'],
config['optimizer']['type'], config['optimizer']['args']['lr'],
config['loss']
]
description = "Epochs {0}: Arch: {1} Optimizer: {2} lr: {3} Loss: {4}".format(
*description)
experiment.set_description(description)
if 'type' in config['trainer'].keys():
trainer_name = config['trainer']['type']
else:
trainer_name = "Trainer"
trainer = getattr(trainers_module, trainer_name)
trainer = trainer(model,
criterion,
metrics,
optimizer,
config=config,
data_loader=data_loader,
valid_data_loader=valid_data_loader,
lr_scheduler=lr_scheduler,
experiment=experiment)
trainer.train()
def run_experiment(data_path, model_name, params):
try:
log_level = get_log_level()
if not log_level:
log_level = logging.INFO
logger.info("Starting experiment")
experiment = Experiment()
logging.basicConfig(level=log_level)
# initiate model class
model = Model(model_name)
logger.info(f'{model_name} ok')
# get data
refs = model.get_data(data_path, **params)
logger.info('data ok')
# train model
model.model.train()
logger.info('model trained')
# get pred
preds = refs.apply(lambda x: model.model.predict(x))
logger.info('preds ok')
# eval
precision, recall, f1 = model.model.eval(preds, refs)
logger.info('eval ok')
print(f'Precision: {precision}')
print(f'Recall: {recall}')
print(f'F1: {f1}')
experiment.log_metrics(precision=precision)
experiment.log_metrics(recall=recall)
experiment.log_metrics(f1=f1)
logger.info("Experiment completed")
except Exception as e:
logger.error(f"Experiment failed: {str(e)}")
def run(config, logger=None, local_rank=0, **kwargs):
assert torch.cuda.is_available(), torch.cuda.is_available()
assert (torch.backends.cudnn.enabled
), "Nvidia/Amp requires cudnn backend to be enabled."
dist.init_process_group("nccl", init_method="env://")
# As we passed config with option --manual_config_load
assert hasattr(config, "setup"), (
"We need to manually setup the configuration, please set --manual_config_load "
"to py_config_runner")
config = config.setup()
assert_config(config, TRAINVAL_CONFIG)
# The following attributes are automatically added by py_config_runner
assert hasattr(config, "config_filepath") and isinstance(
config.config_filepath, Path)
assert hasattr(config, "script_filepath") and isinstance(
config.script_filepath, Path)
config.output_path = Path(get_outputs_path())
if dist.get_rank() == 0:
plx_exp = Experiment()
plx_exp.log_params(
**{
"pytorch version": torch.__version__,
"ignite version": ignite.__version__,
})
plx_exp.log_params(**get_params(config, TRAINVAL_CONFIG))
try:
training(
config,
local_rank=local_rank,
with_mlflow_logging=False,
with_plx_logging=True,
)
except KeyboardInterrupt:
logger.info("Catched KeyboardInterrupt -> exit")
except Exception as e: # noqa
logger.exception("")
dist.destroy_process_group()
raise e
dist.destroy_process_group()
default=-3
)
parser.add_argument(
'--batch_size',
type=int,
default=100
)
parser.add_argument(
'--epochs',
type=int,
default=1
)
args = parser.parse_args()
# Polyaxon
experiment = Experiment('mnist')
experiment.create(framework='tensorflow', tags=['examples'])
experiment.log_params(
conv1_size=args.conv1_size,
conv1_out=args.conv1_out,
conv1_activation=args.conv1_activation,
pool1_size=args.pool1_size,
conv2_size=args.conv2_size,
conv2_out=args.conv2_out,
conv2_activation=args.conv2_activation,
pool2_size=args.pool2_size,
fc1_activation=args.fc1_activation,
fc1_size=args.fc1_size,
optimizer=args.optimizer,
log_learning_rate=args.log_learning_rate,
batch_size=args.batch_size,
def _plx_log_artifact(fp):
from polyaxon_client.tracking import Experiment
plx_exp = Experiment()
plx_exp.log_artifact(fp)
