def main():
mlp_hiddens = [1000]
filter_sizes = [(9, 9), (5, 5), (5, 5)]
feature_maps = [80, 50, 20]
pooling_sizes = [(3, 3), (2, 2), (2, 2)]
save_to = "DvC.pkl"
image_size = (128, 128)
output_size = 2
learningRate = 0.1
num_epochs = 300
num_batches = None
if socket.gethostname() == 'tim-X550JX':
host_plot = 'http://*****:*****@ %s' %
('CNN ', datetime.datetime.now(), socket.gethostname()),
channels=[['train_error_rate', 'valid_error_rate'],
['train_total_gradient_norm']],
after_epoch=True,
server_url=host_plot))
model = Model(cost)
main_loop = MainLoop(algorithm,
stream_data_train,
model=model,
extensions=extensions)
main_loop.run()
def main():
mlp_hiddens = [1000]
filter_sizes = [(9,9),(5,5),(5,5)]
feature_maps = [80, 50, 20]
pooling_sizes = [(3,3),(2,2),(2,2)]
save_to="DvC.pkl"
image_size = (128, 128)
output_size = 2
learningRate=0.1
num_epochs=300
num_batches=None
if socket.gethostname()=='tim-X550JX':host_plot = 'http://*****:*****@ %s' % ('CNN ', datetime.datetime.now(), socket.gethostname()),
channels=[['train_error_rate', 'valid_error_rate'],
['train_total_gradient_norm']], after_epoch=True, server_url=host_plot))
model = Model(cost)
main_loop = MainLoop(
algorithm,
stream_data_train,
model=model,
extensions=extensions)
main_loop.run()
