def train(args):
import models
import numpy as np
# np.random.seed(1234)
if args.dataset == 'mnist':
n_dim, n_out, n_channels = 28, 10, 1
X_train, y_train, X_val, y_val, _, _ = data.load_mnist()
elif args.dataset == 'random':
n_dim, n_out, n_channels = 2, 2, 1
X_train, y_train = data.load_noise(n=1000, d=n_dim)
X_val, y_val = X_train, y_train
else:
raise ValueError('Invalid dataset name: %s' % args.dataset)
# set up optimization params
opt_params = { 'lr' : args.lr, 'c' : args.c, 'n_critic' : args.n_critic }
# create model
if args.model == 'dcgan':
model = models.DCGAN(n_dim=n_dim, n_chan=n_channels, opt_params=opt_params)
elif args.model == 'wdcgan':
model = models.WDCGAN(n_dim=n_dim, n_chan=n_channels, opt_params=opt_params)
else:
raise ValueError('Invalid model')
# train model
model.fit(X_train, X_val,
n_epoch=args.epochs, n_batch=args.n_batch,
logdir=args.logdir)
def train(args):
#load dataset
if args.dataset == 'mnist':
n_dim, n_out, n_channels = 28, 10, 1
X_train, y_train, X_val, y_val, _, _ = data.load_mnist()
elif args.dataset == 'random':
n_dim, n_out, n_channels = 2, 2, 1
X_train, y_train = data.load_noise(n=1000, d=n_dim)
X_val, y_val = X_train, y_train
#可扩展
elif args.dataset == 'malware_clean_data':
n_dim, n_channels = 64, 1
xtrain_mal, ytrain_mal, xtrain_ben, ytrain_ben, xtest_mal, ytest_mal, xtest_ben, ytest_ben = data.load_Malware_clean_ApkToImage(
)
if args.same_train_data:
X_train, y_train, X_val, y_val = xtrain_mal, ytrain_mal, xtrain_ben, ytrain_ben
else:
raise ValueError('Invalid dataset name: %s' % args.dataset)
# set up optimization params
opt_params = {'lr': args.lr, 'c': args.c, 'n_critic': args.n_critic}
# create model
if args.model == 'dcgan':
model = models.DCGAN(n_dim=n_dim,
n_chan=n_channels,
opt_params=opt_params)
elif args.model == 'wdcgan':
model = models.WDCGAN(n_dim=n_dim,
n_chan=n_channels,
opt_params=opt_params)
else:
raise ValueError('Invalid model')
# train model
model.fit(X_train,
y_train,
X_val,
y_val,
n_epoch=args.epochs,
n_batch=args.n_batch,
logdir=args.logdir)
def train(args):
import models
import numpy as np
np.random.seed(1234)
if args.dataset == 'mnist':
n_dim, n_out, n_channels = 28, 10, 1
X_train, Y_train, X_val, Y_val, _, _ = data.load_mnist()
elif args.dataset == 'binmnist':
n_dim, n_out, n_channels = 28, 10, 1
X_train, X_val, _ = data.load_mnist_binarized()
X_train = X_train.reshape((-1, 1, 28, 28))
X_val = X_val.reshape((-1, 1, 28, 28))
Y_train = np.empty((X_train.shape[0], ), dtype='int32')
Y_val = np.empty((X_val.shape[0], ), dtype='int32')
elif args.dataset == 'omniglot':
n_dim, n_out, n_channels = 28, 10, 1
X_train, Y_train, X_val, Y_val = data.load_omniglot_iwae()
X_train = X_train.reshape((-1, 1, 28, 28))
X_val = X_val.reshape((-1, 1, 28, 28))
Y_train = np.empty((X_train.shape[0], ), dtype='int32')
Y_val = np.empty((X_val.shape[0], ), dtype='int32')
elif args.dataset == 'digits':
n_dim, n_out, n_channels = 8, 10, 1
X_train, Y_train, X_val, Y_val, _, _ = data.load_digits()
else:
X_train, Y_train = data.load_h5(args.train)
X_val, Y_val = data.load_h5(args.test)
# also get the data dimensions
print 'dataset loaded.'
