N = 1000
X = np.zeros((N, 2))
X[:, 0] = np.linspace(-1, 1, N)
X[:, 1] = np.sin(2 * np.pi * np.linspace(-1, 1, N))
print X.shape
from dae_untied_weights import DAE_untied_weights
mydae = DAE_untied_weights(n_inputs=2,
n_hiddens=32,
act_func=['tanh', 'tanh'],
want_plus_x=False)
mydae.fit_with_decreasing_noise(X, [1.0, 0.5, 0.2, 0.1, 0.05, 0.01, 0.001], {
'method': 'fmin_bfgs',
'maxiter': 5000
})
clean_data = X
import matplotlib
matplotlib.use('Agg')
import pylab
def plot_grid_reconstruction_grid(mydae,
outputfile,
plotgrid_N_buckets=30,
window_width=1.0,
center=(0.0, 0.0)):
X = np.zeros((N,2))
X[:,0] = np.linspace(-1,1,N)
X[:,1] = np.sin( 2* np.pi * np.linspace(-1,1,N) )
print X.shape
from dae_untied_weights import DAE_untied_weights
mydae = DAE_untied_weights(n_inputs = 2,
n_hiddens = 32,
act_func = ['tanh', 'tanh'],
want_plus_x = False)
mydae.fit_with_decreasing_noise(X,
[1.0, 0.5, 0.2, 0.1, 0.05, 0.01, 0.001],
{'method' : 'fmin_bfgs',
'maxiter' : 5000})
clean_data = X
import matplotlib
matplotlib.use('Agg')
import pylab
def plot_grid_reconstruction_grid(mydae, outputfile, plotgrid_N_buckets = 30, window_width = 1.0, center = (0.0,0.0)):
(plotgrid_X, plotgrid_Y) = np.meshgrid(np.arange(center[0] - window_width,
center[0] + window_width,
2 * window_width / plotgrid_N_buckets),
np.arange(center[1] - window_width,
from dae_untied_weights import DAE_untied_weights
mydae = DAE_untied_weights(n_inputs=784,
n_hiddens=1024,
act_func=['tanh', 'sigmoid'])
#mydae.fit_with_decreasing_noise(mnist_train_data[0:2000,:],
# [0.1, 0.05, 0.01, 0.001],
# {'method' : 'fmin_cg',
# 'maxiter' : 500,
# 'gtol':0.001})
mydae.fit_with_decreasing_noise(mnist_train_data[0:2000, :], [0.1, 0.05, 0.01],
{
'method': 'fmin_l_bfgs_b',
'maxiter': 500,
'm': 25
})
print mydae.s
pkl_file = "/u/alaingui/umontreal/denoising_autoencoder/mcmc_pof/trained_models/mydae_2013_02_08.pkl"
cPickle.dump(
{
'Wc': mydae.Wc,
'Wb': mydae.Wb,
'b': mydae.b,
'c': mydae.c,
's': mydae.s,
'act_func': mydae.act_func
}, open(pkl_file, "w"))
import cPickle
mnist_dataset = cPickle.load(open('/data/lisa/data/mnist/mnist.pkl', 'rb'))
mnist_train = mnist_dataset[0]
mnist_train_data, mnist_train_labels = mnist_train
from dae_untied_weights import DAE_untied_weights
mydae = DAE_untied_weights(n_inputs = 784,
n_hiddens = 1024,
act_func = ['tanh', 'sigmoid'])
#mydae.fit_with_decreasing_noise(mnist_train_data[0:2000,:],
# [0.1, 0.05, 0.01, 0.001],
# {'method' : 'fmin_cg',
# 'maxiter' : 500,
# 'gtol':0.001})
mydae.fit_with_decreasing_noise(mnist_train_data[0:2000,:],
[0.1, 0.05, 0.01],
{'method' : 'fmin_l_bfgs_b',
'maxiter' : 500,
'm':25})
print mydae.s
pkl_file = "/u/alaingui/umontreal/denoising_autoencoder/mcmc_pof/trained_models/mydae_2013_02_08.pkl"
cPickle.dump({'Wc':mydae.Wc, 'Wb':mydae.Wb, 'b':mydae.b, 'c':mydae.c, 's':mydae.s, 'act_func':mydae.act_func},
open(pkl_file, "w"))
