from cle.cle.train import Training from cle.cle.train.ext import (EpochCount, GradientClipping, Monitoring, Picklize, EarlyStopping) from cle.cle.train.opt import RMSProp from cle.cle.utils import error, flatten, predict, OrderedDict from cle.datasets.mnist import MNIST # Set your dataset data_path = '/home/junyoung/data/mnist/mnist.pkl' save_path = '/home/junyoung/src/cle/saved/' batch_size = 128 debug = 0 model = Model() train_data = MNIST(name='train', path=data_path) valid_data = MNIST(name='valid', path=data_path) # Choose the random initialization method init_W = InitCell('randn') init_b = InitCell('zeros') # Define nodes: objects x, y = train_data.theano_vars() # You must use THEANO_FLAGS="compute_test_value=raise" python -m ipdb if debug: x.tag.test_value = np.zeros((batch_size, 784), dtype=np.float32) y.tag.test_value = np.zeros((batch_size, 1), dtype=np.float32) h1 = FullyConnectedLayer(name='h1',
from cle.cle.train.opt import Adam from cle.cle.utils import flatten from cle.cle.utils.compat import OrderedDict from cle.datasets.mnist import MNIST datapath = '/home/junyoung/data/mnist/mnist_binarized_salakhutdinov.pkl' savepath = '/home/junyoung/repos/cle/saved/' batch_size = 100 input_dim = 784 latent_dim = 100 n_steps = 64 debug = 0 model = Model() data = MNIST(name='train', unsupervised=1, path=datapath) init_W = InitCell('rand') init_U = InitCell('ortho') init_b = InitCell('zeros') init_b_sig = InitCell('const', mean=0.6) x, _ = data.theano_vars() if debug: x.tag.test_value = np.zeros((batch_size, 784), dtype=np.float32) error = ErrorLayer(name='error', parent=['x'], recurrent=['canvas'], batch_size=batch_size)