def main(args):
print(args)
parser = argparse.ArgumentParser(description='train')
parser.add_argument('-p', '--parallel', action='store_true')
parser.add_argument('-m', '--mnist', action='store_true')
parser.add_argument('--L1', type=float)
parser.add_argument('--L2', type=float)
parser.add_argument('-e', '--early_stopping', action='store_true')
parser.add_argument('-d', '--dropout', action='store_true')
parser.add_argument('-j', '--jobid')
parser.add_argument('-s', '--small', action='store_true')
parser.add_argument('-u', '--update', choices=["rmsprop"])
parser.add_argument('-f', '--finish', type=int)
parser.add_argument('-t', '--duration', type=int)
parser.add_argument('-a', '--augmentation', action='store_true')
parser.add_argument('--port', default= 5557, type=int)
args = parser.parse_args(args)
image_size = (128,128)
if args.mnist:
train, test = get_mnist()
net = net_mnist()
else:
net = net_dvc(image_size)
if args.parallel:
sources = ('image_features','targets')
train = ServerDataStream(sources, True, port=args.port)
valid = ServerDataStream(sources, True, port=args.port+1)
test = ServerDataStream(sources, True, port=args.port+2)
else:
train, valid , test = get_dvc(image_size, shortcut = args.small, augmentation = args.augmentation)
train_net(net, train, test, **vars(args))
def main(args):
print(args)
parser = argparse.ArgumentParser(description='train')
parser.add_argument('-p', '--parallel', action='store_true')
parser.add_argument('-m', '--mnist', action='store_true')
parser.add_argument('--L1', type=float)
parser.add_argument('--L2', type=float)
parser.add_argument('-e', '--early_stopping', action='store_true')
parser.add_argument('-d', '--dropout', action='store_true')
parser.add_argument('-j', '--jobid')
parser.add_argument('-s', '--small', action='store_true')
parser.add_argument('-u', '--update', choices=["rmsprop"])
parser.add_argument('-f', '--finish', type=int)
parser.add_argument('-t', '--duration', type=int)
parser.add_argument('-a', '--augmentation', action='store_true')
parser.add_argument('--port', default=5557, type=int)
args = parser.parse_args(args)
image_size = (128, 128)
if args.mnist:
train, test = get_mnist()
net = net_mnist()
else:
net = net_dvc(image_size)
if args.parallel:
sources = ('image_features', 'targets')
train = ServerDataStream(sources, True, port=args.port)
valid = ServerDataStream(sources, True, port=args.port + 1)
test = ServerDataStream(sources, True, port=args.port + 2)
else:
train, valid, test = get_dvc(image_size,
shortcut=args.small,
augmentation=args.augmentation)
train_net(net, train, test, **vars(args))
def main(argv):
name = argv[1]
files = map(lambda p: join(folder, p), listdir(folder))
file = next(filter(lambda n: name in n, files))
print(file)
p = load_parameter_values(file)
net = net_dvc((128,128))
x = tensor.tensor4('image_features')
y_hat = net.apply(x)
g = Model(y_hat)
for k,v in p.items():
p[k] = v.astype('float32')
g.set_parameter_values(p)
a,t,v = get_dvc((128,128),trainning=False, shortcut=False)
run = function([x], y_hat)
def run_test(data):
res = []
for i in data.get_epoch_iterator():
res.extend(run(i[0]))
return res
def max_index(l):
if l[0] > l[1]:
return 0
else:
return 1
def write_kaggle(f, l):
f.write("id,label\n")
for i,e in enumerate(l,start=1):
f.write(str(i)+","+str(e)+"\n")
def kaggle(file, data):
write_kaggle(file,map(max_index, run_test(data)))
def accuracy(data):
res = []
true = []
for i in data.get_epoch_iterator():
res.extend(run(i[0]))
true.extend(i[1])
res = map(max_index, res)
total = 0
equal = 0
for r,t in zip(res,true):
total += 1
equal += 1 if r == t else 0
return equal/total
print("Training accuracy: ", accuracy(a))
print("Test accuracy: ", accuracy(v))
kaggle_file = join(result_folder, name+".kaggle")
print(kaggle_file)
with open(kaggle_file,'w') as f:
kaggle(f, t)
def main(argv):
name = argv[1]
files = map(lambda p: join(folder, p), listdir(folder))
file = next(filter(lambda n: name in n, files))
print(file)
p = load_parameter_values(file)
net = net_dvc((128, 128))
x = tensor.tensor4('image_features')
y_hat = net.apply(x)
g = Model(y_hat)
for k, v in p.items():
p[k] = v.astype('float32')
g.set_parameter_values(p)
a, t, v = get_dvc((128, 128), trainning=False, shortcut=False)
run = function([x], y_hat)
def run_test(data):
res = []
for i in data.get_epoch_iterator():
res.extend(run(i[0]))
return res
def max_index(l):
if l[0] > l[1]:
return 0
else:
return 1
def write_kaggle(f, l):
f.write("id,label\n")
for i, e in enumerate(l, start=1):
f.write(str(i) + "," + str(e) + "\n")
def kaggle(file, data):
write_kaggle(file, map(max_index, run_test(data)))
def accuracy(data):
res = []
true = []
for i in data.get_epoch_iterator():
res.extend(run(i[0]))
true.extend(i[1])
res = map(max_index, res)
total = 0
equal = 0
for r, t in zip(res, true):
total += 1
equal += 1 if r == t else 0
return equal / total
print("Training accuracy: ", accuracy(a))
print("Test accuracy: ", accuracy(v))
kaggle_file = join(result_folder, name + ".kaggle")
print(kaggle_file)
with open(kaggle_file, 'w') as f:
kaggle(f, t)
def tuple_parse(string):
try:
x, y = map(int, string.split(','))
return x, y
except:
raise argparse.ArgumentTypeError("Need a width and height")
def start_server(i, stream):
port = 5557 + i
print("starting server ", port)
fuel_start_server(stream, port)
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description='Open a datastream for the cat and dog dataset')
parser.add_argument('--image_size',
type=tuple_parse,
help='(width, height) for the resize')
parser.add_argument('--batch_size',
type=int,
help='Set the number of exemple per batch')
args = vars(parser.parse_args())
args = {k: v for k, v in args.items() if v is not None}
streams = get_dvc(**args)
for t in enumerate(streams):
Thread(target=start_server, args=t).start()
from datastream import get_dvc
import argparse
from threading import Thread
from fuel.server import start_server as fuel_start_server
def tuple_parse(string):
try:
x, y = map(int, string.split(','))
return x, y
except:
raise argparse.ArgumentTypeError("Need a width and height")
def start_server(i,stream):
port = 5557 + i
print("starting server " , port)
fuel_start_server(stream, port)
if __name__=="__main__":
parser = argparse.ArgumentParser(description='Open a datastream for the cat and dog dataset')
parser.add_argument('--image_size', type=tuple_parse, help='(width, height) for the resize')
parser.add_argument('--batch_size', type=int, help='Set the number of exemple per batch')
args = vars(parser.parse_args())
args= {k:v for k,v in args.items() if v is not None}
streams = get_dvc(**args)
for t in enumerate(streams):
Thread(target=start_server, args = t).start()
