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()