def main():
"""
Testing the convolutional example on the mnist dataset.
"""
dataset = RNNMNIST(BATCH_SIZE)
print(dataset.get_train().y.shape)
in_shape = (None, N_STEPS, N_INPUT)
inputs = Value(type=tf.float32, shape=in_shape, cls=None)
targets = Value(type=tf.int32, shape=(None), cls=10)
fc_hidden = [500, 150]
rnn_config = RNNHidden(rnn_weights=RNN_HIDDEN,
depth=1,
fc_weights=fc_hidden)
config = Config(inputs, targets, rnn_config, LEARNING_RATE)
network = ConvNetworkBuilder(config)
hidden = SimpleRNNBuilder()
_ = network.build_network(hidden)
train_config = TrainerConfig(epochs=EPOCHS,
display_after=DISPLAY_STEP,
keep_prob=KEEP_PROB,
checkpoint_path=None,
summary_path=None)
trainer = Trainer(network, train_config)
trainer.train(dataset)
def main():
"""
Testing the convolutional example on the mnist dataset.
"""
dataset = ConvMNIST(64)
print(dataset.get_train().x.shape)
inputs = Value(type=tf.float32, shape=(None, 28, 28, 1), cls=None)
targets = Value(type=tf.int64, shape=(None), cls=10)
learning_rate = 0.0001
fc_hidden = [1024, 500]
c_h = [(3, 3, 1, 32), (3, 3, 32, 64)]
conv_hidden = ConvHidden(conv_weights=c_h, fc_weights=fc_hidden)
config = Config(inputs, targets, conv_hidden, learning_rate)
network = ConvNetworkBuilder(config)
hidden = FFConvHiddenBuilder()
_ = network.build_network(hidden)
train_config = TrainerConfig(epochs=EPOCHS,
display_after=DISPLAY_STEP,
keep_prob=KEEP_PROB,
checkpoint_path=None,
summary_path=None)
trainer = Trainer(network, train_config)
trainer.train(dataset)
def main():
opt = TrainOptions().parse()
# create dataloaders for each phase
dataloaders = create_dataloader(opt)
print("type of subset: ", type(dataloaders[0]))
# Create model
model = create_model(opt)
model.setup(
opt) # regular setup: load and print networks; create schedulers
visualizer = Visualizer(
opt) # create a visualizer that display/save images and plots
# initialize trainer
trainer = Trainer(dataloaders, model, visualizer, opt)
trainer.train()
def main():
args = get_args()
m_config = process_config(args.config)
config = tf.ConfigProto(log_device_placement=False)
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
# create_dirs([config.summary_dir, config.checkpoint_dir])
data_loader = SiameseDataLoader(config=m_config)
model = ConvNet(data_loader=data_loader, config=m_config)
logger = Logger(sess=sess, config=m_config)
trainer = Trainer(sess=sess,
model=model,
config=m_config,
logger=logger,
data_loader=data_loader)
trainer.train()
def main():
"""
Testing the feedforward framework on the mnist dataset.
"""
dataset = MNIST(BATCH_SIZE)
inputs = Value(type=tf.float32, shape=(None, 784), cls=None)
targets = Value(type=tf.int64, shape=(None), cls=10)
fc_hidden = FCHidden(weights=[300, 150])
config = Config(inputs, targets, fc_hidden, LEARNING_RATE)
network_builder = FFNetworkBuilder(config)
hidden_builder = FFHiddenBuilder()
_ = network_builder.build_network(hidden_builder)
train_config = TrainerConfig(epochs=EPOCHS,
display_after=DISPLAY_STEP,
keep_prob=KEEP_PROB,
checkpoint_path=None,
summary_path=None)
trainer = Trainer(network_builder, train_config)
trainer.train(dataset)
smoothing=cfg['model']['smoothing'])
testset = PixWiseDataset(root_dir=cfg['dataset']['root'],
csv_file=cfg['dataset']['test_set'],
map_size=cfg['model']['map_size'],
transform=test_transform,
smoothing=cfg['model']['smoothing'])
trainloader = torch.utils.data.DataLoader(
dataset=trainset,
batch_size=cfg['train']['batch_size'],
shuffle=True,
num_workers=0)
testloader = torch.utils.data.DataLoader(dataset=testset,
batch_size=cfg['test']['batch_size'],
shuffle=True,
num_workers=0)
trainer = Trainer(cfg=cfg,
network=network,
optimizer=optimizer,
loss=loss,
lr_scheduler=None,
device=device,
trainloader=trainloader,
testloader=testloader,
writer=writer)
trainer.train()
writer.close()