# set up optimization params
p = {'lr': args.lr, 'b1': args.b1, 'b2': args.b2, 'nb': args.n_batch}
# create model
if args.model == 'vae':
model = models.VAE(
n_dim=n_dim,
n_out=n_out,
n_chan=n_channels,
n_superbatch=args.n_superbatch,
opt_alg=args.alg,
opt_params=p,
)
elif args.model == 'discrete-vae':
model = models.SBN(
n_dim=n_dim,
n_out=n_out,
n_chan=n_channels,
n_superbatch=args.n_superbatch,
opt_alg=args.alg,
opt_params=p,
)
elif args.model == 'discrete-vae-rbm':
model = models.USBN(
n_dim=n_dim,
n_out=n_out,
n_chan=n_channels,
n_superbatch=args.n_superbatch,
opt_alg=args.alg,
opt_params=p,
)
elif args.model == 'adgm':
model = models.ADGM(
n_dim=n_dim,
n_out=n_out,
n_chan=n_channels,
n_superbatch=args.n_superbatch,
opt_alg=args.alg,
opt_params=p,
)
elif args.model == 'discrete-adgm':
model = models.DADGM(
n_dim=n_dim,
n_out=n_out,
n_chan=n_channels,
n_superbatch=args.n_superbatch,
opt_alg=args.alg,
opt_params=p,
)
elif args.model == 'discrete-adgm-rbm':
model = models.UDADGM(
n_dim=n_dim,
n_out=n_out,
n_chan=n_channels,
n_superbatch=args.n_superbatch,
opt_alg=args.alg,
opt_params=p,
)
elif args.model == 'rbm':
model = models.RBM(
n_dim=n_dim,
n_out=n_out,
n_chan=n_channels,
n_superbatch=args.n_superbatch,
opt_alg=args.alg,
opt_params=p,
)
elif args.model == 'vrbm':
model = models.VariationalRBM(
n_dim=n_dim,
n_out=n_out,
n_chan=n_channels,
n_superbatch=args.n_superbatch,
opt_alg=args.alg,
opt_params=p,
)
elif args.model == 'avrbm':
model = models.AuxiliaryVariationalRBM(
n_dim=n_dim,
n_out=n_out,
n_chan=n_channels,
n_superbatch=args.n_superbatch,
opt_alg=args.alg,
opt_params=p,
)
else:
raise ValueError('Invalid model')
if args.pkl:
model.load(args.pkl)
# generate samples
samples = model.hallucinate_chain()
# plot them
plt.figure(figsize=(5, 5))
plt.imshow(samples, cmap=plt.cm.gray, interpolation='none')
plt.title('Hallucinated Samples')
plt.tight_layout()
plt.savefig(args.plotname)
exit('hello')
# train model
model.fit(X_train,
Y_train,
X_val,
Y_val,
n_epoch=args.epochs,
n_batch=args.n_batch,
logname=args.logname)
# generate samples
samples = model.hallucinate()
# plot them
plt.figure(figsize=(5, 5))
plt.imshow(samples, cmap=plt.cm.gray, interpolation='none')
plt.title('Hallucinated Samples')
plt.tight_layout()
plt.savefig(args.plotname)
def train(args):
import models
import numpy as np
np.random.seed(1234)
if args.dataset == 'digits':
n_dim, n_out, n_channels = 8, 10, 1
X_train, y_train, X_val, y_val = data.load_digits()
elif args.dataset == 'mnist':
n_dim, n_out, n_channels = 28, 10, 1
X_train, y_train, X_val, y_val, _, _ = data.load_mnist()
elif args.dataset == 'svhn':
n_dim, n_out, n_channels = 32, 10, 3
X_train, y_train, X_val, y_val = data.load_svhn()
X_train, y_train, X_val, y_val = data.prepare_dataset(X_train, y_train, X_val, y_val)
elif args.dataset == 'cifar10':
n_dim, n_out, n_channels = 32, 10, 3
X_train, y_train, X_val, y_val = data.load_cifar10()
X_train, y_train, X_val, y_val = data.prepare_dataset(X_train, y_train, X_val, y_val)
elif args.dataset == 'random':
n_dim, n_out, n_channels = 2, 2, 1
X_train, y_train = data.load_noise(n=1000, d=n_dim)
X_val, y_val = X_train, y_train
else:
raise ValueError('Invalid dataset name: %s' % args.dataset)
print 'dataset loaded, dim:', X_train.shape
# set up optimization params
p = { 'lr' : args.lr, 'b1': args.b1, 'b2': args.b2 }
# create model
if args.model == 'softmax':
