def test_split_dataset_random(self):
original = [1, 2, 3, 4, 5]
subset1, subset2 = datasets.split_dataset_random(original, 2)
reconst = sorted(set(subset1).union(subset2))
self.assertEqual(reconst, original)
subset1a, subset2a = datasets.split_dataset_random(original, 2, seed=3)
reconst = sorted(set(subset1a).union(subset2a))
self.assertEqual(reconst, original)
subset1b, subset2b = datasets.split_dataset_random(original, 2, seed=3)
self.assertEqual(set(subset1a), set(subset1b))
self.assertEqual(set(subset2a), set(subset2b))
reconst = sorted(set(subset1a).union(subset2a))
self.assertEqual(reconst, original)
# xに設計変数を格納 x = input_data.loc[:, ["Column_num","Color_white","Color_black", "Icon_A", "Icon_B", "Icon_C", "Layout_thin", "Layout_thick", "Layout_ratio", "Menu_rectangle", "Menu_float", "Menu_convex", "Shape_corner", "Shape_round", "Shape_chamfer", "Header_none", "Header_half", "Header_full", "Char_none", "Char_half", "Char_full"]] #df.loc[:, ['col_1','col_2']]で烈ラベル指定 # tに15点満点のアンケート結果(Total)を格納 t = input_data.loc[:,["Total"]] #Chainerがデフォルトで用いるfloat32型に変換 x = np.array(x,np.float32) t = np.array(t,np.float32) # 分類型のときはint, 回帰型のときはfloat # TupleDatasetを使ってxとtをセットにする from chainer.datasets import TupleDataset dataset = TupleDataset(x,t) # datasetを任意の割合でtrain(学習用データ)とvalid(検証用データ)に分割する from chainer.datasets import split_dataset_random train, valid = split_dataset_random(dataset, int(len(dataset) * 0.8), seed=0) # 抽出するデータは固定 # 用意したデータをどう抽出し，どうグループ(バッチ)わけするか設定する from chainer import iterators batchsize = 128 train_iter = iterators.SerialIterator(train, batchsize, shuffle=True, repeat=True) valid_iter = iterators.SerialIterator(valid, batchsize, shuffle=True, repeat=True) # 何周も何周も全データを繰り返し読み出す必要がある場合はrepeat引数をTrue # 1周が終わったらそれ以上データを取り出したくない場合はこれをFalse # デフォルトではTrueなので本当は書かなくてもいい # ------------------------------------------------------------------------------ # ネットワークの定義と最適化手法の選択 import chainer.links as L import chainer.functions as F # ネットワークの定義
def test_split_dataset_random(self):
original = [1, 2, 3, 4, 5]
subset1, subset2 = datasets.split_dataset_random(original, 2)
reconst = sorted(set(subset1).union(subset2))
self.assertEqual(reconst, original)
def main():
# Parse the arguments.
args = parse_arguments()
# Set up some useful variables that will be used later on.
method = args.method
if args.label != 'all':
labels = args.label
cache_dir = os.path.join('input', '{}_{}'.format(method, labels))
class_num = len(labels) if isinstance(labels, list) else 1
else:
labels = None
cache_dir = os.path.join('input', '{}_all'.format(method))
class_num = len(D.get_qm9_label_names())
# Get the filename corresponding to the cached dataset, based on the amount
# of data samples that need to be parsed from the original dataset.
num_data = args.num_data
if num_data >= 0:
dataset_filename = 'data_{}.npz'.format(num_data)
else:
dataset_filename = 'data.npz'
# Load the cached dataset.
dataset_cache_path = os.path.join(cache_dir, dataset_filename)
dataset = None
if os.path.exists(dataset_cache_path):
print('Loading cached dataset from {}.'.format(dataset_cache_path))
dataset = NumpyTupleDataset.load(dataset_cache_path)
if dataset is None:
print('Preprocessing dataset...')
preprocessor = preprocess_method_dict[method]()
if num_data >= 0:
# Select the first `num_data` samples from the dataset.
target_index = numpy.arange(num_data)
dataset = D.get_qm9(preprocessor,
labels=labels,
target_index=target_index)
else:
# Load the entire dataset.
dataset = D.get_qm9(preprocessor, labels=labels)
# Cache the laded dataset.
if not os.path.exists(cache_dir):
os.makedirs(cache_dir)
if isinstance(dataset, NumpyTupleDataset):
NumpyTupleDataset.save(dataset_cache_path, dataset)
# Scale the label values, if necessary.
if args.scale == 'standardize':
print('Fit StandardScaler to the labels.')
scaler = StandardScaler()
if isinstance(dataset, NumpyTupleDataset):
scaler.fit(dataset.get_datasets()[-1])
else:
y = numpy.array([data.y for data in dataset])
scaler.fit(y)
else:
print('No standard scaling was selected.')
scaler = None
# Split the dataset into training and validation.
train_data_size = int(len(dataset) * args.train_data_ratio)
train, valid = split_dataset_random(dataset, train_data_size, args.seed)
# Set up the predictor.
predictor = set_up_predictor(method, args.unit_num, args.conv_layers,
class_num, scaler)
# Set up the regressor.
device = chainer.get_device(args.device)
metrics_fun = {'mae': F.mean_absolute_error, 'rmse': rmse}
regressor = Regressor(predictor,
lossfun=F.mean_squared_error,
metrics_fun=metrics_fun,
device=device)
print('Training...')
run_train(regressor,
train,
valid=valid,
batch_size=args.batchsize,
epoch=args.epoch,
out=args.out,
extensions_list=None,
device=device,
converter=dataset.converter,
resume_path=None)
# Save the regressor's parameters.
model_path = os.path.join(args.out, args.model_filename)
print('Saving the trained model to {}...'.format(model_path))
regressor.save_pickle(model_path, protocol=args.protocol)
def main():
# Supported preprocessing/network list
method_list = ['nfp', 'ggnn', 'schnet', 'weavenet', 'rsgcn']
label_names = [
'A', 'B', 'C', 'mu', 'alpha', 'h**o', 'lumo', 'gap', 'r2', 'zpve',
'U0', 'U', 'H', 'G', 'Cv'
]
scale_list = ['standardize', 'none']
parser = argparse.ArgumentParser(description='Regression with QM9.')
parser.add_argument('--method',
'-m',
type=str,
choices=method_list,
default='nfp')
parser.add_argument('--label',
'-l',
type=str,
choices=label_names,
default='',
help='target label for regression, '
'empty string means to predict all '
'property at once')
parser.add_argument('--scale',
type=str,
choices=scale_list,
default='standardize',
help='Label scaling method')
parser.add_argument('--conv-layers', '-c', type=int, default=4)
parser.add_argument('--batchsize', '-b', type=int, default=32)
parser.add_argument('--gpu', '-g', type=int, default=-1)
parser.add_argument('--out', '-o', type=str, default='result')
parser.add_argument('--epoch', '-e', type=int, default=20)
parser.add_argument('--unit-num', '-u', type=int, default=16)
parser.add_argument('--seed', '-s', type=int, default=777)
parser.add_argument('--train-data-ratio', '-t', type=float, default=0.7)
args = parser.parse_args()
seed = args.seed
train_data_ratio = args.train_data_ratio
method = args.method
if args.label:
labels = args.label
cache_dir = os.path.join('input', '{}_{}'.format(method, labels))
class_num = len(labels) if isinstance(labels, list) else 1
else:
labels = None
cache_dir = os.path.join('input', '{}_all'.format(method))
class_num = len(D.get_qm9_label_names())
# Dataset preparation
dataset = None
if os.path.exists(cache_dir):
print('load from cache {}'.format(cache_dir))
dataset = NumpyTupleDataset.load(os.path.join(cache_dir, 'data.npz'))
if dataset is None:
print('preprocessing dataset...')
preprocessor = preprocess_method_dict[method]()
dataset = D.get_qm9(preprocessor, labels=labels)
os.makedirs(cache_dir)
NumpyTupleDataset.save(os.path.join(cache_dir, 'data.npz'), dataset)
if args.scale == 'standardize':
# Standard Scaler for labels
ss = StandardScaler()
labels = ss.fit_transform(dataset.get_datasets()[-1])
dataset = NumpyTupleDataset(*dataset.get_datasets()[:-1], labels)
train_data_size = int(len(dataset) * train_data_ratio)
train, val = split_dataset_random(dataset, train_data_size, seed)
# Network
n_unit = args.unit_num
conv_layers = args.conv_layers
if method == 'nfp':
print('Train NFP model...')
model = GraphConvPredictor(
NFP(out_dim=n_unit, hidden_dim=n_unit, n_layers=conv_layers),
MLP(out_dim=class_num, hidden_dim=n_unit))
elif method == 'ggnn':
print('Train GGNN model...')
model = GraphConvPredictor(
GGNN(out_dim=n_unit, hidden_dim=n_unit, n_layers=conv_layers),
MLP(out_dim=class_num, hidden_dim=n_unit))
elif method == 'schnet':
print('Train SchNet model...')
model = GraphConvPredictor(
SchNet(out_dim=class_num, hidden_dim=n_unit, n_layers=conv_layers),
None)
elif method == 'weavenet':
print('Train WeaveNet model...')
n_atom = 20
n_sub_layer = 1
weave_channels = [50] * conv_layers
model = GraphConvPredictor(
WeaveNet(weave_channels=weave_channels,
hidden_dim=n_unit,
n_sub_layer=n_sub_layer,
n_atom=n_atom), MLP(out_dim=class_num, hidden_dim=n_unit))
elif method == 'rsgcn':
print('Train RSGCN model...')
model = GraphConvPredictor(
RSGCN(out_dim=n_unit, hidden_dim=n_unit, n_layers=conv_layers),
MLP(out_dim=class_num, hidden_dim=n_unit))
else:
raise ValueError('[ERROR] Invalid method {}'.format(method))
train_iter = I.SerialIterator(train, args.batchsize)
val_iter = I.SerialIterator(val,
args.batchsize,
repeat=False,
shuffle=False)
def scaled_abs_error(x0, x1):
if isinstance(x0, Variable):
x0 = cuda.to_cpu(x0.data)
if isinstance(x1, Variable):
x1 = cuda.to_cpu(x1.data)
if args.scale == 'standardize':
scaled_x0 = ss.inverse_transform(cuda.to_cpu(x0))
scaled_x1 = ss.inverse_transform(cuda.to_cpu(x1))
diff = scaled_x0 - scaled_x1
elif args.scale == 'none':
diff = cuda.to_cpu(x0) - cuda.to_cpu(x1)
return numpy.mean(numpy.absolute(diff), axis=0)[0]
classifier = L.Classifier(model,
lossfun=F.mean_squared_error,
accfun=scaled_abs_error)
if args.gpu >= 0:
chainer.cuda.get_device_from_id(args.gpu).use()
classifier.to_gpu()
optimizer = O.Adam()
optimizer.setup(classifier)
updater = training.StandardUpdater(train_iter,
optimizer,
device=args.gpu,
converter=concat_mols)
trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=args.out)
trainer.extend(
E.Evaluator(val_iter,
classifier,
device=args.gpu,
converter=concat_mols))
trainer.extend(E.snapshot(), trigger=(args.epoch, 'epoch'))
trainer.extend(E.LogReport())
trainer.extend(
E.PrintReport([
'epoch', 'main/loss', 'main/accuracy', 'validation/main/loss',
'validation/main/accuracy', 'elapsed_time'
]))
trainer.extend(E.ProgressBar())
trainer.run()
def get_mpii_dataset(insize,
image_root,
annotations,
train_size=0.5,
min_num_keypoints=1,
use_cache=False,
seed=0):
dataset_type = 'mpii'
annotations = json.load(open(annotations, 'r'))
# filename => keypoints, bbox, is_visible, is_labeled
images = {}
# DEBUGGING OVERFITTING
# create a list containing the actual iamges in folder -> for debugging
from os import listdir
files = listdir(image_root)
for filename in np.unique([anno['filename'] for anno in annotations]):
if filename in files:
images[filename] = [], [], [], [], [], [
] # include scale and position
for anno in annotations:
if anno['filename'] in files:
is_visible = [anno['is_visible'][k] for k in KEYPOINT_NAMES[1:]]
if sum(is_visible) < min_num_keypoints:
continue
keypoints = [
anno['joint_pos'][k][::-1] for k in KEYPOINT_NAMES[1:]
]
x1, y1, x2, y2 = anno['head_rect']
entry = images[anno['filename']]
entry[0].append(np.array(keypoints)) # array of y,x
entry[1].append(np.array([x1, y1, x2 - x1, y2 - y1])) # x, y, w, h
entry[2].append(np.array(is_visible, dtype=np.bool))
is_labeled = np.ones(len(is_visible), dtype=np.bool)
entry[3].append(is_labeled)
entry[4].append(np.array(anno['scale']))
entry[5].append(np.array(anno['position']))
# THIS IS THE OFFICIAL CODE WHEN ALL IMAGES OR USED
# for filename in np.unique([anno['filename'] for anno in annotations]):
# images[filename] = [], [], [], [], [], [] # include scale and position
#
# for anno in annotations:
# is_visible = [anno['is_visible'][k] for k in KEYPOINT_NAMES[1:]]
# if sum(is_visible) < min_num_keypoints:
# continue
# keypoints = [anno['joint_pos'][k][::-1] for k in KEYPOINT_NAMES[1:]]
#
# x1, y1, x2, y2 = anno['head_rect']
#
# entry = images[anno['filename']]
# entry[0].append(np.array(keypoints)) # array of y,x
# entry[1].append(np.array([x1, y1, x2 - x1, y2 - y1])) # x, y, w, h
# entry[2].append(np.array(is_visible, dtype=np.bool))
# is_labeled = np.ones(len(is_visible), dtype=np.bool)
# entry[3].append(is_labeled)
# entry[4].append(np.array(anno['scale']))
# entry[5].append(np.array(anno['position']))
# split dataset
train_images, test_images = split_dataset_random(
list(images.keys()), int(len(images) * train_size), seed=seed)
train_set = KeypointDataset2D(
dataset_type=dataset_type,
insize=insize,
keypoint_names=KEYPOINT_NAMES,
edges=np.array(EDGES),
flip_indices=FLIP_INDICES,
keypoints=[images[i][0] for i in train_images],
bbox=[images[i][1] for i in train_images],
is_visible=[images[i][2] for i in train_images],
is_labeled=[images[i][3] for i in train_images],
scale=[images[i][4] for i in train_images],
position=[images[i][5] for i in train_images],
image_paths=train_images,
image_root=image_root,
use_cache=use_cache,
do_augmentation=False # TODO must be True
)
test_set = KeypointDataset2D(
dataset_type=dataset_type,
insize=insize,
keypoint_names=KEYPOINT_NAMES,
edges=np.array(EDGES),
flip_indices=FLIP_INDICES,
keypoints=[images[i][0] for i in test_images],
bbox=[images[i][1] for i in test_images],
is_visible=[images[i][2] for i in test_images],
is_labeled=[images[i][3] for i in test_images],
scale=[images[i][4] for i in test_images],
position=[images[i][5] for i in test_images],
image_paths=test_images,
image_root=image_root,
use_cache=use_cache,
do_augmentation=False)
return train_set, test_set
def main():
parser = argparse.ArgumentParser(description='Chainer CIFAR example:')
parser.add_argument('--dataset', '-d', default='cifar10',
help='The dataset to use: cifar10 or cifar100')
parser.add_argument('--batchsize', '-b', type=int, default=64,
help='Number of images in each mini-batch')
parser.add_argument('--learnrate', '-l', type=float, default=0.05,
help='Learning rate for SGD')
parser.add_argument('--epoch', '-e', type=int, default=300,
help='Number of sweeps over the dataset to train')
parser.add_argument('--gpu', '-g', type=int, default=-1,
help='GPU ID (negative value indicates CPU)')
parser.add_argument('--out', '-o', default='result',
help='Directory to output the result')
parser.add_argument('--resume', '-r', default='',
help='Resume the training from snapshot')
parser.add_argument('--seed', '-s', type=int, default=123,
help='seed for split dataset into train & validation')
parser.add_argument('--augment', '-a', type=bool, default=True,
help='whether augment dataset or not')
parser.add_argument('--parallel', '-p', type=bool, default=True,
help='use multiprocess iterator or not')
args = parser.parse_args()
print('GPU: {}'.format(args.gpu))
print('# Minibatch-size: {}'.format(args.batchsize))
print('# epoch: {}'.format(args.epoch))
print('')
