from da import DenoisingAutoencoder from dataset import Dataset import theano.tensor as T import numpy if __name__ == "__main__": fname = "/data/lisa/data/mnist/mnist_all.pickle" # fname = "/data/lisa/data/pentomino/" ds = Dataset() ds.setup_dataset(data_path=fname, train_split_scale=0.8) x_data = ds.Xtrain input = T.dmatrix("x_input") weights_file = "../out/dae_mnist_weights.npy" recons_file = "../out/dae_mnist_recons.npy" rnd = numpy.random.RandomState(1231) dae = DenoisingAutoencoder(input, nvis=28 * 28, nhid=600, rnd=rnd) dae.fit(learning_rate=0.1, data=x_data, weights_file=weights_file, n_epochs=100, recons_img_file=recons_file)
input = T.fmatrix("x_input") ds = Dataset() data = ds._get_data(fname) x_data = numpy.asarray(data[0][0:42000], dtype=theano.config.floatX) weights_file_l1 = "dae_mnist_weights_l1.npy" weights_file_l2 = "dae_mnist_weights_l2.npy" rnd = numpy.random.RandomState(1231) nhid_l1 = 800 nhid_l2 = 800 dae_l1 = DenoisingAutoencoder(input, nvis=28*28, nhid=nhid_l1, L1_reg=9*1e-5, L2_reg=7*1e-4, rnd=rnd) #std_data = standardize(x_data) std_data = numpy.asarray(x_data, dtype=theano.config.floatX) dae_l1.fit(learning_rate=9.96*1e-3, shuffle_data=True, data=std_data, weights_file=weights_file_l1, recons_img_file=None, corruption_level=0.095, batch_size=40, n_epochs=1400)
from da import DenoisingAutoencoder from dataset import Dataset import theano.tensor as T import numpy if __name__ == "__main__": fname = "/data/lisa/data/pentomino/pento64x64_40k_seed_5365102867_64patches.npy" ds = Dataset() ds.setup_dataset(data_path=fname, train_split_scale=0.4) x_data = ds.Xtrain input = T.dmatrix("x_input") rnd = numpy.random.RandomState(1231) dae = DenoisingAutoencoder(input, nvis=64 * 64, nhid=1500, rnd=rnd) dae.fit(data=x_data, learning_rate=0.04, n_epochs=32, weights_file="out/dae_weights_pento.npy")
from da import DenoisingAutoencoder from dataset import Dataset import theano.tensor as T import numpy if __name__ == "__main__": fname = "/data/lisa/data/mnist/mnist_all.pickle" #fname = "/data/lisa/data/pentomino/" ds = Dataset() ds.setup_dataset(data_path=fname, train_split_scale=0.8) x_data = ds.Xtrain input = T.dmatrix("x_input") weights_file = "../out/dae_mnist_weights.npy" recons_file = "../out/dae_mnist_recons.npy" rnd = numpy.random.RandomState(1231) dae = DenoisingAutoencoder(input, nvis=28 * 28, nhid=600, rnd=rnd) dae.fit(learning_rate=0.1, data=x_data, weights_file=weights_file, n_epochs=100, recons_img_file=recons_file)
from da import DenoisingAutoencoder from dataset import Dataset import theano.tensor as T import numpy if __name__=="__main__": fname = "/data/lisa/data/pentomino/pento64x64_40k_seed_5365102867_64patches.npy" ds = Dataset() ds.setup_dataset(data_path=fname, train_split_scale=0.4) x_data = ds.Xtrain input = T.dmatrix("x_input") rnd = numpy.random.RandomState(1231) dae = DenoisingAutoencoder(input, nvis=64*64, nhid=1500, rnd=rnd) dae.fit(data=x_data, learning_rate=0.04, n_epochs=32, weights_file="out/dae_weights_pento.npy")