model = models.Softmax(n_dim=n_dim, n_out=n_out, n_superbatch=args.n_superbatch,
opt_alg=args.alg, opt_params=p)
elif args.model == 'mlp':
model = models.MLP(n_dim=n_dim, n_out=n_out, n_superbatch=args.n_superbatch,
opt_alg=args.alg, opt_params=p)
elif args.model == 'cnn':
model = models.CNN(n_dim=n_dim, n_out=n_out, n_chan=n_channels, model=args.dataset,
n_superbatch=args.n_superbatch, opt_alg=args.alg, opt_params=p)
elif args.model == 'kcnn':
model = models.KCNN(n_dim=n_dim, n_out=n_out, n_chan=n_channels, model=args.dataset,
n_superbatch=args.n_superbatch, opt_alg=args.alg, opt_params=p)
elif args.model == 'resnet':
model = models.Resnet(n_dim=n_dim, n_out=n_out, n_chan=n_channels,
n_superbatch=args.n_superbatch, opt_alg=args.alg, opt_params=p)
elif args.model == 'vae':
model = models.VAE(n_dim=n_dim, n_out=n_out, n_chan=n_channels, n_batch=args.n_batch,
n_superbatch=args.n_superbatch, opt_alg=args.alg, opt_params=p,
model='bernoulli' if args.dataset in ('digits', 'mnist')
else 'gaussian')
elif args.model == 'convvae':
model = models.ConvVAE(n_dim=n_dim, n_out=n_out, n_chan=n_channels, n_batch=args.n_batch,
n_superbatch=args.n_superbatch, opt_alg=args.alg, opt_params=p,
model='bernoulli' if args.dataset in ('digits', 'mnist')
else 'gaussian')
elif args.model == 'convadgm':
model = models.ConvADGM(n_dim=n_dim, n_out=n_out, n_chan=n_channels, n_batch=args.n_batch,
n_superbatch=args.n_superbatch, opt_alg=args.alg, opt_params=p,
model='bernoulli' if args.dataset in ('digits', 'mnist')
else 'gaussian')
elif args.model == 'sbn':
model = models.SBN(n_dim=n_dim, n_out=n_out, n_chan=n_channels,
n_superbatch=args.n_superbatch, opt_alg=args.alg, opt_params=p)
elif args.model == 'adgm':
model = models.ADGM(n_dim=n_dim, n_out=n_out, n_chan=n_channels, n_batch=args.n_batch,
n_superbatch=args.n_superbatch, opt_alg=args.alg, opt_params=p,
model='bernoulli' if args.dataset in ('digits', 'mnist')
else 'gaussian')
elif args.model == 'hdgm':
model = models.HDGM(n_dim=n_dim, n_out=n_out, n_chan=n_channels, n_batch=args.n_batch,
n_superbatch=args.n_superbatch, opt_alg=args.alg, opt_params=p)
elif args.model == 'dadgm':
model = models.DADGM(n_dim=n_dim, n_out=n_out, n_chan=n_channels,
n_superbatch=args.n_superbatch, opt_alg=args.alg, opt_params=p)
elif args.model == 'dcgan':
model = models.DCGAN(n_dim=n_dim, n_out=n_out, n_chan=n_channels,
n_superbatch=args.n_superbatch, opt_alg=args.alg, opt_params=p)
elif args.model == 'ssadgm':
X_train_lbl, y_train_lbl, X_train_unl, y_train_unl \
= data.split_semisup(X_train, y_train, n_lbl=args.n_labeled)
model = models.SSADGM(X_labeled=X_train_lbl, y_labeled=y_train_lbl, n_out=n_out,
n_superbatch=args.n_superbatch, opt_alg=args.alg, opt_params=p)
X_train, y_train = X_train_unl, y_train_unl
else:
raise ValueError('Invalid model')
# train model
model.fit(X_train, y_train, X_val, y_val,
n_epoch=args.epochs, n_batch=args.n_batch,
logname=args.logname)
start_time = time.time() # time evaluation
# mini batch
for i in range(0, (train_Size // BATCH_SIZE)):
_, loss_ = sess.run(
fetches=(optimizer, loss),
feed_dict={X_p: X[i * BATCH_SIZE:(i + 1) * BATCH_SIZE]})
losses.append(loss_)
print("loss:", sum(losses) / len(losses))
#store the first pictre
gen_pic = sess.run(fetches=generate, feed_dict={X_p: X})
#print("gen_pic:\n",gen_pic)
plt.imshow(np.reshape(X[3], newshape=[28, 28]))
plt.imshow(np.reshape(gen_pic[3], newshape=[28, 28]))
plt.show()
if __name__ == "__main__":
print("Load Data....")