# Set up a neural network to train.
# Classifier reports softmax cross entropy loss and accuracy at every
# iteration, which will be used by the PrintReport extension below.
if args.dataset == 'cifar10':
print('Using CIFAR10 dataset.')
class_labels = 10
train, test = get_cifar10()
elif args.dataset == 'cifar100':
print('Using CIFAR100 dataset.')
class_labels = 100
train, test = get_cifar100()
else:
raise RuntimeError('Invalid dataset choice.')
if args.augment:
train = TransformDataset(train, transform)
else:
train, val = split_dataset_random(train, 45000, seed=args.seed)
model = L.Classifier(DenseNetCifar(n_class=class_labels))
if args.gpu >= 0:
# Make a specified GPU current
chainer.cuda.get_device_from_id(args.gpu).use()
model.to_gpu() # Copy the model to the GPU
optimizer = chainer.optimizers.NesterovAG(lr=args.learnrate, momentum=0.9)
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer.WeightDecay(5e-4))
train_iter = chainer.iterators.SerialIterator(train, args.batchsize)
test_iter = chainer.iterators.SerialIterator(test, args.batchsize,
repeat=False, shuffle=False)
if args.parallel:
train_iter = chainer.iterators.MultiprocessIterator(
train, args.batchsize, n_processes=2)
test_iter = chainer.iterators.MultiprocessIterator(
test, args.batchsize, repeat=False, shuffle=False, n_processes=2)
# Set up a trainer
updater = training.StandardUpdater(train_iter, optimizer, device=args.gpu)
trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=args.out)
# Evaluate the model with the test dataset for each epoch
trainer.extend(extensions.Evaluator(test_iter, model, device=args.gpu))
# Reduce the learning rate by half every 25 epochs.
trainer.extend(extensions.ExponentialShift('lr', 0.5),
trigger=(25, 'epoch'))
# Dump a computational graph from 'loss' variable at the first iteration
# The "main" refers to the target link of the "main" optimizer.
trainer.extend(extensions.dump_graph('main/loss'))
# Take a snapshot at each epoch
trainer.extend(extensions.snapshot(), trigger=(args.epoch, 'epoch'))
trainer.extend(extensions.snapshot_object(model.predictor,
filename='densenet.model'), trigger=(args.epoch, 'epoch'))
trainer.extend(extensions.observe_lr(), trigger=(1, 'epoch'))
# Write a log of evaluation statistics for each epoch
trainer.extend(extensions.LogReport())
# Print selected entries of the log to stdout
# Here "main" refers to the target link of the "main" optimizer again, and
# "validation" refers to the default name of the Evaluator extension.
# Entries other than 'epoch' are reported by the Classifier link, called by
# either the updater or the evaluator.
trainer.extend(extensions.PrintReport(
['epoch', 'main/loss', 'validation/main/loss',
'main/accuracy', 'validation/main/accuracy', 'lr', 'elapsed_time']))
trainer.extend(extensions.PlotReport(
y_keys=['main/loss', 'validation/main/loss'], file_name='loss.png'))
trainer.extend(extensions.PlotReport(
y_keys=['main/accuracy', 'validation/main/accuracy'], file_name='accuracy.png'))
# Print a progress bar to stdout
trainer.extend(extensions.ProgressBar())
if args.resume:
# Resume from a snapshot
chainer.serializers.load_npz(args.resume, trainer)
# Run the training
trainer.run()
def main():
parser = argparse.ArgumentParser(
description='Chainer example: POS-tagging')
parser.add_argument('--batchsize', '-b', type=int, default=30,
help='Number of images in each mini batch')
parser.add_argument('--epoch', '-e', type=int, default=20,
help='Number of sweeps over the dataset to train')
parser.add_argument('--gpu', '-g', type=int, default=-1,
help='GPU ID (negative value indicates CPU)')
parser.add_argument('--out', '-o', default='result',
help='Directory to output the result')
parser.add_argument('--resume', '-r', default='',
help='Resume the training from snapshot')
args = parser.parse_args()
vocab = collections.defaultdict(lambda: len(vocab))
pos_vocab = collections.defaultdict(lambda: len(pos_vocab))
# Convert word sequences and pos sequences to integer sequences.
nltk.download('brown')
data = []
for sentence in nltk.corpus.brown.tagged_sents():
xs = numpy.array([vocab[lex] for lex, _ in sentence], 'i')
ys = numpy.array([pos_vocab[pos] for _, pos in sentence], 'i')
data.append((xs, ys))
print('# of sentences: {}'.format(len(data)))
print('# of words: {}'.format(len(vocab)))
print('# of pos: {}'.format(len(pos_vocab)))
model = CRF(len(vocab), len(pos_vocab))
if args.gpu >= 0:
chainer.cuda.get_device(args.gpu).use()
model.to_gpu(args.gpu)
optimizer = chainer.optimizers.Adam()
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer.WeightDecay(0.0001))
test_data, train_data = datasets.split_dataset_random(
data, len(data) // 10, seed=0)
train_iter = chainer.iterators.SerialIterator(train_data, args.batchsize)
test_iter = chainer.iterators.SerialIterator(test_data, args.batchsize,
repeat=False, shuffle=False)
updater = training.StandardUpdater(
train_iter, optimizer, converter=convert, device=args.gpu)
trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=args.out)
evaluator = extensions.Evaluator(
test_iter, model, device=args.gpu, converter=convert)
# Only validate in each 1000 iteration
trainer.extend(evaluator, trigger=(1000, 'iteration'))
trainer.extend(extensions.LogReport(trigger=(100, 'iteration')),
trigger=(100, 'iteration'))
trainer.extend(
extensions.MicroAverage(
'main/correct', 'main/total', 'main/accuracy'))
trainer.extend(
extensions.MicroAverage(
'validation/main/correct', 'validation/main/total',
'validation/main/accuracy'))
trainer.extend(
extensions.PrintReport(
['epoch', 'main/loss', 'validation/main/loss',
'main/accuracy', 'validation/main/accuracy', 'elapsed_time']),
trigger=(100, 'iteration'))
trainer.extend(extensions.ProgressBar(update_interval=10))
if args.resume:
chainer.serializers.load_npz(args.resume, trainer)
trainer.run()
def main():
parser = argparse.ArgumentParser(description="DCGAN")
parser.add_argument("--batchsize", "-b", type=int, default=128)
parser.add_argument("--epoch", "-e", type=int, default=100)
parser.add_argument("--gpu", "-g", type=int, default=0)
parser.add_argument("--snapshot_interval", "-s", type=int, default=10)
parser.add_argument("--display_interval", "-d", type=int, default=1)
parser.add_argument("--n_dimz", "-z", type=int, default=100)
parser.add_argument("--dataset", "-ds", type=str, default="mnist")
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--out", "-o", type=str, default="result")
parser.add_argument("--resume", '-r', default='')
args = parser.parse_args()
#import .py
import Updater
import Visualize
import Network.mnist_net as Network
#print settings
print("GPU:{}".format(args.gpu))
print("epoch:{}".format(args.epoch))
print("Minibatch_size:{}".format(args.batchsize))
print("Dataset:{}".format(args.dataset))
print('')
out = os.path.join(args.out, args.dataset)
#Set up NN
gen = Network.Generator(n_hidden=args.n_dimz)
dis = Network.Discriminator()
enc = Network.Encoder(n_hidden=args.n_dimz)
if args.gpu >= 0:
chainer.backends.cuda.get_device_from_id(args.gpu).use()
gen.to_gpu()
dis.to_gpu()
#Make optimizer
def make_optimizer(model, alpha=0.0002, beta1=0.5):
optimizer = optimizers.Adam(alpha=alpha, beta1=beta1) #init_lr = alpha
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer_hooks.WeightDecay(0.0001), 'hook_dec')
return optimizer
opt_gen = make_optimizer(gen)
opt_dis = make_optimizer(dis)
opt_enc = make_optimizer(enc)
#Get dataset
train_valid, test = mnist.get_mnist(withlabel=True, ndim=3)
train, valid = split_dataset_random(train_valid, 50000, seed=0)
train = [i[0] for i in train if(i[1]==1)] #ラベル1のみを選択
#Setup iterator
train_iter = iterators.SerialIterator(train, args.batchsize)
#Setup updater
updater = Updater.DCGANUpdater(
models=(gen, dis, enc),
iterator=train_iter,
optimizer={'gen':opt_gen, 'dis':opt_dis, 'enc':opt_enc},
device=args.gpu)
#Setup trainer
trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=out)
snapshot_interval = (args.snapshot_interval, 'epoch')
display_interval = (args.display_interval, 'epoch')
trainer.extend(extensions.snapshot_object(
gen, 'gen_epoch_{.updater.epoch}.npz'),
trigger=snapshot_interval)
trainer.extend(extensions.snapshot_object(
dis, 'dis_epoch_{.updater.epoch}.npz'),
trigger=snapshot_interval)
trainer.extend(extensions.snapshot_object(
enc, 'enc_epoch_{.updater.epoch}.npz'),
trigger=snapshot_interval)
trainer.extend(extensions.LogReport(
trigger=display_interval))
trainer.extend(extensions.PrintReport([
'epoch', 'gen/loss', 'dis/loss', 'enc/loss', 'elapsed_time'
]), trigger=display_interval)
trainer.extend(extensions.ProgressBar())
trainer.extend(Visualize.out_generated_image(
gen, dis, enc,
10, 10, args.seed, args.out, args.dataset),
trigger=snapshot_interval)
if args.resume:
chainer.serializers.load_npz(args.resume, trainer)
trainer.run()
def main():
# Parse the arguments.
args = parse_arguments()
if args.label:
labels = args.label
class_num = len(labels) if isinstance(labels, list) else 1
else:
raise ValueError('No target label was specified.')
