from container import ModelContainer
from experiments.conv_test.train import construct
if __name__ == '__main__':
import sys # basic arg parsing, infer name
name = sys.argv[0].split('/')[-2]
if len(sys.argv) < 2:
print "Usage: submit weight_file"
sys.exit()
c = ModelContainer(name,construct(),10,"adam")
c.evaluate(str(sys.argv[1]))
fcn = Dropout(.5)(fcn)
fcn = Dense(128)(fcn)
fcn = Dense(1)(fcn)
return Model(input=inp, output=fcn)
datagen_args = dict(rotation_range=5.,
width_shift_range=0.1,
height_shift_range=0.1,
zoom_range=.1,
channel_shift_range=0.,
horizontal_flip=False)
if __name__ == '__main__':
import sys # basic arg parsing, infer name
if len(sys.argv) < 4:
print "Usage: train nb_epoch batch_size samples_per_epoch"
sys.exit()
name = sys.argv[0].split('/')[-2]
model = ModelContainer(name,
construct(),
"adam",
datagen_args=datagen_args)
model.train(nb_epoch=int(sys.argv[1]),
batch_size=int(sys.argv[2]),
samples_per_epoch=int(sys.argv[3]))