from cle.cle.train import Training from cle.cle.train.ext import (EpochCount, GradientClipping, Monitoring, Picklize) from cle.cle.train.opt import Adam from cle.cle.utils import flatten, OrderedDict from cle.datasets.bouncing_balls import BouncingBalls data_path = '/home/junyoung/data/bouncing_balls/bouncing_ball_2balls_16wh_20len_50000cases.npy' save_path = '/home/junyoung/src/cle/saved/' batch_size = 128 frame_size = 256 debug = 0 model = Model() train_data = BouncingBalls(name='train', path=data_path) valid_data = BouncingBalls(name='valid', path=data_path) init_W = InitCell('randn') init_U = InitCell('ortho') init_b = InitCell('zeros') x, y = train_data.theano_vars() if debug: x.tag.test_value = np.zeros((10, batch_size, frame_size), dtype=np.float32) y.tag.test_value = np.zeros((10, batch_size, frame_size), dtype=np.float32) h1 = LSTM(name='h1', parent=['x'], parent_dim=[frame_size],
from cle.cle.train.opt import Adam from cle.cle.utils import unpack, OrderedDict from cle.datasets.bouncing_balls import BouncingBalls #data_path = '/data/lisatmp3/chungjun/bouncing_balls/bouncing_ball_2balls_16wh_20len_50000cases.npy' #save_path = '/u/chungjun/repos/cle/saved/' data_path = '/home/junyoung/data/bouncing_balls/bouncing_ball_2balls_16wh_20len_50000cases.npy' save_path = '/home/junyoung/repos/cle/saved/' batch_size = 128 res = 256 debug = 0 model = Model() trdata = BouncingBalls(name='train', path=data_path) init_W = InitCell('randn') init_U = InitCell('ortho') init_b = InitCell('zeros') model.inputs = trdata.theano_vars() x, y = model.inputs if debug: x.tag.test_value = np.zeros((10, batch_size, res), dtype=np.float32) y.tag.test_value = np.zeros((10, batch_size, res), dtype=np.float32) inputs = [x, y] inputs_dim = {'x':256, 'y':256} h1 = GFLSTM(name='h1',
Picklize ) from cle.cle.train.opt import Adam from cle.cle.utils import init_tparams, OrderedDict from cle.datasets.bouncing_balls import BouncingBalls data_path = '/data/lisatmp3/chungjun/bouncing_balls/bouncing_ball_2balls_16wh_20len_50000cases.npy' save_path = '/u/chungjun/repos/cle/saved/' batch_size = 128 frame_size = 256 debug = 0 model = Model() train_data = BouncingBalls(name='train', path=data_path) valid_data = BouncingBalls(name='valid', path=data_path) x, y = train_data.theano_vars() if debug: x.tag.test_value = np.zeros((10, batch_size, frame_size), dtype=np.float32) y.tag.test_value = np.zeros((10, batch_size, frame_size), dtype=np.float32) init_W = InitCell('randn') init_U = InitCell('ortho') init_b = InitCell('zeros') h1 = GFLSTM(name='h1',
Picklize) from cle.cle.train.opt import Adam from cle.cle.utils import unpack, OrderedDict from cle.datasets.bouncing_balls import BouncingBalls #data_path = '/data/lisatmp3/chungjun/bouncing_balls/bouncing_ball_2balls_16wh_20len_50000cases.npy' #save_path = '/u/chungjun/repos/cle/saved/' data_path = '/home/junyoung/data/bouncing_balls/bouncing_ball_2balls_16wh_20len_50000cases.npy' save_path = '/home/junyoung/repos/cle/saved/' batch_size = 128 res = 256 debug = 0 model = Model() trdata = BouncingBalls(name='train', path=data_path) init_W = InitCell('randn') init_U = InitCell('ortho') init_b = InitCell('zeros') model.inputs = trdata.theano_vars() x, y = model.inputs if debug: x.tag.test_value = np.zeros((10, batch_size, res), dtype=np.float32) y.tag.test_value = np.zeros((10, batch_size, res), dtype=np.float32) inputs = [x, y] inputs_dim = {'x': 256, 'y': 256} h1 = GFLSTM(name='h1',
from cle.datasets.bouncing_balls import BouncingBalls #data_path = '$your_data_path' data_path = '/data/lisatmp3/chungjun/bouncing_balls/bouncing_ball_2balls_16wh_20len_50000cases.npy' #save_path = '$your_model_path' save_path = '/u/chungjun/src/cle/saved/' #pkl_name = '$your_model_name' pkl_name = 'toy_bb_lstm.pkl' frame_size = 256 # How many examples you want to proceed at a time batch_size = 100 debug = 0 test_data = BouncingBalls(name='test', path=data_path) x = test_data.theano_vars() if debug: x.tag.test_value = np.zeros((15, batch_size, frame_size), dtype=np.float32) exp = unpickle(save_path + pkl_name) nodes = exp.model.nodes names = [node.name for node in nodes] [h1, h2, h3, h4] = nodes s1_0 = h1.get_init_state(batch_size) s2_0 = h2.get_init_state(batch_size) s3_0 = h3.get_init_state(batch_size)
from cle.cle.utils import unpickle, tolist, OrderedDict from cle.datasets.bouncing_balls import BouncingBalls #data_path = '$your_data_path' data_path = '/data/lisatmp3/chungjun/bouncing_balls/bouncing_ball_2balls_16wh_20len_50000cases.npy' #save_path = '$your_model_path' save_path = '/u/chungjun/src/cle/saved/' #pkl_name = '$your_model_name' pkl_name = 'toy_bb_lstm.pkl' frame_size = 256 # How many examples you want to proceed at a time batch_size = 100 debug = 0 test_data = BouncingBalls(name='test', path=data_path) x = test_data.theano_vars() if debug: x.tag.test_value = np.zeros((15, batch_size, frame_size), dtype=np.float32) exp = unpickle(save_path + pkl_name) nodes = exp.model.nodes names = [node.name for node in nodes] [h1, h2, h3, h4] = nodes s1_0 = h1.get_init_state(batch_size) s2_0 = h2.get_init_state(batch_size) s3_0 = h3.get_init_state(batch_size)
from cle.cle.train.opt import Adam from cle.cle.utils import unpack, OrderedDict from cle.datasets.bouncing_balls import BouncingBalls #data_path = '/data/lisatmp3/chungjun/bouncing_balls/bouncing_ball_2balls_16wh_20len_50000cases.npy' #save_path = '/u/chungjun/repos/cle/saved/' data_path = '/home/junyoung/data/bouncing_balls/bouncing_ball_2balls_16wh_20len_50000cases.npy' save_path = '/home/junyoung/repos/cle/saved/' batch_size = 128 res = 256 debug = 0 model = Model() train_data = BouncingBalls(name='train', path=data_path) # Choose the random initialization method init_W = InitCell('randn') init_U = InitCell('ortho') init_b = InitCell('zeros') # Define nodes: objects model.inputs = train_data.theano_vars() x, y = model.inputs # You must use THEANO_FLAGS="compute_test_value=raise" python -m ipdb if debug: x.tag.test_value = np.zeros((10, batch_size, res), dtype=np.float32) y.tag.test_value = np.zeros((10, batch_size, res), dtype=np.float32) inputs = [x, y]