# Dataset preparation. Postprocessing is required for the regression task.
def postprocess_label(label_list):
return numpy.asarray(label_list, dtype=numpy.float32)
# Apply a preprocessor to the dataset.
print('Preprocessing dataset...')
preprocessor = preprocess_method_dict[args.method]()
parser = CSVFileParser(preprocessor,
postprocess_label=postprocess_label,
labels=labels,
smiles_col='SMILES')
dataset = parser.parse(args.datafile)['dataset']
# Scale the label values, if necessary.
if args.scale == 'standardize':
scaler = StandardScaler()
scaler.fit(dataset.get_datasets()[-1])
else:
scaler = None
# Split the dataset into training and validation.
train_data_size = int(len(dataset) * args.train_data_ratio)
train, _ = split_dataset_random(dataset, train_data_size, args.seed)
# Set up the predictor.
predictor = set_up_predictor(args.method,
args.unit_num,
args.conv_layers,
class_num,
label_scaler=scaler)
# Set up the regressor.
device = chainer.get_device(args.device)
metrics_fun = {'mae': F.mean_absolute_error, 'rmse': rmse}
regressor = Regressor(predictor,
lossfun=F.mean_squared_error,
metrics_fun=metrics_fun,
device=device)
print('Training...')
converter = converter_method_dict[args.method]
run_train(regressor,
train,
valid=None,
batch_size=args.batchsize,
epoch=args.epoch,
out=args.out,
extensions_list=None,
device=device,
converter=converter,
resume_path=None)
# Save the regressor's parameters.
model_path = os.path.join(args.out, args.model_filename)
print('Saving the trained model to {}...'.format(model_path))
# TODO(nakago): ChainerX array cannot be sent to numpy array when internal
# state has gradients.
if hasattr(regressor.predictor.graph_conv, 'reset_state'):
regressor.predictor.graph_conv.reset_state()
regressor.save_pickle(model_path, protocol=args.protocol)
def main():
# Parse the arguments.
args = parse_arguments()
if args.label:
labels = args.label
class_num = len(labels) if isinstance(labels, list) else 1
else:
raise ValueError('No target label was specified.')
# Dataset preparation. Postprocessing is required for the regression task.
def postprocess_label(label_list):
label_arr = np.asarray(label_list, dtype=np.int32)
return label_arr
# Apply a preprocessor to the dataset.
print('Preprocessing dataset...')
preprocessor = preprocess_method_dict[args.method]()
parser = CSVFileParserForPair(preprocessor,
postprocess_label=postprocess_label,
labels=labels,
smiles_cols=['smiles_1', 'smiles_2'])
dataset = parser.parse(args.datafile)['dataset']
# Split the dataset into training and validation.
train_data_size = int(len(dataset) * args.train_data_ratio)
train, val = split_dataset_random(dataset, train_data_size, args.seed)
# Set up the predictor.
predictor = set_up_predictor(args.method, args.unit_num, args.conv_layers,
class_num)
# Set up the iterator.
train_iter = SerialIterator(train, args.batchsize)
val_iter = SerialIterator(val, args.batchsize, repeat=False, shuffle=False)
# Set up the regressor.
metric_fun = {
'accuracy': F.accuracy,
# 'precision': F.precision,
# 'recall': F.recall,
# 'F1-score': F.f1_score,
}
classifier = Classifier(predictor,
lossfun=F.sigmoid_cross_entropy,
metrics_fun=F.accuracy,
device=args.gpu)
# Set up the optimizer.
optimizer = optimizers.Adam()
optimizer.setup(classifier)
# Set up the updater.
updater = training.StandardUpdater(train_iter,
optimizer,
device=args.gpu,
converter=concat_mols)
# updater = training.ParallelUpdater(train_iter, optimizer, devices={'main': 0, 'second': 1},
# converter=concat_mols)
# Set up the trainer.
print('Training...')
trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=args.out)
trainer.extend(
E.Evaluator(val_iter,
classifier,
device=args.gpu,
converter=concat_mols))
train_eval_iter = SerialIterator(train,
args.batchsize,
repeat=False,
shuffle=False)
trainer.extend(
ROCAUCEvaluator(train_eval_iter,
classifier,
eval_func=predictor,
device=args.gpu,
converter=concat_mols,
name='train_roc',
pos_labels=1,
ignore_labels=-1,
raise_value_error=False))
# extension name='validation' is already used by `Evaluator`,
# instead extension name `val` is used.
trainer.extend(
ROCAUCEvaluator(val_iter,
classifier,
eval_func=predictor,
device=args.gpu,
converter=concat_mols,
name='val_roc',
pos_labels=1,
ignore_labels=-1))
trainer.extend(
PRCAUCEvaluator(train_eval_iter,
classifier,
eval_func=predictor,
device=args.gpu,
converter=concat_mols,
name='train_prc',
pos_labels=1,
ignore_labels=-1,
raise_value_error=False))
# extension name='validation' is already used by `Evaluator`,
# instead extension name `val` is used.
trainer.extend(
PRCAUCEvaluator(val_iter,
classifier,
eval_func=predictor,
device=args.gpu,
converter=concat_mols,
name='val_prc',
pos_labels=1,
ignore_labels=-1))
trainer.extend(
E.PrintReport([
'epoch',
'main/loss',
'main/accuracy',
# 'train_roc/main/roc_auc', 'train_prc/main/prc_auc',
'validation/main/loss',
'validation/main/accuracy',
# 'val_roc/main/roc_auc', 'val_prc/main/prc_auc',
'elapsed_time'
]))
trainer.extend(E.snapshot(), trigger=(args.epoch, 'epoch'))
trainer.extend(E.LogReport())
trainer.extend(E.ProgressBar())
trainer.run()
# Save the regressor's parameters.
model_path = os.path.join(args.out, args.model_filename)
print('Saving the trained models to {}...'.format(model_path))
classifier.save_pickle(model_path, protocol=args.protocol)
print(ans)
imagedata = Image.open(imgname[i])
imgarr = [np.asarray(imagedata)]
arr.append(np.asarray(imgarr))
arr = np.asarray(arr)
pictureArray = arr.reshape(imglen, 1, 795, 492).astype(np.float32)
pictureArray /= pictureArray.max()
npAnserArray = np.asarray(anserArray)
npAnserArray = npAnserArray.reshape(imglen).astype(np.int32)
#%%
train, test = datasets.split_dataset_random(
chainer.datasets.TupleDataset(pictureArray, npAnserArray), 700)
#%%
train_iter = iterators.SerialIterator(train, 50)
test_iter = iterators.SerialIterator(test, 1, repeat=False, shuffle=False)
#%%
class MLP(Chain):
def __init__(self):
super(MLP, self).__init__(
conv1=F.Convolution2D(1, 30, 5,
stride=1), # 入力3 枚、出力30枚、フィルタサイズ5ピクセル
conv2=F.Convolution2D(30, 30, 6,
stride=1), # 入力30>枚、出力30枚、フィルタサイズ5ピクセル
conv3=F.Convolution2D(30, 30, 5,
stride=1), # 入力30>枚、出力30枚、フィルタサイズ6ピクセル
from chainer.training import extensions
import numpy as np
import matplotlib
matplotlib.use('Agg')
mushroomsfile = 'mushrooms.csv'
data_array = np.genfromtxt(
mushroomsfile, delimiter=',', dtype=str, skip_header=1)
for col in range(data_array.shape[1]):
data_array[:, col] = np.unique(data_array[:, col], return_inverse=True)[1]
X = data_array[:, 1:].astype(np.float32)
Y = data_array[:, 0].astype(np.int32)[:, None]
train, test = datasets.split_dataset_random(
datasets.TupleDataset(X, Y), int(data_array.shape[0] * .7))
train_iter = ch.iterators.SerialIterator(train, 100)
test_iter = ch.iterators.SerialIterator(
test, 100, repeat=False, shuffle=False)
# Network definition
def MLP(n_units, n_out):
layer = ch.Sequential(L.Linear(n_units), F.relu)
model = layer.repeat(2)
model.append(L.Linear(n_out))
return model
def split_train(train_val, num=50000, seed=0):
train, valid = split_dataset_random(train_val, num, seed)
return train, valid
from chainer.datasets import mnist from chainer.datasets import split_dataset_random from network import network from chainer import training from chainer import optimizers from chainer import iterators from chainer.training import extensions import chainer.links as L import numpy as np # setup mnist dataset(example) train_val, test = mnist.get_mnist(withlabel=True, ndim=1) train, valid = split_dataset_random(train_val, round(len(train_val) * 0.8)) # training params batchsize = 64 # setup iterators train_iter = iterators.SerialIterator(train, batchsize) valid_iter = iterators.SerialIterator(valid, batchsize, False, False) test_iter = iterators.SerialIterator(test, batchsize, False, False) # gpu params gpu_id = 0 # defining updater net = L.Classifier(network()) # optimizer optimizer = optimizers.SGD(lr=0.01).setup(net)
def main():
# Parse the arguments.
args = parse_arguments()
# Set up some useful variables that will be used later on.
method = args.method
if args.label:
labels = args.label
cache_dir = os.path.join('input', '{}_{}'.format(method, labels))
else:
labels = D.get_qm9_label_names()
cache_dir = os.path.join('input', '{}_all'.format(method))
# Get the filename corresponding to the cached dataset, based on the amount
# of data samples that need to be parsed from the original dataset.
num_data = args.num_data
if num_data >= 0:
dataset_filename = 'data_{}.npz'.format(num_data)
else:
dataset_filename = 'data.npz'
# Load the cached dataset.
dataset_cache_path = os.path.join(cache_dir, dataset_filename)
dataset = None
if os.path.exists(dataset_cache_path):
print('Loading cached data from {}.'.format(dataset_cache_path))
dataset = NumpyTupleDataset.load(dataset_cache_path)
if dataset is None:
print('Preprocessing dataset...')
preprocessor = preprocess_method_dict[method]()
dataset = D.get_qm9(preprocessor, labels=labels)
# Cache the newly preprocessed dataset.
if not os.path.exists(cache_dir):
os.mkdir(cache_dir)
NumpyTupleDataset.save(dataset_cache_path, dataset)
# Load the standard scaler parameters, if necessary.
if args.scale == 'standardize':
scaler_path = os.path.join(args.in_dir, 'scaler.pkl')
print('Loading scaler parameters from {}.'.format(scaler_path))
with open(scaler_path, mode='rb') as f:
scaler = pickle.load(f)
else:
print('No standard scaling was selected.')
scaler = None
# Split the dataset into training and testing.
train_data_size = int(len(dataset) * args.train_data_ratio)
_, test = split_dataset_random(dataset, train_data_size, args.seed)
# Use a predictor with scaled output labels.
model_path = os.path.join(args.in_dir, args.model_filename)
regressor = Regressor.load_pickle(model_path, device=args.gpu)
# Replace the default predictor with one that scales the output labels.
scaled_predictor = ScaledGraphConvPredictor(regressor.predictor)
scaled_predictor.scaler = scaler
regressor.predictor = scaled_predictor
# This callback function extracts only the inputs and discards the labels.
def extract_inputs(batch, device=None):
return concat_mols(batch, device=device)[:-1]
# Predict the output labels.
print('Predicting...')
y_pred = regressor.predict(test, converter=extract_inputs)
# Extract the ground-truth labels.
t = concat_mols(test, device=-1)[-1]
n_eval = 10
# Construct dataframe.
df_dict = {}
for i, l in enumerate(labels):
df_dict.update({
'y_pred_{}'.format(l): y_pred[:, i],
't_{}'.format(l): t[:, i],
})
df = pandas.DataFrame(df_dict)
# Show a prediction/ground truth table with 5 random examples.
print(df.sample(5))
for target_label in range(y_pred.shape[1]):
diff = y_pred[:n_eval, target_label] - t[:n_eval, target_label]
print('target_label = {}, y_pred = {}, t = {}, diff = {}'.format(
target_label, y_pred[:n_eval, target_label],
t[:n_eval, target_label], diff))
# Run an evaluator on the test dataset.
print('Evaluating...')
test_iterator = SerialIterator(test, 16, repeat=False, shuffle=False)
eval_result = Evaluator(test_iterator,
regressor,
converter=concat_mols,
device=args.gpu)()
print('Evaluation result: ', eval_result)
# Save the evaluation results.
with open(os.path.join(args.in_dir, 'eval_result.json'), 'w') as f:
json.dump(eval_result, f)
def main():
parser = argparse.ArgumentParser(description='Chanier example: MNIST')
parser.add_argument('--batchsize',
'-b',
type=int,
default=32,
help='Number of sweeps over the dataset to train')
parser.add_argument('--gpu',
'-g',
type=int,
default=-1,
help='GPU ID (negative value indicates CPU)')
parser.add_argument('--epoch',
'-e',
type=int,
default=40,
help='Number of sweeps over the datasdt to train')
parser.add_argument('--out',
'-o',
default='result',
help='Directory to output the result')
parser.add_argument('--resume',
'-r',
default='',
help='Resume the training from snapshot')
parser.add_argument('--unit',
'-u',
type=int,
default=1000,
help='Number of units')
args = parser.parse_args()
print('GPU: {}'.format(args.gpu))
print('# unit: {}'.format(args.unit))
print('# Minibatch-size: {}'.format(args.batchsize))
print('# epoch: {}'.format(args.epoch))
print('')
dataset_gomi = []
preprocessing(dataset_gomi, "sample_images_can1/pic", 10, 1)
preprocessing(dataset_gomi, "sample_images_can2/pic", 10, 1)
preprocessing(dataset_gomi, "sample_images_can3/pic", 20, 1)
preprocessing(dataset_gomi, "sample_images_can4/pic", 10, 1)
preprocessing(dataset_gomi, "sample_images_can5/pic", 10, 1)
preprocessing(dataset_gomi, "sample_images_bin1/pic", 10, 2)
preprocessing(dataset_gomi, "sample_images_bin2/pic", 10, 2)
preprocessing(dataset_gomi, "sample_images_bin3/pic", 20, 2)
preprocessing(dataset_gomi, "sample_images_pet1/pic", 10, 3)
preprocessing(dataset_gomi, "sample_images_pet2/pic", 10, 3)
preprocessing(dataset_gomi, "sample_images_pet3/pic", 10, 3)
preprocessing(dataset_gomi, "sample_images_pet4/pic", 10, 3)
train, test = split_dataset_random(dataset_gomi, 120, seed=0)
model = L.Classifier(MLP(), lossfun=F.softmax_cross_entropy)
if args.gpu >= 0:
chainer.cuda.get_device(args.gpu).user()
model.to_gpu()
optimizer = chainer.optimizers.Adam()
optimizer.setup(model)
train_iter = chainer.iterators.SerialIterator(train, args.batchsize)
test_iter = chainer.iterators.SerialIterator(test,
args.batchsize,
repeat=False,
shuffle=False)
updater = training.StandardUpdater(train_iter, optimizer, device=args.gpu)
trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=args.out)
trainer.extend(extensions.Evaluator(test_iter, model, device=args.gpu))
trainer.extend(extensions.dump_graph('main/loss'))
trainer.extend(extensions.snapshot(), trigger=(args.epoch, 'epoch'))
#trainer.extend(extensions.PlotReport(['main/loss','validation/main/accuracy'],'epoch',file_name='loss.png'))
trainer.extend(
extensions.PlotReport(['main/accuracy', 'validation/main/accuracy'],
'epoch',
file_name='accuracy.png'))
trainer.extend(
extensions.PrintReport([
'epoch', 'main/loss', 'validation/main/loss', 'main/accuracy',
'validation/main/accuracy', 'elapsed_time'
]))
trainer.extend(extensions.ProgressBar())
trainer.extend(extensions.LogReport())
if args.resume:
chainer.serializers.load_npz(args.resume, trainer)
trainer.run()
model.to_cpu()
modelname = args.out + "/MLP.model"
print('same the trained model: {}'.format(modelname))
chainer.serializers.save_npz("model.npz", model)
def main():
# Parse the arguments.
args = parse_arguments()
if args.label:
labels = args.label
class_num = len(labels) if isinstance(labels, list) else 1
else:
raise ValueError('No target label was specified.')
