def main():
dataset = dman.Dataset(normalize=FLAGS.datnorm)
neuralnet = nn.MemAE(height=dataset.height,
width=dataset.width,
channel=dataset.channel,
leaning_rate=FLAGS.lr)
sess_config = tf.compat.v1.ConfigProto()
sess_config.gpu_options.allow_growth = True
sess = tf.compat.v1.Session(config=sess_config)
sess.run(tf.compat.v1.global_variables_initializer())
saver = tf.compat.v1.train.Saver()
tfp.training(sess=sess,
neuralnet=neuralnet,
saver=saver,
dataset=dataset,
epochs=FLAGS.epoch,
batch_size=FLAGS.batch,
normalize=True)
tfp.test(sess=sess,
neuralnet=neuralnet,
saver=saver,
dataset=dataset,
batch_size=FLAGS.batch)
def main():
dataset = dman.DataSet(setname=FLAGS.setname, tr_ratio=FLAGS.tr_ratio)
neuralnet = nn.ConvNet(data_dim=dataset.data_dim, channel=dataset.channel, num_class=dataset.num_class, learning_rate=FLAGS.lr)
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
tfp.training(sess=sess, neuralnet=neuralnet, saver=saver, dataset=dataset, epochs=FLAGS.epoch, batch_size=FLAGS.batch, dropout=FLAGS.dropout)
tfp.validation(sess=sess, neuralnet=neuralnet, saver=saver, dataset=dataset)
def main():
dataset = dman.Dataset(normalize=FLAGS.datnorm)
neuralnet = nn.ADAE(height=dataset.height, width=dataset.width, channel=dataset.channel, \
ksize=FLAGS.ksize, learning_rate=FLAGS.lr, path='Checkpoint')
neuralnet.confirm_params(verbose=False)
# neuralnet.confirm_bn()
tfp.training(neuralnet=neuralnet, dataset=dataset, \
epochs=FLAGS.epoch, batch_size=FLAGS.batch, normalize=True)
tfp.test(neuralnet=neuralnet, dataset=dataset, \
batch_size=FLAGS.batch)
def main():
physical_devices = tf.config.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(physical_devices[0], True)
dataset = dman.Dataset(normalize=FLAGS.datnorm)
neuralnet = nn.CVAE(height=dataset.height, width=dataset.width, channel=dataset.channel, \
ksize=FLAGS.ksize, z_dim=FLAGS.z_dim, leaning_rate=FLAGS.lr)
tfp.training(neuralnet=neuralnet,
dataset=dataset,
epochs=FLAGS.epoch,
batch_size=FLAGS.batch)
tfp.test(neuralnet=neuralnet, dataset=dataset, batch_size=FLAGS.batch)
def main():
srnet = nn.SRNET()
dataset = dman.DataSet()
sess = tf.compat.v1.InteractiveSession()
sess.run(tf.compat.v1.global_variables_initializer())
saver = tf.compat.v1.train.Saver()
tfp.training(sess=sess,
neuralnet=srnet,
saver=saver,
dataset=dataset,
epochs=FLAGS.epoch,
batch_size=FLAGS.batch)
tfp.validation(sess=sess, neuralnet=srnet, saver=saver, dataset=dataset)
def main():
try:
physical_devices = tf.config.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(physical_devices[0], True)
except:
pass
dataset = dman.Dataset(normalize=FLAGS.datnorm)
neuralnet = nn.CNN(height=dataset.height, width=dataset.width, channel=dataset.channel, \
num_class=dataset.num_class, ksize=3, learning_rate=FLAGS.lr, ckpt_dir=CKPT_DIR)
tfp.training(neuralnet=neuralnet,
dataset=dataset,
epochs=FLAGS.epoch,
batch_size=FLAGS.batch,
normalize=True)
tfp.test(neuralnet=neuralnet, dataset=dataset, batch_size=FLAGS.batch)
def main():
dataset = dman.Dataset(normalize=FLAGS.datnorm)
neuralnet = nn.Context_Encoder(height=dataset.height, width=dataset.width, channel=dataset.channel, \
z_dim=FLAGS.z_dim, learning_rate=FLAGS.lr)
sess_config = tf.compat.v1.ConfigProto()
sess_config.gpu_options.allow_growth = True
sess = tf.compat.v1.Session(config=sess_config)
sess.run(tf.compat.v1.global_variables_initializer())
saver = tf.compat.v1.train.Saver()
time_1 = time.time()
tfp.training(sess=sess, neuralnet=neuralnet, saver=saver, dataset=dataset, epochs=FLAGS.epoch, batch_size=FLAGS.batch, normalize=True)
time_2 = time.time()
tfp.test(sess=sess, neuralnet=neuralnet, saver=saver, dataset=dataset, batch_size=FLAGS.batch)
time_3 = time.time()
print("TR: ", time_2 - time_1, dataset.num_tr)
print("TE: ", (time_3 - time_2)/dataset.num_te)
def main():
training_keys = ['100']
dataset = dman.DataSet(key_tr=training_keys)
lstm = nn.LSTM_Model(batch_size=FLAGS.batch, data_dim=dataset.data_dim)
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
tfp.training(sess=sess,
neuralnet=lstm,
saver=saver,
dataset=dataset,
batch_size=FLAGS.batch,
sequence_length=FLAGS.trainlen,
iteration=FLAGS.iter)
tfp.validation(sess=sess,
neuralnet=lstm,
saver=saver,
dataset=dataset,
sequence_length=FLAGS.testlen)
def main():
os.environ["CUDA_VISIBLE_DEVICES"] = FLAGS.gpu
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
try:
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
print(len(gpus), "Physical GPUs,", len(logical_gpus),
"Logical GPUs")
except RuntimeError as e:
print(e)
dataset = dman.Dataset()
agent = con.connect(nn=FLAGS.nn).Agent(\
dim_h = dataset.height, \
dim_w = dataset.width, \
dim_c = dataset.channel, \
num_class = dataset.num_class, \
ksize = FLAGS.ksize, \
learning_rate = FLAGS.lr, \
path_ckpt = 'Checkpoint')
time_tr = time.time()
tfp.training(agent=agent, dataset=dataset, \
batch_size=FLAGS.batch, epochs=FLAGS.epochs)
time_te = time.time()
tfp.test(agent=agent, dataset=dataset, batch_size=FLAGS.batch)
time_fin = time.time()
print("Time (TR): %.5f [sec]" % (time_te - time_tr))
te_time = time_fin - time_te
print("Time (TE): %.5f (%.5f [sec/sample])" %
(te_time, te_time / dataset.num_te))
def main():
"""Initializing dataset and neural network"""
dataset = dman.Dataset(normalize=FLAGS.datnorm)
neuralnet = nn.adVAE(height=dataset.height, width=dataset.width, channel=dataset.channel, \
z_dim=FLAGS.z_dim, mx=FLAGS.mx, mz=FLAGS.mz, leaning_rate=FLAGS.lr)
sess_config = tf.compat.v1.ConfigProto()
sess_config.gpu_options.allow_growth = True
sess = tf.compat.v1.Session(config=sess_config)
sess.run(tf.compat.v1.global_variables_initializer())
saver = tf.compat.v1.train.Saver()
"""process of training and test with neural network"""
tfp.training(sess=sess,
neuralnet=neuralnet,
saver=saver,
dataset=dataset,
epochs=FLAGS.epoch,
batch_size=FLAGS.batch,
normalize=True)
tfp.test(sess=sess,
neuralnet=neuralnet,
saver=saver,
dataset=dataset,
batch_size=FLAGS.batch)