X, y_train, X_test = data.load_mnist(reshape=False)
X, X_test = data.standard(X_train=X, X_test=X_test)
print(X.shape)
print(y_train.shape)
print(X_test.shape)
print(X[0])
print("Run Model....")
train(X)
def train(args):
import models
import numpy as np
# np.random.seed(1234)
if args.dataset == 'digits':
n_dim_x, n_dim_y, n_out, n_channels = 8, 8, 10, 1
X_train, y_train, X_val, y_val = data.load_digits()
elif args.dataset == 'mnist':
# load supservised data
n_dim, n_aug, n_dim_x, n_dim_y, n_out, n_channels = 784, 0, 28, 28, 10, 1
X_train, y_train, X_val, y_val, X_test, y_test = data.load_mnist()
X_train = np.concatenate((X_train, X_val))
y_train = np.concatenate((y_train, y_val))
X_val, y_val = X_test, y_test
elif args.dataset == 'svhn':
n_dim, n_aug, n_dim_x, n_dim_y, n_out, n_channels = 3072, 0, 32, 32, 10, 3
X_train, y_train, X_val, y_val = data.load_svhn()
elif args.dataset == 'random':
X_train, y_train = data.load_noise(n=1000, d=n_dim)
else:
raise ValueError('Invalid dataset name: %s' % args.dataset)
print 'dataset loaded.'
# set up optimization params
p = {'lr': args.lr, 'b1': args.b1, 'b2': args.b2}
# X_train_unl = X_train_unl.reshape((-1, n_channels, n_dim_x, n_dim_y))
# X_test = X_test.reshape((-1, n_channels, n_dim_x, n_dim_y))
# create model
if args.model == 'supervised-mlp':
model = models.SupervisedMLP(n_out=n_out,
n_dim=n_dim,
n_aug=n_aug,
n_dim_x=n_dim_x,
n_dim_y=n_dim_y,
n_chan=n_channels,
n_superbatch=args.n_superbatch,
opt_alg=args.alg,
opt_params=p)
elif args.model == 'supervised-hdgm':
X_train = X_train.reshape(-1, n_channels, n_dim_x, n_dim_y)
X_val = X_val.reshape(-1, n_channels, n_dim_x, n_dim_y)
model = models.SupervisedHDGM(
n_out=n_out,
n_dim=n_dim_x,
n_chan=n_channels,
n_superbatch=args.n_superbatch,
opt_alg=args.alg,
opt_params=p,
model='bernoulli' if args.dataset == 'mnist' else 'gaussian')
else:
raise ValueError('Invalid model')
if args.reload: model.load(args.reload)
logname = '%s-%s-%s-%d-%f-%f' % (args.logname, args.dataset, args.model,
args.n_labeled, args.sup_weight,
args.unsup_weight)
# train model
model.fit(X_train,
y_train,
X_val,
y_val,
n_epoch=args.epochs,
n_batch=args.n_batch,
logname=logname)