# Dataset preparation. Postprocessing is required for the regression task.
def postprocess_label(label_list):
return numpy.asarray(label_list, dtype=numpy.float32)
# Apply a preprocessor to the dataset.
print('Preprocessing dataset...')
preprocessor = preprocess_method_dict[args.method]()
parser = CSVFileParser(preprocessor, postprocess_label=postprocess_label,
labels=labels, smiles_col='SMILES')
dataset = parser.parse(args.datafile)['dataset']
# Scale the label values, if necessary.
if args.scale == 'standardize':
scaler = StandardScaler()
labels = scaler.fit_transform(dataset.get_datasets()[-1])
dataset = NumpyTupleDataset(*(dataset.get_datasets()[:-1] + (labels,)))
else:
scaler = None
# Split the dataset into training and validation.
train_data_size = int(len(dataset) * args.train_data_ratio)
train, _ = split_dataset_random(dataset, train_data_size, args.seed)
# Set up the predictor.
predictor = set_up_predictor(args.method, args.unit_num,
args.conv_layers, class_num)
# Set up the iterator.
train_iter = SerialIterator(train, args.batchsize)
# Set up the regressor.
metrics_fun = {'mean_abs_error': MeanAbsError(scaler=scaler),
'root_mean_sqr_error': RootMeanSqrError(scaler=scaler)}
regressor = Regressor(predictor, lossfun=F.mean_squared_error,
metrics_fun=metrics_fun, device=args.gpu)
# Set up the optimizer.
optimizer = optimizers.Adam()
optimizer.setup(regressor)
# Set up the updater.
updater = training.StandardUpdater(train_iter, optimizer, device=args.gpu,
converter=concat_mols)
# Set up the trainer.
print('Training...')
trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=args.out)
trainer.extend(E.snapshot(), trigger=(args.epoch, 'epoch'))
trainer.extend(E.LogReport())
trainer.extend(E.PrintReport(['epoch', 'main/loss', 'main/mean_abs_error',
'main/root_mean_sqr_error', 'elapsed_time']))
trainer.extend(E.ProgressBar())
trainer.run()
# Save the regressor's parameters.
model_path = os.path.join(args.out, args.model_filename)
print('Saving the trained model to {}...'.format(model_path))
regressor.save_pickle(model_path, protocol=args.protocol)
# Save the standard scaler's parameters.
if scaler is not None:
with open(os.path.join(args.out, 'scaler.pkl'), mode='wb') as f:
pickle.dump(scaler, f, protocol=args.protocol)
# In[]
batchsize = 256
learnrate = 0.05
epoch = 300
gpu = 0
out = 'result'
resume = ''
# In[]
class_labels = 5
train_val, test = make_datasets()
# model = L.Classifier(VGG16Net.VGG16Net(class_labels))
train_size = int(len(train_val) * 0.9)
train, valid = split_dataset_random(train_val, train_size, seed=0)
model = L.Classifier(VGG_chainer.VGG(class_labels))
#GPUのセットアップ
if gpu >= 0:
# Make a specified GPU current
chainer.backends.cuda.get_device_from_id(gpu).use()
model.to_gpu(gpu) # Copy the model to the GPU
xp = cuda.cupy
optimizer = chainer.optimizers.MomentumSGD(lr=learnrate).setup(model)
optimizer.add_hook(chainer.optimizer.WeightDecay(0.0005))
# In[]
train_iter = chainer.iterators.SerialIterator(train, batchsize)
valid_iter = chainer.iterators.SerialIterator(valid,
batchsize,
def main():
parser = argparse.ArgumentParser(description="Vanilla_AE")
parser.add_argument("--batchsize", "-b", type=int, default=64)
parser.add_argument("--epoch", "-e", type=int, default=100)
parser.add_argument("--gpu", "-g", type=int, default=0)
parser.add_argument("--snapshot", "-s", type=int, default=10)
parser.add_argument("--n_dimz", "-z", type=int, default=64)
parser.add_argument("--dataset", "-d", type=str, default='mnist')
args = parser.parse_args()
#import program
import Updater
import Evaluator
#print settings
print("GPU:{}".format(args.gpu))
print("epoch:{}".format(args.epoch))
print("Minibatch_size:{}".format(args.batchsize))
print('')
batchsize = args.batchsize
gpu_id = args.gpu
max_epoch = args.epoch
if args.dataset == "mnist":
train_val, test = mnist.get_mnist(withlabel=False, ndim=3, scale=255.)
import Network.mnist_net as Network
else:
train_val, test = chainer.datasets.get_cifar10(withlabel=False,
scale=255.)
import Network.cifar10_net as Network
train, valid = split_dataset_random(train_val, 50000, seed=0)
model = Network.AE(n_dimz=args.n_dimz)
#set iterator
train_iter = iterators.SerialIterator(train, batchsize)
valid_iter = iterators.SerialIterator(valid,
batchsize,
repeat=False,
shuffle=False)
#optimizer
def make_optimizer(model, alpha=0.0002, beta1=0.5):
optimizer = optimizers.Adam(alpha=alpha, beta1=beta1)
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer.WeightDecay(0.0001))
return optimizer
opt = make_optimizer(model)
#trainer
updater = Updater.AEUpdater(model=model,
iterator=train_iter,
optimizer=opt,
device=args.gpu)
trainer = training.Trainer(updater, (max_epoch, 'epoch'), out='result')
#trainer.extend(extensions.ExponentialShift('lr', 0.5),trigger=(30, 'epoch'))
trainer.extend(extensions.LogReport(log_name='log'))
trainer.extend(
Evaluator.AEEvaluator(iterator=valid_iter,
target=model,
device=args.gpu))
trainer.extend(extensions.snapshot_object(
model, filename='model_snapshot_epoch_{.updater.epoch}.npz'),
trigger=(args.snapshot, 'epoch'))
#trainer.extend(extensions.snapshot_object(optimizer, filename='optimizer_snapshot_epoch_{.updater.epoch}'), trigger=(args.snapshot, 'epoch'))
trainer.extend(
extensions.PrintReport(['epoch', 'main/loss', 'validation/main/loss']))
trainer.extend(extensions.ProgressBar())
trainer.run()
del trainer
def iris_nn():
"""Untitled1.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1VP7qclm8kv1T7PDDy7l2cW1IjjnlUBCr
"""
epochnum = 1000
from sklearn.datasets import load_iris
iris = load_iris()
# print(iris)
# print(len(iris.data))
# print(len(iris.target))
from chainer import functions as F
from chainer import links as L
import chainer
from chainer.datasets import split_dataset_random
class MyNN(chainer.Chain):
def __init__(self, output_label):
super(MyNN, self).__init__()
weight_initializer = chainer.initializers.Normal(scale=0.08,
dtype=None)
with self.init_scope():
self.l1 = L.Linear(4, 128)
self.l2 = L.Linear(None, 128)
self.l3 = L.Linear(None, 128)
self.l4 = L.Linear(None, 256)
self.l5 = L.Linear(None, 512)
self.fc1 = L.Linear(None, 256)
self.fc2 = L.Linear(None, output_label)
def __call__(self, x, t=None, train=True):
h1 = F.relu(self.l1(x))
h2 = F.relu(self.l2(h1))
h3 = F.relu(h2)
h4 = F.dropout(h3)
h6 = self.fc1(h4)
h7 = self.fc2(h6)
return h7 # y if train else F.softmax(h5)
model = MyNN(3)
model = L.Classifier(model)
optimizer = chainer.optimizers.SGD()
optimizer.setup(model)
iris["data"] = iris["data"].astype(np.float32)
data_and_labels = list(zip(iris["data"], iris["target"]))
np.random.shuffle(data_and_labels)
threshold = 0.7
l = len(data_and_labels)
lth = int(l * threshold)
print(lth)
print("************")
data = [d[0] for d in data_and_labels]
labels = [d[1] for d in data_and_labels]
"""
classnum = np.max(labels)
tmplabels = np.zeros((len(labels), classnum + 1), dtype=np.int)
for i, j in zip(tmplabels, labels):
i[j] = 1
print(tmplabels)
labels = tmplabels
"""
train = chainer.datasets.tuple_dataset.TupleDataset(
data[:lth], labels[:lth])
test = chainer.datasets.tuple_dataset.TupleDataset(data[lth:],
labels[lth:])
split_at = int(len(data) * 0.7)
train, test = split_dataset_random(
chainer.datasets.tuple_dataset.TupleDataset(data, labels), split_at)
batch_size = 16
test_batch_size = 32
train_iter = chainer.iterators.SerialIterator(train,
batch_size,
shuffle=True)
test_iter = chainer.iterators.SerialIterator(test,
test_batch_size,
repeat=False,
shuffle=False)
from chainer import training, datasets, iterators, optimizers
from chainer.training import extensions
from chainer.training.extensions import LogReport
from chainer.backends import cuda
updater = training.StandardUpdater(train_iter, optimizer)
trainer = training.Trainer(updater, (epochnum, 'epoch'), out='result')
trainer.extend(extensions.Evaluator(test_iter, model), name='validation')
trainer.extend(extensions.LogReport())
trainer.extend(
extensions.PrintReport([
'epoch', 'main/loss', 'validation/main/loss', 'main/accuracy',
'validation/main/accuracy', 'elapsed_time'
]))
# trainer.extend(extensions.PrintReport(['epoch', 'val/main/loss']))
trainer.extend(extensions.ProgressBar())
trainer.run()
def main():
# Supported preprocessing/network list
method_list = ['nfp', 'ggnn', 'schnet', 'weavenet', 'rsgcn']
label_names = [
'A', 'B', 'C', 'mu', 'alpha', 'h**o', 'lumo', 'gap', 'r2', 'zpve',
'U0', 'U', 'H', 'G', 'Cv'
]
scale_list = ['standardize', 'none']
parser = argparse.ArgumentParser(description='Regression with QM9.')
parser.add_argument('--method',
'-m',
type=str,
choices=method_list,
default='nfp')
parser.add_argument('--label',
'-l',
type=str,
choices=label_names,
default='',
help='target label for regression, '
'empty string means to predict all '
'property at once')
parser.add_argument('--scale',
type=str,
choices=scale_list,
default='standardize',
help='Label scaling method')
parser.add_argument('--conv-layers', '-c', type=int, default=4)
parser.add_argument('--batchsize', '-b', type=int, default=32)
parser.add_argument('--gpu', '-g', type=int, default=-1)
parser.add_argument('--out', '-o', type=str, default='result')
parser.add_argument('--epoch', '-e', type=int, default=20)
parser.add_argument('--unit-num', '-u', type=int, default=16)
parser.add_argument('--seed', '-s', type=int, default=777)
parser.add_argument('--train-data-ratio', '-t', type=float, default=0.7)
parser.add_argument('--protocol', type=int, default=2)
parser.add_argument('--model-filename', type=str, default='regressor.pkl')
parser.add_argument('--num-data',
type=int,
default=-1,
help='Number of data to be parsed from parser.'