# Polyaxon from polyaxon_client.tracking import Experiment from sklearn.linear_model import SGDClassifier from sklearn.model_selection import cross_val_score from sklearn import datasets from sklearn import linear_model from sklearn.model_selection import train_test_split from sklearn import metrics import os import pandas as pd # Polyaxon experiment = Experiment() dataset = datasets.load_boston() # x 训练特征：['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', #'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT'] x = dataset.data target = dataset.target #把label变为(?, 1)维度，为了使用下面的数据集合分割 y = np.reshape(target, (len(target), 1)) #讲数据集1:3比例分割为 测试集：训练集 x_train, x_verify, y_train, y_verify = train_test_split(x, y, random_state=1) ''' x_train的shape：(379, 13) y_train的shape：(379, 1)
def main():
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument('--batch-size', type=int, default=1000, metavar='N',
help='input batch size for training (default: 1000)')
parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
help='input batch size for testing (default: 1000)')
parser.add_argument('--epochs', type=int, default=15, metavar='N',
help='number of epochs to train (default: 9)')
parser.add_argument('--lr', type=float, default=1.0, metavar='LR',
help='learning rate (default: 1.0)')
parser.add_argument('--no-cuda', action='store_true', default=False,
help='disables CUDA training')
parser.add_argument('--seed', type=int, default=42, metavar='S',
help='random seed (default: 42)')
args = parser.parse_args()
experiment = Experiment()
logger = logging.getLogger('main')
logger.setLevel(get_log_level())
use_cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
device = torch.device("cuda" if use_cuda else "cpu")
logger.info('%s', device)
kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
train_loader = torch.utils.data.DataLoader(
datasets.MNIST('.', train=True, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=args.batch_size, shuffle=True, **kwargs)
test_loader = torch.utils.data.DataLoader(
datasets.MNIST('.', train=False, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=args.test_batch_size, shuffle=True, **kwargs)
model = Net().to(device)
model_path = os.path.join(get_outputs_path(), 'model.p')
state_path = os.path.join(get_outputs_path(), 'state.json')
start = 1
if os.path.isfile(model_path):
model.load_state_dict(torch.load(model_path))
logger.info('%s', 'Model Loaded')
if os.path.isfile(state_path):
with open(state_path, 'r') as f:
data = json.load(f)
start = data['epoch']
logger.info('%s', 'State Loaded')
optimizer = optim.SGD(model.parameters(), lr=args.lr)
with SummaryWriter(log_dir=get_outputs_path()) as writer:
for epoch in range(start, args.epochs + 1):
train(epoch, writer, experiment, args, model, device, train_loader, optimizer)
test(epoch, writer, experiment, args, model, device, test_loader)
torch.save(model.state_dict(), model_path)
with open(state_path, 'w') as f:
data = {
'epoch' : epoch
}
json.dump(data, f)
y = iris.target
h = .02 # step size in the mesh
# Create color maps
cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])
cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])
for weights in ['uniform', 'distance']:
# we create an instance of Neighbours Classifier and fit the data.
clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)
clf.fit(X, y)
y_model = clf.predict(X)
model_accuracy = accuracy_score(y, y_model)
experiment = Experiment()
experiment.create()
experiment.log_metrics(model_accuracy=model_accuracy)
experiment.log_params(weights=weights, n_neighbors=n_neighbors)
# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, x_max]x[y_min, y_max].