'-1 indicates to parse all data.')
args = parser.parse_args()
seed = args.seed
train_data_ratio = args.train_data_ratio
method = args.method
if args.label:
labels = args.label
cache_dir = os.path.join('input', '{}_{}'.format(method, labels))
class_num = len(labels) if isinstance(labels, list) else 1
else:
labels = None
cache_dir = os.path.join('input', '{}_all'.format(method))
class_num = len(D.get_qm9_label_names())
# Dataset preparation
dataset = None
num_data = args.num_data
if num_data >= 0:
dataset_filename = 'data_{}.npz'.format(num_data)
else:
dataset_filename = 'data.npz'
dataset_cache_path = os.path.join(cache_dir, dataset_filename)
if os.path.exists(dataset_cache_path):
print('load from cache {}'.format(dataset_cache_path))
dataset = NumpyTupleDataset.load(dataset_cache_path)
if dataset is None:
print('preprocessing dataset...')
preprocessor = preprocess_method_dict[method]()
if num_data >= 0:
# only use first 100 for debug
target_index = numpy.arange(num_data)
dataset = D.get_qm9(preprocessor,
labels=labels,
target_index=target_index)
else:
dataset = D.get_qm9(preprocessor, labels=labels)
os.makedirs(cache_dir)
NumpyTupleDataset.save(dataset_cache_path, dataset)
if args.scale == 'standardize':
# Standard Scaler for labels
ss = StandardScaler()
labels = ss.fit_transform(dataset.get_datasets()[-1])
else:
ss = None
dataset = NumpyTupleDataset(*(dataset.get_datasets()[:-1] + (labels, )))
train_data_size = int(len(dataset) * train_data_ratio)
train, val = split_dataset_random(dataset, train_data_size, seed)
# Network
n_unit = args.unit_num
conv_layers = args.conv_layers
if method == 'nfp':
print('Train NFP model...')
model = GraphConvPredictor(
NFP(out_dim=n_unit, hidden_dim=n_unit, n_layers=conv_layers),
MLP(out_dim=class_num, hidden_dim=n_unit))
elif method == 'ggnn':
print('Train GGNN model...')
model = GraphConvPredictor(
GGNN(out_dim=n_unit, hidden_dim=n_unit, n_layers=conv_layers),
MLP(out_dim=class_num, hidden_dim=n_unit))
elif method == 'schnet':
print('Train SchNet model...')
model = GraphConvPredictor(
SchNet(out_dim=class_num, hidden_dim=n_unit, n_layers=conv_layers),
None)
elif method == 'weavenet':
print('Train WeaveNet model...')
n_atom = 20
n_sub_layer = 1
weave_channels = [50] * conv_layers
model = GraphConvPredictor(
WeaveNet(weave_channels=weave_channels,
hidden_dim=n_unit,
n_sub_layer=n_sub_layer,
n_atom=n_atom), MLP(out_dim=class_num, hidden_dim=n_unit))
elif method == 'rsgcn':
print('Train RSGCN model...')
model = GraphConvPredictor(
RSGCN(out_dim=n_unit, hidden_dim=n_unit, n_layers=conv_layers),
MLP(out_dim=class_num, hidden_dim=n_unit))
else:
raise ValueError('[ERROR] Invalid method {}'.format(method))
train_iter = I.SerialIterator(train, args.batchsize)
val_iter = I.SerialIterator(val,
args.batchsize,
repeat=False,
shuffle=False)
regressor = Regressor(
model,
lossfun=F.mean_squared_error,
metrics_fun={'abs_error': ScaledAbsError(scale=args.scale, ss=ss)},
device=args.gpu)
optimizer = O.Adam()
optimizer.setup(regressor)
updater = training.StandardUpdater(train_iter,
optimizer,
device=args.gpu,
converter=concat_mols)
trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=args.out)
trainer.extend(
E.Evaluator(val_iter,
regressor,
device=args.gpu,
converter=concat_mols))
trainer.extend(E.snapshot(), trigger=(args.epoch, 'epoch'))
trainer.extend(E.LogReport())
trainer.extend(
E.PrintReport([
'epoch', 'main/loss', 'main/abs_error', 'validation/main/loss',
'validation/main/abs_error', 'elapsed_time'
]))
trainer.extend(E.ProgressBar())
trainer.run()
# --- save regressor & standardscaler ---
protocol = args.protocol
regressor.save_pickle(os.path.join(args.out, args.model_filename),
protocol=protocol)
if args.scale == 'standardize':
with open(os.path.join(args.out, 'ss.pkl'), mode='wb') as f:
pickle.dump(ss, f, protocol=protocol)
def main():
set_random_seed(0)
parser = argparse.ArgumentParser(
description='Document Classification Example')
parser.add_argument('--batchsize',
'-b',
type=int,
default=64,
help='Number of documents in each mini-batch')
parser.add_argument('--epoch',
'-e',
type=int,
default=30,
help='Number of training epochs')
parser.add_argument('--gpu',
'-g',
type=int,
default=-1,
help='GPU ID (negative value indicates CPU)')
parser.add_argument('--out',
'-o',
default='result',
help='Directory to output the result')
parser.add_argument('--unit',
'-u',
type=int,
default=200,
help='Number of units')
parser.add_argument('--vocab',
'-v',
type=int,
default=50000,
help='Vocabulary size')
parser.add_argument('--layer',
'-l',
type=int,
default=1,
help='Number of layers of LSMT')
parser.add_argument('--dropout',
'-d',
type=float,
default=0.4,
help='Dropout rate')
parser.add_argument('--gradclip',
type=float,
default=5,
help='Gradient clipping threshold')
parser.add_argument('--train_file',
'-train',
default='data/train.seg.csv',
help='Trainig data file.')
parser.add_argument('--test_file',
'-test',
default='data/test.seg.csv',
help='Test data file.')
parser.add_argument('--model',
'-m',
help='read model parameters from npz file')
parser.add_argument(
'--vcb_file',
'-vf',
default=
'/mnt/gold/users/s18153/prjPyCharm/prjNLP_GPU/data/vocab_train_w_NoReplace.vocab_file',
help='Vocabulary data file.')
parser.add_argument('--case',
'-c',
default='original',
help='Select NN Architecture.')
parser.add_argument('--opt', default='sgd', help='Select Optimizer.')
parser.add_argument('--dbg_on',
action='store_true',
help='No save, MiniTrain')
args = parser.parse_args()
print(args)
# train_val = data.DocDataset(args.train_file, vocab_size=args.vocab)
if os.path.exists(args.vcb_file): # args.vocab_fileの存在確認(作成済みの場合ロード)
with open(args.vcb_file, 'rb') as f_vocab_data:
train_val = pickle.load(f_vocab_data)
if len(train_val.get_vocab()) != args.vocab:
warnings.warn('vocab size incorrect (not implemented...)')
else:
train_val = data.DocDataset(
args.train_file,
vocab_size=args.vocab) # make vocab from training data
with open(args.vcb_file, 'wb') as f_vocab_save:
pickle.dump(train_val, f_vocab_save)
if args.dbg_on:
len_train_data = len(train_val)
N = 1000
print('N', N)
rnd_ind = np.random.permutation(range(len_train_data))[:N]
train_val = train_val[rnd_ind]
(train, valid) = split_dataset_random(train_val, 800, seed=0)
else:
(train, valid) = split_dataset_random(train_val, 4000, seed=0)
train_iter = iterators.SerialIterator(train, args.batchsize)
valid_iter = iterators.SerialIterator(valid,
args.batchsize,
repeat=False,
shuffle=False)
# test = data.DocDataset(args.test_file, train_val.get_vocab())
# test_iter = iterators.SerialIterator(test, args.batchsize, repeat=False, shuffle=False)
print('case', args.case)
if args.case == 'original':
print('originalで実行されます')
result_path = 'result/original'
model = L.Classifier(
nets_B.DocClassify(n_vocab=args.vocab + 1,
n_units=args.unit,
n_layers=args.layer,
n_out=4,
dropout=args.dropout))
elif args.case == 'bi':
print('biで実行されます')
result_path = 'result/bi'
model = L.Classifier(
nets_B.DocClassifyBi(n_vocab=args.vocab + 1,
n_units=args.unit,
n_layers=args.layer,
n_out=4,
dropout=args.dropout))
elif args.case == 'bi2' or args.case == 'bi_adam_2layer':
print('bi改良版')
result_path = 'result/bi2'
model = L.Classifier(
nets_B.DocClassifyBi2(n_vocab=args.vocab + 1,
n_units=args.unit,
n_layers=args.layer,
n_out=4,
dropout=args.dropout))
else:
warnings.warn('指定したケースは存在しません。デフォルトで実行します')
result_path = 'result/sample_result'
model = L.Classifier(
nets_B.DocClassify(n_vocab=args.vocab + 1,
n_units=args.unit,
n_layers=args.layer,
n_out=4,
dropout=args.dropout))
if args.gpu >= 0:
chainer.cuda.get_device_from_id(args.gpu).use()
# get_device_from_id(args.gpu).use()
model.to_gpu()
if args.opt == 'sgd':
result_path += '_sgd'
print('SGD')
optimizer = optimizers.SGD(lr=0.01)
elif args.opt == 'adam':
result_path += '_adam'
print('Adam')
optimizer = optimizers.Adam()
elif args.opt == 'bi_adam_2layer':
result_path += '_adam_2layer'
print('Adam')
optimizer = optimizers.Adam()
else:
print('指定なしのためSGDで実行')
optimizer = optimizers.SGD(lr=0.01)
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer.GradientClipping(args.gradclip))
# optimizer.add_hook(chainer.optimizer.Lasso(0.01))
updater = training.StandardUpdater(train_iter,
optimizer,
converter=convert_seq,
device=args.gpu)
trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=result_path)
trainer.extend(extensions.LogReport())
if not args.dbg_on:
trainer.extend(
extensions.snapshot(filename='snapshot_epoch-{.updater.epoch}'))
trainer.extend(extensions.Evaluator(valid_iter,
model,
converter=convert_seq,
device=args.gpu),
name='val')
trainer.extend(
extensions.PrintReport([
'epoch', 'main/loss', 'main/accuracy', 'val/main/loss',
'val/main/accuracy', 'elapsed_time'
]))
trainer.extend(
extensions.ParameterStatistics(model.predictor.doc_enc,
{'std': np.std}))
trainer.extend(
extensions.PlotReport(['main/loss', 'val/main/loss'],
x_key='epoch',
file_name='loss.png'))
trainer.extend(
extensions.PlotReport(['main/accuracy', 'val/main/accuracy'],
x_key='epoch',
file_name='accuracy.png'))
trainer.extend(extensions.dump_graph('main/loss'))
if args.model:
serializers.load_npz(args.model, trainer)
trainer.run()
pass
def run_train(net, optimizer, dataset, save_dir, gpu_id):
n_batch = 64
n_epoch = 50
SAVE_MODEL_PER_ITER = 1000
# GPUに転送
if gpu_id is not None:
net.to_gpu(gpu_id)
# log
results_train, results_valid = {}, {}
results_train['loss'], results_train['accuracy'] = [], []
results_valid['loss'], results_valid['accuracy'] = [], []
# 入力データを分割
train_val, test_data = split_dataset_random(dataset,
int(len(dataset) * 0.8),
seed=0)
train, valid = split_dataset_random(train_val,
int(len(train_val) * 0.8),
seed=0)
# iteration数出力
print('# of epoch:', n_epoch)
print('# of batch:', n_batch)
print('# of train data:', len(train))
print('# of valid data:', len(valid))
print('# of iteration:', int(max(n_epoch,
n_epoch * len(train) / n_batch)), '\n')
# ぷよぷよAIを参考にbatch_sizeは64
train_iter = SerialIterator(train,
batch_size=n_batch,
repeat=True,
shuffle=True)
count = 0
for epoch in range(n_epoch):
while True:
# ミニバッチの取得
train_batch = train_iter.next()
# [(x1,t1), (x2, t2), ...]形式から，[[x1,x2,x3,...], [t1,t2,t3,...]]形式へ
x0_train, x1_train, t_train = concat_samples(train_batch, gpu_id)
# 予測値と目的関数の計算
y_train = net(x0_train, x1_train)
loss_train = F.softmax_cross_entropy(y_train, t_train)
acc_train = F.accuracy(y_train, t_train)
# 勾配の初期化と勾配の計算
net.cleargrads()
loss_train.backward()
# パラメータの更新
optimizer.update()
# iteration カウントアップ
count += 1
# SAVE_MODEL_PER_ITER iterationごとにモデルを保存
if count % SAVE_MODEL_PER_ITER == 0:
# 各epochのモデルの保存
save_filename = os.path.join(save_dir,
'net_{:03d}.npz'.format(count))
save_model(net, gpu_id, save_filename)
print('save model (iteration {}) to {}\n'.format(
count, save_filename))
# 1エポック終えたら、valid データで評価する
if train_iter.is_new_epoch or count % SAVE_MODEL_PER_ITER == 0:
# 検証用データに対する結果の確認
with chainer.using_config('train',
False), chainer.using_config(
'enable_backprop', False):
# x_valid, t_valid = chainer.dataset.concat_examples(valid, gpu_id)
x0_valid, x1_valid, t_valid = concat_samples(valid, gpu_id)
y_valid = net(x0_valid, x1_valid)
loss_valid = F.softmax_cross_entropy(y_valid, t_valid)
acc_valid = F.accuracy(y_valid, t_valid)
# 注意：GPU で計算した結果はGPU上に存在するため、CPU上に転送します
if gpu_id is not None:
loss_train.to_cpu()
loss_valid.to_cpu()
acc_train.to_cpu()
acc_valid.to_cpu()
# 結果の表示
print(
'epoch: {}, iteration: {}, loss (train): {:.4f}, loss (valid): {:.4f}\n'
'acc (train): {:.4f}, acc (valid): {:.4f}\n'.format(
epoch, count, loss_train.array.mean(),
loss_valid.array.mean(), acc_train.array.mean(),
acc_valid.array.mean()))
if train_iter.is_new_epoch:
# 可視化用に保存
results_train['loss'].append(loss_train.array)
results_train['accuracy'].append(acc_train.array)
results_valid['loss'].append(loss_valid.array)
results_valid['accuracy'].append(acc_valid.array)
break
# モデルの保存
save_filename = os.path.join(save_dir, 'net_final.npz')
save_model(net, gpu_id, save_filename)
print('save model to {} at {}\n'.format(count, save_filename))
# 損失 (loss)
plt.plot(results_train['loss'], label='train') # label で凡例の設定
plt.plot(results_valid['loss'], label='valid') # label で凡例の設定
plt.legend() # 凡例の表示
plt.savefig(os.path.join(save_dir, 'loss.png'))
plt.figure()
# 精度 (accuracy)
plt.plot(results_train['accuracy'], label='train') # label で凡例の設定
plt.plot(results_valid['accuracy'], label='valid') # label で凡例の設定
plt.legend() # 凡例の表示
plt.savefig(os.path.join(save_dir, 'accuracy.png'))
def get_datasets():
labels = pd.read_csv(LabelsPath, index_col=0)
# label: 1 -> 102
ds = datasets.LabeledImageDataset(list(zip(labels["image"], labels["label"] - 1)), PreProcessedFlowerImagesDirectory)
return datasets.split_dataset_random(ds, int(len(ds) * 0.8), seed=SplitDatasetSeed)
from chainer import datasets
from chainer import training
from chainer.training import extensions
import numpy as np
mushroomsfile = 'mushrooms.csv'
data_array = np.genfromtxt(
mushroomsfile, delimiter=',', dtype=str, skip_header=1)
for col in range(data_array.shape[1]):
data_array[:, col] = np.unique(data_array[:, col], return_inverse=True)[1]
X = data_array[:, 1:].astype(np.float32)
Y = data_array[:, 0].astype(np.int32)[:, None]
train, test = datasets.split_dataset_random(
datasets.TupleDataset(X, Y), int(data_array.shape[0] * .7))
train_iter = chainer.iterators.SerialIterator(train, 100)
test_iter = chainer.iterators.SerialIterator(
test, 100, repeat=False, shuffle=False)
# Network definition
class MLP(chainer.Chain):
def __init__(self, n_units, n_out):
super(MLP, self).__init__()
with self.init_scope():
# the input size to each layer inferred from the layer before
self.l1 = L.Linear(n_units) # n_in -> n_units
self.l2 = L.Linear(n_units) # n_units -> n_units
self.l3 = L.Linear(n_out) # n_units -> n_out
def TrainUNet(X,
Y,
model_=None,
optimizer_=None,
epoch=40,
alpha=0.001,
gpu_id=0,
loop=1,
earlystop=True):
assert (len(X) == len(Y))
d_time = datetime.datetime.now().strftime("%m-%d-%H-%M-%S")
# 1. Model load.
# print(sum(p.data.size for p in model.unet.params()))
if model_ is not None:
model = Regressor(model_)
print("## model loaded.")
else:
model = Regressor(UNet())
model.compute_accuracy = False
if gpu_id >= 0:
model.to_gpu(gpu_id)
# 2. optimizer load.
if optimizer_ is not None:
opt = optimizer_
print("## optimizer loaded.")
else:
opt = optimizers.Adam(alpha=alpha)
opt.setup(model)
# 3. Data Split.
dataset = Unet_DataSet(X, Y)
print("# number of patterns", len(dataset))
train, valid = \
split_dataset_random(dataset, int(len(dataset) * 0.8), seed=0)
# 4. Iterator
train_iter = SerialIterator(train, batch_size=C.BATCH_SIZE)
test_iter = SerialIterator(valid,
batch_size=C.BATCH_SIZE,
repeat=False,
shuffle=False)
# 5. config train, enable backprop
chainer.config.train = True
chainer.config.enable_backprop = True
# 6. UnetUpdater
updater = UnetUpdater(train_iter, opt, model, device=gpu_id)
# 7. EarlyStopping
if earlystop:
stop_trigger = triggers.EarlyStoppingTrigger(
monitor='validation/main/loss',
max_trigger=(epoch, 'epoch'),
patients=5)
else:
stop_trigger = (epoch, 'epoch')
# 8. Trainer
trainer = training.Trainer(updater, stop_trigger, out=C.PATH_TRAINRESULT)
# 8.1. UnetEvaluator
trainer.extend(UnetEvaluator(test_iter, model, device=gpu_id))
trainer.extend(SaveRestore(),
trigger=triggers.MinValueTrigger('validation/main/loss'))
# 8.2. Extensions LogReport
trainer.extend(extensions.LogReport())
# 8.3. Extension Snapshot
# trainer.extend(extensions.snapshot(filename='snapshot_epoch-{.updater.epoch}'))
# trainer.extend(extensions.snapshot_object(model.unet, filename='loop' + str(loop) + '.model'))
# 8.4. Print Report
trainer.extend(extensions.observe_lr())
trainer.extend(
extensions.PrintReport([
'epoch', 'main/loss', 'validation/main/loss', 'elapsed_time', 'lr'
]))
# 8.5. Extension Graph
trainer.extend(
extensions.PlotReport(['main/loss', 'validation/main/loss'],
x_key='epoch',
file_name='loop-' + str(loop) + '-loss' +
d_time + '.png'))
# trainer.extend(extensions.dump_graph('main/loss'))
# 8.6. Progree Bar
trainer.extend(extensions.ProgressBar())
# 9. Trainer run
trainer.run()
chainer.serializers.save_npz(C.PATH_TRAINRESULT / ('loop' + str(loop)),
model.unet)
return model.unet, opt
def main():
# オプション処理
parser = argparse.ArgumentParser(description='話者認識モデルの学習')
parser.add_argument('--batchsize', '-b', type=int, default=50,
help='Number of images in each mini-batch')
parser.add_argument('--epoch', '-e', type=int, default=20,
help='Number of sweeps over the dataset to train')
parser.add_argument('--gpu', '-g', type=int, default=-1,
help='GPU ID (negative value indicates CPU)')
parser.add_argument('--out', '-o', default='result',
help='Directory to output the result')
parser.add_argument('--datadir', '-d', default='train',
help='学習データのディレクトリ')
args = parser.parse_args()
sys.stderr.write('GPU: {}\n'.format(args.gpu))
sys.stderr.write('# minibatch-size: {}\n'.format(args.batchsize))
sys.stderr.write('# epoch: {}\n'.format(args.epoch))
trainf = []
label = 0
print('loading dataset')
mfcc = os.listdir(args.datadir)
for i in [f for f in mfcc if ('mfcc' in f)]:
trainf.append([os.path.join(args.datadir, i), label])
label += 1
#print('{}'.format(trainf))
input = []
target = []
if not os.path.exists(args.out):
os.mkdir(args.out)
log = open(args.out+"/class_log",'w')
for file in trainf:
print('{}'.format(file))
log.write('{},{}\n'.format(file[0].strip().split("/")[-1].split(".")[0],file[1]))
with open(file[0], 'r') as f:
lines = f.readlines()
for i, l in enumerate(lines):
#print('{}'.format(len(lines)))
tmp = []
flag = False
for j in range(3): # 3フレームで評価
if i + 2 < len(lines):
frame = lines[i+j].strip().split(" ")
#print("i:{},file:{}".format(i,file))
np.array(frame,dtype=np.float32)
tmp.extend(frame)
flag = True
#print('{}'.format(tmp))
if flag:
input.append(tmp)
target.append(file[1])
# print('{}'.format(input))
log.close()
#print('{}'.format(input))
#sys.exit()
#print(np.array(input))
input = np.array(input).astype(np.float32)
target = np.array(target).astype(np.int32)
#print('{},{}'.format(len(input), len(target)))
train = D.TupleDataset(input, target)
#sys.stderr.write(train)
#print(len(input)*0.9)
train, test = D.split_dataset_random(train, int(len(input)*0.9))
print('{},{}'.format(len(train), len(test)))
#sys.exit()
model = L.Classifier(DNN(label)) # CNNにする
if args.gpu >= 0:
chainer.cuda.get_device(args.gpu).use() # Make a specified GPU current
model.to_gpu() # Copy the model to the GPU
# Setup an optimizer
optimizer = chainer.optimizers.Adam()
optimizer.setup(model)
train_iter = chainer.iterators.SerialIterator(train, args.batchsize)
test_iter = chainer.iterators.SerialIterator(test, args.batchsize,
repeat=False, shuffle=False)
updater = training.StandardUpdater(train_iter, optimizer, device=args.gpu)
trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=args.out)
trainer.extend(extensions.Evaluator(test_iter, model, device=args.gpu))
trainer.extend(extensions.dump_graph('main/loss'))
trainer.extend(extensions.snapshot(), trigger=(args.epoch, 'epoch'))
trainer.extend(extensions.LogReport())
# trainer.extend(
# extensions.PlotReport('main/loss', 'epoch', file_name='loss.png'))
# trainer.extend(
# extensions.PlotReport('main/accuracy', 'epoch', file_name='accuracy.png'))
trainer.extend(extensions.PrintReport(
['epoch', 'main/loss', 'validation/main/loss',
'main/accuracy', 'validation/main/accuracy', 'elapsed_time']))
#trainer.extend(extensions.PrintReport(
# ['epoch', 'main/loss', 'main/accuracy', 'elapsed_time']))
trainer.extend(extensions.ProgressBar())
print('training start!')
trainer.run()
# モデルをCPU対応へ
model.to_cpu()
# 保存
modelname = args.out + "/speaker.model"
print('save the trained model: {}'.format(modelname))
chainer.serializers.save_npz(modelname, model)
def main():
# Parse the arguments.
args = parse_arguments()
device = args.gpu
# Set up some useful variables that will be used later on.
method = args.method
if args.label != 'all':
label = args.label
cache_dir = os.path.join('input', '{}_{}'.format(method, label))
labels = [label]
else:
labels = D.get_qm9_label_names()
cache_dir = os.path.join('input', '{}_all'.format(method))
# Get the filename corresponding to the cached dataset, based on the amount
# of data samples that need to be parsed from the original dataset.
num_data = args.num_data
if num_data >= 0:
dataset_filename = 'data_{}.npz'.format(num_data)
else:
dataset_filename = 'data.npz'
# Load the cached dataset.
dataset_cache_path = os.path.join(cache_dir, dataset_filename)
dataset = None
if os.path.exists(dataset_cache_path):
print('Loading cached data from {}.'.format(dataset_cache_path))
dataset = NumpyTupleDataset.load(dataset_cache_path)
if dataset is None:
print('Preprocessing dataset...')
preprocessor = preprocess_method_dict[method]()
dataset = D.get_qm9(preprocessor, labels=labels)
# Cache the newly preprocessed dataset.
if not os.path.exists(cache_dir):
os.mkdir(cache_dir)
NumpyTupleDataset.save(dataset_cache_path, dataset)
# Use a predictor with scaled output labels.
model_path = os.path.join(args.in_dir, args.model_filename)
regressor = Regressor.load_pickle(model_path, device=device)
scaler = regressor.predictor.scaler
if scaler is not None:
original_t = dataset.get_datasets()[-1]
if args.gpu >= 0:
scaled_t = cuda.to_cpu(scaler.transform(
cuda.to_gpu(original_t)))
else:
scaled_t = scaler.transform(original_t)
dataset = NumpyTupleDataset(*(dataset.get_datasets()[:-1] +
(scaled_t,)))
# Split the dataset into training and testing.
train_data_size = int(len(dataset) * args.train_data_ratio)
_, test = split_dataset_random(dataset, train_data_size, args.seed)
# This callback function extracts only the inputs and discards the labels.
def extract_inputs(batch, device=None):
return concat_mols(batch, device=device)[:-1]
def postprocess_fn(x):
if scaler is not None:
scaled_x = scaler.inverse_transform(x)
return scaled_x
else:
return x
# Predict the output labels.
print('Predicting...')
y_pred = regressor.predict(
test, converter=extract_inputs,
postprocess_fn=postprocess_fn)
# Extract the ground-truth labels.
t = concat_mols(test, device=device)[-1]
original_t = cuda.to_cpu(scaler.inverse_transform(t))
# Construct dataframe.
df_dict = {}
for i, l in enumerate(labels):
df_dict.update({'y_pred_{}'.format(l): y_pred[:, i],
't_{}'.format(l): original_t[:, i], })
df = pandas.DataFrame(df_dict)
# Show a prediction/ground truth table with 5 random examples.
print(df.sample(5))
n_eval = 10
for target_label in range(y_pred.shape[1]):
label_name = labels[target_label]
diff = y_pred[:n_eval, target_label] - original_t[:n_eval,
target_label]
print('label_name = {}, y_pred = {}, t = {}, diff = {}'
.format(label_name, y_pred[:n_eval, target_label],
original_t[:n_eval, target_label], diff))
# Run an evaluator on the test dataset.
print('Evaluating...')
test_iterator = SerialIterator(test, 16, repeat=False, shuffle=False)
eval_result = Evaluator(test_iterator, regressor, converter=concat_mols,
device=device)()
print('Evaluation result: ', eval_result)
# Save the evaluation results.
save_json(os.path.join(args.in_dir, 'eval_result.json'), eval_result)
# Calculate mean abs error for each label
mae = numpy.mean(numpy.abs(y_pred - original_t), axis=0)
eval_result = {}
for i, l in enumerate(labels):
eval_result.update({l: mae[i]})
save_json(os.path.join(args.in_dir, 'eval_result_mae.json'), eval_result)
def main():
# Supported preprocessing/network list
method_list = ['nfp', 'ggnn', 'schnet', 'weavenet', 'rsgcn']
label_names = [
'A', 'B', 'C', 'mu', 'alpha', 'h**o', 'lumo', 'gap', 'r2', 'zpve',
'U0', 'U', 'H', 'G', 'Cv'
]
scale_list = ['standardize', 'none']
parser = argparse.ArgumentParser(description='Regression with QM9.')
parser.add_argument('--method',
'-m',
type=str,
choices=method_list,
default='nfp')
parser.add_argument('--label',
'-l',
type=str,
choices=label_names,
default='',
help='target label for regression, '
'empty string means to predict all '
'property at once')
parser.add_argument('--scale',
type=str,
choices=scale_list,
default='standardize',
help='Label scaling method')
parser.add_argument('--batchsize', '-b', type=int, default=32)
parser.add_argument('--gpu', '-g', type=int, default=-1)
parser.add_argument('--in-dir', '-i', type=str, default='result')
parser.add_argument('--seed', '-s', type=int, default=777)
parser.add_argument('--train-data-ratio', '-t', type=float, default=0.7)
parser.add_argument('--model-filename', type=str, default='regressor.pkl')
parser.add_argument('--num-data',
type=int,
default=-1,
help='Number of data to be parsed from parser.'