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
np.arange(y_min, y_max, h))
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.figure()
def main(config):
logging.basicConfig(level=logging.INFO)
logging.info("STARTING PROGRAM")
if config.TRAIN.POLYAXON:
from polyaxon_client.tracking import Experiment, get_data_paths, get_outputs_path
data_dir = get_data_paths()
config.DATASET.OUTPUT_PATH = get_outputs_path()
config.DATASET.PATH = os.path.join(data_dir['data1'],
config.DATASET.PATH_NAS)
model_path = os.path.join(data_dir['data1'],
config.MODEL.PRETRAINED_NAS)
logger = logging.getLogger()
logger.setLevel(logging.INFO)
logger.addHandler(
logging.FileHandler(
os.path.join(config.DATASET.OUTPUT_PATH,
'Heatmaps_from_human_joints.log')))
# Polyaxon
experiment = Experiment()
else:
logger = logging.getLogger()
logger.setLevel(logging.INFO)
logger.addHandler(
logging.FileHandler(
os.path.join(config.DATASET.OUTPUT_PATH,
'Heatmaps_Resnet101.log')))
model_path = config.MODEL.PRETRAINED
trainloader, valloader = utils.load_split_train_val(
config.DATASET.PATH, "train", "validation", config)
print('batch size', config.TRAIN.BATCH_SIZE)
print('dataset', config.DATASET.PATH_NAS)
print("weights", config.TRAIN.UPDATE_WEIGHTS)
print("Model: ", model_path)
print("LR: ", config.TRAIN.LR)
model = utils.model_pose_resnet.get_pose_net(model_path, is_train=True)
model.eval()
for name, parameter in model.named_parameters():
parameter.requires_grad = config.TRAIN.UPDATE_WEIGHTS
if "deconv" in name or "final" in name:
parameter.requires_grad = True
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
optimizer = optim.Adam(model.parameters(), lr=config.TRAIN.LR)
model.to(device)
# Decay LR by a factor of 0.1 every 3 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=20, gamma=0.01)
writer = SummaryWriter(config.DATASET.OUTPUT_PATH)
best_acc = 0
for epoch in range(config.TRAIN.END_EPOCH):
criterion = nn.MSELoss()
logger.info('Epoch {}/{}'.format(epoch, config.TRAIN.END_EPOCH - 1))
logger.info('-' * 10)
acc = utils.AverageMeter()
batch_loss = utils.AverageMeter()
for i, (inputs, labels) in enumerate(trainloader):
inputs, labels = inputs.to(device), labels.to(device)
# print(summary(model, tuple(inputs.size())[1:]))
logps = model.forward(inputs)
criterion = nn.MSELoss()
loss = criterion(logps, labels.float())
batch_loss.update(loss.item(), inputs.size(0))
optimizer.zero_grad()
loss.backward()
optimizer.step()
_, avg_acc, cnt, pred, target, dists = utils.accuracy(
logps.detach().cpu().numpy(),
labels.detach().cpu().numpy(),
thr=config.TRAIN.THRESHOLD)
print("Current batch accuracy: ", avg_acc)
acc.update(avg_acc, cnt)
print("Batch {} train accurcy: {}, loss: {}".format(
i, acc.avg, batch_loss.avg))
writer.add_scalar('Loss/train', float(batch_loss.avg), epoch)
val_acc = run_val(model, valloader, device, criterion, writer, epoch,
config)
logger.info(
'Train Loss: {:.4f} Train Acc: {:.4f} Val Acc: {:.4f}'.format(
batch_loss.avg, acc.avg, val_acc))
if val_acc > best_acc:
best_acc = val_acc
logging.info("best val at epoch: " + str(epoch))
torch.save(
{
'epoch': epoch,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'loss': batch_loss.avg,
}, os.path.join(config.DATASET.OUTPUT_PATH, "best_model.pt"))
if epoch % 250 == 0:
torch.save(
{
'epoch': epoch,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'loss': batch_loss.avg,
},
os.path.join(config.DATASET.OUTPUT_PATH,
"model" + str(epoch) + ".pt"))
logger.info('Best val Acc: {:4f}'.format(best_acc))
parser.add_argument('--skip_top',
type=int,
default=30,
help='Top occurring words to skip')
parser.add_argument('--maxlen', type=int, default=100)
parser.add_argument('--batch_size', type=int, default=32)
parser.add_argument('--num_nodes', type=int, default=8)
parser.add_argument('--optimizer', type=str, default='adam')
parser.add_argument('--log_learning_rate', type=int, default=-3)
parser.add_argument('--dropout', type=float, default=0.8)
parser.add_argument('--epochs', type=int, default=1)
parser.add_argument('--seed', type=int, default=234)
args = parser.parse_args()
# Polyaxon
experiment = Experiment('bidirectional-lstm')
experiment.create(framework='keras', tags=['examples'])
experiment.log_params(max_features=args.max_features,
skip_top=args.skip_top,
maxlen=args.maxlen,
batch_size=args.batch_size,
num_nodes=args.num_nodes,
optimizer=args.optimizer,
log_learning_rate=args.log_learning_rate,
dropout=args.dropout,
epochs=args.epochs,
seed=args.seed)
logger.info('Loading data...')