'-1 indicates to parse all data.')
args = parser.parse_args()
seed = args.seed
train_data_ratio = args.train_data_ratio
method = args.method
if args.label:
labels = args.label
cache_dir = os.path.join('input', '{}_{}'.format(method, labels))
# class_num = len(labels) if isinstance(labels, list) else 1
else:
labels = D.get_qm9_label_names()
cache_dir = os.path.join('input', '{}_all'.format(method))
# class_num = len(labels)
# Dataset preparation
dataset = None
num_data = args.num_data
if num_data >= 0:
dataset_filename = 'data_{}.npz'.format(num_data)
else:
dataset_filename = 'data.npz'
dataset_cache_path = os.path.join(cache_dir, dataset_filename)
if os.path.exists(dataset_cache_path):
print('load from cache {}'.format(dataset_cache_path))
dataset = NumpyTupleDataset.load(dataset_cache_path)
if dataset is None:
print('preprocessing dataset...')
preprocessor = preprocess_method_dict[method]()
dataset = D.get_qm9(preprocessor, labels=labels)
if not os.path.exists(cache_dir):
os.mkdir(cache_dir)
NumpyTupleDataset.save(dataset_cache_path, dataset)
if args.scale == 'standardize':
# Standard Scaler for labels
with open(os.path.join(args.in_dir, 'ss.pkl'), mode='rb') as f:
ss = pickle.load(f)
else:
ss = None
train_data_size = int(len(dataset) * train_data_ratio)
train, val = split_dataset_random(dataset, train_data_size, seed)
regressor = Regressor.load_pickle(os.path.join(args.in_dir,
args.model_filename),
device=args.gpu) # type: Regressor
# We need to feed only input features `x` to `predict`/`predict_proba`.
# This converter extracts only inputs (x1, x2, ...) from the features which
# consist of input `x` and label `t` (x1, x2, ..., t).
def extract_inputs(batch, device=None):
return concat_mols(batch, device=device)[:-1]
def postprocess_fn(x):
if ss is not None:
# Model's output is scaled by StandardScaler,
# so we need to rescale back.
if isinstance(x, Variable):
x = x.data
scaled_x = ss.inverse_transform(cuda.to_cpu(x))
return scaled_x
else:
return x
print('Predicting...')
y_pred = regressor.predict(val,
converter=extract_inputs,
postprocess_fn=postprocess_fn)
print('y_pred.shape = {}, y_pred[:5, 0] = {}'.format(
y_pred.shape, y_pred[:5, 0]))
t = concat_mols(val, device=-1)[-1]
n_eval = 10
# Construct dataframe
df_dict = {}
for i, l in enumerate(labels):
df_dict.update({
'y_pred_{}'.format(l): y_pred[:, i],
't_{}'.format(l): t[:, i],
})
df = pandas.DataFrame(df_dict)
# Show random 5 example's prediction/ground truth table
print(df.sample(5))
for target_label in range(y_pred.shape[1]):
diff = y_pred[:n_eval, target_label] - t[:n_eval, target_label]
print('target_label = {}, y_pred = {}, t = {}, diff = {}'.format(
target_label, y_pred[:n_eval, target_label],
t[:n_eval, target_label], diff))
# --- evaluate ---
# To calc loss/accuracy, we can use `Evaluator`, `ROCAUCEvaluator`
print('Evaluating...')
val_iterator = SerialIterator(val, 16, repeat=False, shuffle=False)
eval_result = Evaluator(val_iterator,
regressor,
converter=concat_mols,
device=args.gpu)()
print('Evaluation result: ', eval_result)
def main():
# Supported preprocessing/network list
method_list = ['nfp', 'ggnn', 'schnet', 'weavenet', 'rsgcn']
scale_list = ['standardize', 'none']
parser = argparse.ArgumentParser(
description='Regression with own dataset.')
parser.add_argument('--datafile', type=str, default='dataset.csv')
parser.add_argument('--method',
'-m',
type=str,
choices=method_list,
default='nfp')
parser.add_argument('--label',
'-l',
nargs='+',
default=['value1', 'value2'],
help='target label for regression')
parser.add_argument('--scale',
type=str,
choices=scale_list,
default='standardize',
help='Label scaling method')
parser.add_argument('--conv-layers', '-c', type=int, default=4)
parser.add_argument('--batchsize', '-b', type=int, default=32)
parser.add_argument('--gpu', '-g', type=int, default=-1)
parser.add_argument('--out', '-o', type=str, default='result')
parser.add_argument('--epoch', '-e', type=int, default=20)
parser.add_argument('--unit-num', '-u', type=int, default=16)
parser.add_argument('--seed', '-s', type=int, default=777)
parser.add_argument('--train-data-ratio', '-t', type=float, default=0.7)
parser.add_argument('--protocol', type=int, default=2)
args = parser.parse_args()
seed = args.seed
train_data_ratio = args.train_data_ratio
method = args.method
if args.label:
labels = args.label
class_num = len(labels) if isinstance(labels, list) else 1
else:
sys.exit("Error: No target label is specified.")
# Dataset preparation
# Postprocess is required for regression task
def postprocess_label(label_list):
return numpy.asarray(label_list, dtype=numpy.float32)
print('Preprocessing dataset...')
preprocessor = preprocess_method_dict[method]()
parser = CSVFileParser(preprocessor,
postprocess_label=postprocess_label,
labels=labels,
smiles_col='SMILES')
dataset = parser.parse(args.datafile)["dataset"]
if args.scale == 'standardize':
# Standard Scaler for labels
scaler = StandardScaler()
labels = scaler.fit_transform(dataset.get_datasets()[-1])
dataset = NumpyTupleDataset(*(dataset.get_datasets()[:-1] +
(labels, )))
else:
# Not use scaler
scaler = None
train_data_size = int(len(dataset) * train_data_ratio)
train, val = split_dataset_random(dataset, train_data_size, seed)
# Network
n_unit = args.unit_num
conv_layers = args.conv_layers
if method == 'nfp':
print('Train NFP model...')
model = GraphConvPredictor(
NFP(out_dim=n_unit, hidden_dim=n_unit, n_layers=conv_layers),
MLP(out_dim=class_num, hidden_dim=n_unit))
elif method == 'ggnn':
print('Train GGNN model...')
model = GraphConvPredictor(
GGNN(out_dim=n_unit, hidden_dim=n_unit, n_layers=conv_layers),
MLP(out_dim=class_num, hidden_dim=n_unit))
elif method == 'schnet':
print('Train SchNet model...')
model = GraphConvPredictor(
SchNet(out_dim=class_num, hidden_dim=n_unit, n_layers=conv_layers),
None)
elif method == 'weavenet':
print('Train WeaveNet model...')
n_atom = 20
n_sub_layer = 1
weave_channels = [50] * conv_layers
model = GraphConvPredictor(
WeaveNet(weave_channels=weave_channels,
hidden_dim=n_unit,
n_sub_layer=n_sub_layer,
n_atom=n_atom), MLP(out_dim=class_num, hidden_dim=n_unit))
elif method == 'rsgcn':
print('Train RSGCN model...')
model = GraphConvPredictor(
RSGCN(out_dim=n_unit, hidden_dim=n_unit, n_layers=conv_layers),
MLP(out_dim=class_num, hidden_dim=n_unit))
else:
raise ValueError('[ERROR] Invalid method {}'.format(method))
train_iter = iterators.SerialIterator(train, args.batchsize)
val_iter = iterators.SerialIterator(val,
args.batchsize,
repeat=False,
shuffle=False)
regressor = Regressor(
model,
lossfun=F.mean_squared_error,
metrics_fun={'abs_error': ScaledAbsError(scaler=scaler)},
device=args.gpu)
optimizer = optimizers.Adam()
optimizer.setup(regressor)
updater = training.StandardUpdater(train_iter,
optimizer,
device=args.gpu,
converter=concat_mols)
trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=args.out)
trainer.extend(
E.Evaluator(val_iter,
regressor,
device=args.gpu,
converter=concat_mols))
trainer.extend(E.snapshot(), trigger=(args.epoch, 'epoch'))
trainer.extend(E.LogReport())
# Note that original scale absolute errors are reported in
# (validation/)main/abs_error
trainer.extend(
E.PrintReport([
'epoch', 'main/loss', 'main/abs_error', 'validation/main/loss',
'validation/main/abs_error', 'elapsed_time'
]))
trainer.extend(E.ProgressBar())
trainer.run()
# --- save regressor's parameters ---
protocol = args.protocol
model_path = os.path.join(args.out, 'model.npz')
print('saving trained model to {}'.format(model_path))
serializers.save_npz(model_path, regressor)
if scaler is not None:
with open(os.path.join(args.out, 'scaler.pkl'), mode='wb') as f:
pickle.dump(scaler, f, protocol=protocol)
# Example of prediction using trained model
smiles = 'c1ccccc1'
mol = Chem.MolFromSmiles(smiles)
preprocessor = preprocess_method_dict[method]()
standardized_smiles, mol = preprocessor.prepare_smiles_and_mol(mol)
input_features = preprocessor.get_input_features(mol)
atoms, adjs = concat_mols([input_features], device=args.gpu)
prediction = model(atoms, adjs).data[0]
print('Prediction for {}:'.format(smiles))
for i, label in enumerate(args.label):
print('{}: {}'.format(label, prediction[i]))
def main():
# Parse the arguments.
args = parse_arguments()
device = chainer.get_device(args.device)
# Set up some useful variables that will be used later on.
method = args.method
if args.label != 'all':
label = args.label
cache_dir = os.path.join('input', '{}_{}'.format(method, label))
labels = [label]
else:
labels = D.get_qm9_label_names()
cache_dir = os.path.join('input', '{}_all'.format(method))
# Get the filename corresponding to the cached dataset, based on the amount
# of data samples that need to be parsed from the original dataset.
num_data = args.num_data
if num_data >= 0:
dataset_filename = 'data_{}.npz'.format(num_data)
else:
dataset_filename = 'data.npz'
# Load the cached dataset.
dataset_cache_path = os.path.join(cache_dir, dataset_filename)
dataset = None
if os.path.exists(dataset_cache_path):
print('Loading cached data from {}.'.format(dataset_cache_path))
dataset = NumpyTupleDataset.load(dataset_cache_path)
if dataset is None:
print('Preprocessing dataset...')
preprocessor = preprocess_method_dict[method]()
if num_data >= 0:
# Select the first `num_data` samples from the dataset.
target_index = numpy.arange(num_data)
dataset = D.get_qm9(preprocessor,
labels=labels,
target_index=target_index)
else:
# Load the entire dataset.
dataset = D.get_qm9(preprocessor, labels=labels)
# Cache the newly preprocessed dataset.
if not os.path.exists(cache_dir):
os.mkdir(cache_dir)
if isinstance(dataset, NumpyTupleDataset):
NumpyTupleDataset.save(dataset_cache_path, dataset)
# Use a predictor with scaled output labels.
model_path = os.path.join(args.in_dir, args.model_filename)
regressor = Regressor.load_pickle(model_path, device=device)
# Split the dataset into training and testing.
train_data_size = int(len(dataset) * args.train_data_ratio)
_, test = split_dataset_random(dataset, train_data_size, args.seed)
# This callback function extracts only the inputs and discards the labels.
# TODO(nakago): consider how to switch which `converter` to use.
if isinstance(dataset, NumpyTupleDataset):
converter = converter_method_dict[method]
@chainer.dataset.converter()
def extract_inputs(batch, device=None):
return converter(batch, device=device)[:-1]
# Extract the ground-truth labels as numpy array.
original_t = converter(test, device=-1)[-1]
else:
converter = dataset.converter
extract_inputs = converter
# Extract the ground-truth labels as numpy array.
original_t = converter(test, device=-1).y
# Predict the output labels.
print('Predicting...')
y_pred = regressor.predict(test, converter=extract_inputs)
df_dict = {}
for i, l in enumerate(labels):
df_dict.update({
'y_pred_{}'.format(l): y_pred[:, i],
't_{}'.format(l): original_t[:, i],
})
df = pandas.DataFrame(df_dict)
# Show a prediction/ground truth table with 5 random examples.
print(df.sample(5))
n_eval = 10
for target_label in range(y_pred.shape[1]):
label_name = labels[target_label]
diff = y_pred[:n_eval, target_label] - original_t[:n_eval,
target_label]
print('label_name = {}, y_pred = {}, t = {}, diff = {}'.format(
label_name, y_pred[:n_eval, target_label],
original_t[:n_eval, target_label], diff))
# Run an evaluator on the test dataset.
print('Evaluating...')
test_iterator = SerialIterator(test, 16, repeat=False, shuffle=False)
eval_result = Evaluator(test_iterator,
regressor,
converter=converter,
device=device)()
print('Evaluation result: ', eval_result)
# Save the evaluation results.
save_json(os.path.join(args.in_dir, 'eval_result.json'), eval_result)
# Calculate mean abs error for each label
mae = numpy.mean(numpy.abs(y_pred - original_t), axis=0)
eval_result = {}
for i, l in enumerate(labels):
eval_result.update({l: mae[i]})
save_json(os.path.join(args.in_dir, 'eval_result_mae.json'), eval_result)
for j in range(len(dataset)):
if dataset[j][1] == labelids[i]:
for k in range(int(max(labeldist) / labeldist[i])):
for l in range(extend):
datas.append(dataset[j][0])
labels.append(i)
conf = np.unique(labels, return_counts=True)
header = ['Label_No', 'ExtendedCount', 'Probability']
table = [conf[0], conf[1], ext]
result = tabulate(np.array(table).transpose(), header, tablefmt="grid")
log.write(result + "\n")
dataset = TupleDataset(datas, labels)
log.write("data extended\n")
#データをシャッフルする
from chainer.datasets import split_dataset_random
dataset, null = split_dataset_random(dataset, len(dataset), seed=0)
log.write("data randomized\n")
#データセット全体を 7 : 3 の比率でランダム分割し、学習用、検証用のデータセットとする
from chainer.datasets import split_dataset_random
n_train = int(len(dataset) * 0.7)
n_valid = int(len(dataset) * 0.3)
train, valid = split_dataset_random(dataset, n_train, seed=0)
train_len = len(train)
valid_len = len(valid)
log.write('Training dataset size:' + str(train_len) + "\n")
log.write('Validation dataset size:' + str(valid_len) + "\n")
train_datas = []
train_labels = []
valid_datas = []
valid_labels = []
def get_mpii_dataset(insize,
image_root,
annotations,
train_size=0.5,
min_num_keypoints=1,
use_cache=False,
seed=0):
dataset_type = 'mpii'
annotations = json.load(open(annotations, 'r'))
# filename => keypoints, bbox, is_visible, is_labeled
images = {}
for filename in np.unique([anno['filename'] for anno in annotations]):
images[filename] = [], [], [], []
for anno in annotations:
is_visible = [anno['is_visible'][k] for k in KEYPOINT_NAMES[1:]]
if sum(is_visible) < min_num_keypoints:
continue
keypoints = [anno['joint_pos'][k][::-1] for k in KEYPOINT_NAMES[1:]]
x1, y1, x2, y2 = anno['head_rect']
entry = images[anno['filename']]
entry[0].append(np.array(keypoints)) # array of y,x
entry[1].append(np.array([x1, y1, x2 - x1, y2 - y1])) # x, y, w, h
entry[2].append(np.array(is_visible, dtype=np.bool))
entry[3].append(np.ones(len(is_visible), dtype=np.bool))
# split dataset
train_images, test_images = split_dataset_random(
list(images.keys()), int(len(images) * train_size), seed=seed)
train_set = KeypointDataset2D(
dataset_type=dataset_type,
insize=insize,
keypoint_names=KEYPOINT_NAMES,
edges=np.array(EDGES),
flip_indices=FLIP_INDICES,
keypoints=[images[i][0] for i in train_images],
bbox=[images[i][1] for i in train_images],
is_visible=[images[i][2] for i in train_images],
is_labeled=[images[i][3] for i in train_images],
image_paths=train_images,
image_root=image_root,
use_cache=use_cache,
do_augmentation=True)
test_set = KeypointDataset2D(
dataset_type=dataset_type,
insize=insize,
keypoint_names=KEYPOINT_NAMES,
edges=np.array(EDGES),
flip_indices=FLIP_INDICES,
keypoints=[images[i][0] for i in test_images],
bbox=[images[i][1] for i in test_images],
is_visible=[images[i][2] for i in test_images],
is_labeled=[images[i][3] for i in test_images],
image_paths=test_images,
image_root=image_root,
use_cache=use_cache,
do_augmentation=False)
return train_set, test_set
def main():
parser = argparse.ArgumentParser(
description='Chainer example: POS-tagging')
parser.add_argument('--batchsize', '-b', type=int, default=30,
help='Number of images in each mini batch')
parser.add_argument('--epoch', '-e', type=int, default=20,
help='Number of sweeps over the dataset to train')
parser.add_argument('--gpu', '-g', type=int, default=-1,
help='GPU ID (negative value indicates CPU)')
parser.add_argument('--out', '-o', default='result',
help='Directory to output the result')
parser.add_argument('--resume', '-r', default='',
help='Resume the training from snapshot')
args = parser.parse_args()
vocab = collections.defaultdict(lambda: len(vocab))
pos_vocab = collections.defaultdict(lambda: len(pos_vocab))
# Convert word sequences and pos sequences to integer sequences.
nltk.download('brown')
data = []
for sentence in nltk.corpus.brown.tagged_sents():
xs = numpy.array([vocab[lex] for lex, _ in sentence], numpy.int32)
ys = numpy.array([pos_vocab[pos] for _, pos in sentence], numpy.int32)
data.append((xs, ys))
print('# of sentences: {}'.format(len(data)))
print('# of words: {}'.format(len(vocab)))
print('# of pos: {}'.format(len(pos_vocab)))
model = CRF(len(vocab), len(pos_vocab))
if args.gpu >= 0:
chainer.backends.cuda.get_device_from_id(args.gpu).use()
model.to_gpu(args.gpu)
optimizer = chainer.optimizers.Adam()
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer.WeightDecay(0.0001))
test_data, train_data = datasets.split_dataset_random(
data, len(data) // 10, seed=0)
train_iter = chainer.iterators.SerialIterator(train_data, args.batchsize)
test_iter = chainer.iterators.SerialIterator(test_data, args.batchsize,
repeat=False, shuffle=False)
updater = training.updaters.StandardUpdater(
train_iter, optimizer, converter=convert, device=args.gpu)
trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=args.out)
evaluator = extensions.Evaluator(
test_iter, model, device=args.gpu, converter=convert)
# Only validate in each 1000 iteration
trainer.extend(evaluator, trigger=(1000, 'iteration'))
trainer.extend(extensions.LogReport(trigger=(100, 'iteration')),
trigger=(100, 'iteration'))
trainer.extend(
extensions.MicroAverage(
'main/correct', 'main/total', 'main/accuracy'))
trainer.extend(
extensions.MicroAverage(
'validation/main/correct', 'validation/main/total',
'validation/main/accuracy'))
trainer.extend(
extensions.PrintReport(
['epoch', 'main/loss', 'validation/main/loss',
'main/accuracy', 'validation/main/accuracy', 'elapsed_time']),
trigger=(100, 'iteration'))
trainer.extend(extensions.ProgressBar(update_interval=10))
if args.resume:
chainer.serializers.load_npz(args.resume, trainer)
trainer.run()
def main():
# Parse the arguments.
args = parse_arguments()
# Set up some useful variables that will be used later on.
method = args.method
if args.label != 'all':
labels = args.label
cache_dir = os.path.join('input', '{}_{}'.format(method, labels))
class_num = len(labels) if isinstance(labels, list) else 1
else:
labels = None
cache_dir = os.path.join('input', '{}_all'.format(method))
class_num = len(D.get_qm9_label_names())
# Get the filename corresponding to the cached dataset, based on the amount
# of data samples that need to be parsed from the original dataset.
num_data = args.num_data
if num_data >= 0:
dataset_filename = 'data_{}.npz'.format(num_data)
else:
dataset_filename = 'data.npz'
# Load the cached dataset.
dataset_cache_path = os.path.join(cache_dir, dataset_filename)
dataset = None
if os.path.exists(dataset_cache_path):
print('Loading cached dataset from {}.'.format(dataset_cache_path))
dataset = NumpyTupleDataset.load(dataset_cache_path)
if dataset is None:
print('Preprocessing dataset...')
preprocessor = preprocess_method_dict[method]()
if num_data >= 0:
# Select the first `num_data` samples from the dataset.
target_index = numpy.arange(num_data)
dataset = D.get_qm9(preprocessor,
labels=labels,
target_index=target_index)
else:
# Load the entire dataset.
dataset = D.get_qm9(preprocessor, labels=labels)
# Cache the laded dataset.
if not os.path.exists(cache_dir):
os.makedirs(cache_dir)
NumpyTupleDataset.save(dataset_cache_path, dataset)
# Scale the label values, if necessary.
if args.scale == 'standardize':
print('Applying standard scaling to the labels.')
scaler = StandardScaler()
scaled_t = scaler.fit_transform(dataset.get_datasets()[-1])
dataset = NumpyTupleDataset(*(dataset.get_datasets()[:-1] +
(scaled_t, )))
else:
print('No standard scaling was selected.')
scaler = None
# Split the dataset into training and validation.
train_data_size = int(len(dataset) * args.train_data_ratio)
train, valid = split_dataset_random(dataset, train_data_size, args.seed)
# Set up the predictor.
predictor = set_up_predictor(method, args.unit_num, args.conv_layers,
class_num, scaler)
# Set up the iterators.
train_iter = iterators.SerialIterator(train, args.batchsize)
valid_iter = iterators.SerialIterator(valid,
args.batchsize,
repeat=False,
shuffle=False)
# Set up the regressor.
device = args.gpu
metrics_fun = {
'mae': MeanAbsError(scaler=scaler),
'rmse': RootMeanSqrError(scaler=scaler)
}
regressor = Regressor(predictor,
lossfun=F.mean_squared_error,
metrics_fun=metrics_fun,
device=device)
# Set up the optimizer.
optimizer = optimizers.Adam()
optimizer.setup(regressor)
# Set up the updater.
updater = training.StandardUpdater(train_iter,
optimizer,
device=device,
converter=concat_mols)
# Set up the trainer.
trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=args.out)
trainer.extend(
E.Evaluator(valid_iter,
regressor,
device=device,
converter=concat_mols))
trainer.extend(E.snapshot(), trigger=(args.epoch, 'epoch'))
trainer.extend(E.LogReport())
trainer.extend(
E.PrintReport([
'epoch', 'main/loss', 'main/mae', 'main/rmse',
'validation/main/loss', 'validation/main/mae',
'validation/main/rmse', 'elapsed_time'
]))
trainer.extend(E.ProgressBar())
trainer.run()
# Save the regressor's parameters.
model_path = os.path.join(args.out, args.model_filename)
print('Saving the trained model to {}...'.format(model_path))
regressor.save_pickle(model_path, protocol=args.protocol)
def main():
# Parse the arguments.
args = parse_arguments()
# Set up some useful variables that will be used later on.
method = args.method
if args.label != 'all':
labels = args.label
cache_dir = os.path.join('input', '{}_{}'.format(method, labels))
class_num = len(labels) if isinstance(labels, list) else 1
else:
labels = None
cache_dir = os.path.join('input', '{}_all'.format(method))
class_num = len(D.get_qm9_label_names())
# Get the filename corresponding to the cached dataset, based on the amount
# of data samples that need to be parsed from the original dataset.
num_data = args.num_data
if num_data >= 0:
dataset_filename = 'data_{}.npz'.format(num_data)
else:
dataset_filename = 'data.npz'
# Load the cached dataset.
dataset_cache_path = os.path.join(cache_dir, dataset_filename)
dataset = None
if os.path.exists(dataset_cache_path):
print('Loading cached dataset from {}.'.format(dataset_cache_path))
dataset = NumpyTupleDataset.load(dataset_cache_path)
if dataset is None:
print('Preprocessing dataset...')
preprocessor = preprocess_method_dict[method]()
if num_data >= 0:
# Select the first `num_data` samples from the dataset.
target_index = numpy.arange(num_data)
dataset = D.get_qm9(preprocessor, labels=labels,
target_index=target_index)
else:
# Load the entire dataset.
dataset = D.get_qm9(preprocessor, labels=labels)
# Cache the laded dataset.
if not os.path.exists(cache_dir):
os.makedirs(cache_dir)
NumpyTupleDataset.save(dataset_cache_path, dataset)
# Scale the label values, if necessary.
if args.scale == 'standardize':
print('Applying standard scaling to the labels.')
scaler = StandardScaler()
scaled_t = scaler.fit_transform(dataset.get_datasets()[-1])
dataset = NumpyTupleDataset(*(dataset.get_datasets()[:-1]
+ (scaled_t,)))
else:
print('No standard scaling was selected.')
scaler = None
# Split the dataset into training and validation.
train_data_size = int(len(dataset) * args.train_data_ratio)
train, valid = split_dataset_random(dataset, train_data_size, args.seed)
# Set up the predictor.
predictor = set_up_predictor(method, args.unit_num, args.conv_layers,
class_num, scaler)
# Set up the iterators.
train_iter = iterators.SerialIterator(train, args.batchsize)
valid_iter = iterators.SerialIterator(valid, args.batchsize, repeat=False,
shuffle=False)
# Set up the regressor.
device = args.gpu
metrics_fun = {'mae': MeanAbsError(scaler=scaler),
'rmse': RootMeanSqrError(scaler=scaler)}
regressor = Regressor(predictor, lossfun=F.mean_squared_error,
metrics_fun=metrics_fun, device=device)
# Set up the optimizer.
optimizer = optimizers.Adam()
optimizer.setup(regressor)
# Set up the updater.
updater = training.StandardUpdater(train_iter, optimizer, device=device,
converter=concat_mols)
# Set up the trainer.
trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=args.out)
trainer.extend(E.Evaluator(valid_iter, regressor, device=device,
converter=concat_mols))
trainer.extend(E.snapshot(), trigger=(args.epoch, 'epoch'))
trainer.extend(E.LogReport())
trainer.extend(E.PrintReport([
'epoch', 'main/loss', 'main/mae', 'main/rmse', 'validation/main/loss',
'validation/main/mae', 'validation/main/rmse', 'elapsed_time']))
trainer.extend(E.ProgressBar())
trainer.run()
# Save the regressor's parameters.
model_path = os.path.join(args.out, args.model_filename)
print('Saving the trained model to {}...'.format(model_path))
regressor.save_pickle(model_path, protocol=args.protocol)