(x_train, y_train), (x_test,
y_test) = imdb.load_data(num_words=args.max_features,
def testClassifier(classifier, scaled_test_x, test_y, test_ids):
test_y_pred = classifier.predict_classes(scaled_test_x)
prediction = dict(zip(test_ids, test_y_pred.flatten()))
reality = dict(zip(test_ids, test_y))
return prediction, reality
def testClassifier(classifier, scaled_test_x, test_y, test_ids):
test_y_pred = classifier.predict_classes(scaled_test_x)
prediction = dict(zip(test_ids, test_y_pred.flatten()))
reality = dict(zip(test_ids, test_y))
return prediction, reality
# Run dat naow
experiment = Experiment()
# 0. Read Args
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--cluster', default='no cluster given', type=str)
parser.add_argument('--batch_size', default=128, type=int)
parser.add_argument('--learning_rate', default=0.02, type=float)
parser.add_argument('--dropout', default=0.2, type=float)
parser.add_argument('--num_epochs', default=10, type=int)
type=int,
default=2000,
help='The maximum number of features.')
parser.add_argument('--max_df',
type=float,
default=1.0,
help='the maximum document frequency.')
parser.add_argument(
'--C',
type=float,
default=1.0,
help='Inverse of regularization strength of LogisticRegression')
args = parser.parse_args()
# Polyaxon
experiment = Experiment(project='newsgroup')
experiment.create()
experiment.log_params(ngram_range=(args.ngram, args.ngram),
max_features=args.max_features,
max_df=args.max_df,
C=args.C)
# Train and eval the model with given parameters.
# Polyaxon
metrics = train_and_eval(ngram_range=(args.ngram, args.ngram),
max_features=args.max_features,
max_df=args.max_df,
C=args.C)
# Logging metrics
print("Testing metrics: {}", metrics)
# ==================================================================================================#
# PATHS SETUP #
# ==================================================================================================#
run_name = args.run_name
if cluster:
data_paths = get_data_paths()
patient_path = data_paths[
'data1'] + "/HHase_Robotic_RL/NAS_Sacrum_Scans/Patient_files/"
patient_data_path = data_paths[
'data1'] + "/HHase_Robotic_RL/NAS_Sacrum_Scans/"
load_model_path = data_paths[
'data1'] + "/HHase_Robotic_RL/Models/model_best.pth"
output_path = get_outputs_path()
tensorboard_path = get_outputs_path()
experiment = Experiment()
else:
patient_path = "./../Data/Patient_files/"
patient_data_path = "./../Data/"
output_path = './'
tensorboard_path = './runs/'
load_model_path = "./../Data/pretrained_model/model_best.pth"
model_save_path = output_path + "/models/{}.pt".format(run_name)
#load_model_path = output_path + "/models/{}.pt".format(run_name)
datetime = datetime.now()
tensorboard_name = 'Nov' + datetime.strftime(
"%d_%H-%M-%S") + '_Rachet-' + run_name
# ==================================================================================================#
# PARAMETER INITIALIZATION #
Fr = FR / Z
P = Ca / (Ca + Fa)
R = Ca / (Ca + Fr)
SA = Ca + Cr
F = (2 * P * R)/( P + R)
RCa = Ca / (Fr + Ca)
RFa = Fa / (Cr + Fa)
D = IncorrectRejectionRate / CorrectRejectionRate
Da = RCa / RFa
Df = math.sqrt((Da*D))
return Df
experiment = Experiment()
# 0. Read Args
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--batch_size',
default=128,
type=int)
parser.add_argument(
'--learning_rate',
default=0.001,
type=float)
def __init__(self):
self.experiment = Experiment()
def polyaxon_checkpoint_fn(lightning_module):
from polyaxon_client.tracking import Experiment
exp = Experiment()
exp.outputs_store.upload_dir(lightning_module.config.save_path)
exp.outputs_store.upload_dir('lightning_logs')
