def show(self):
"""
.. todo::
WRITEME
"""
#image.imview_async(self.image)
show(self.image)
def explore(X,y):
for i in xrange(X.shape[0]):
patch = X[i,:].reshape(28,28)
print y[i]
show(patch)
x = raw_input('waiting...')
if x == 'n':
return
if x == 'q':
quit(-1)
def show(self):
#image.imview_async(self.image)
show(self.image)
r = 6
c = 6
dataset = CIFAR10(which_set = 'train', one_hot = True, gcn = 55.)
ten4 = dataset.get_batch_topo(m)
from pylearn2.utils import sharedX
ten4th = sharedX(ten4)
X = cifar10neighbs(ten4, (r,c))
from theano import function
X = function([],X)()
print X.shape
from pylearn2.gui.patch_viewer import make_viewer
from pylearn2.utils.image import show
stride = (32-r+1)*(32-c+1)
for i in xrange(m):
ten4v =ten4[i,:,:,:]
ten4v -= ten4v.min()
ten4v /= ten4v.max()
show(ten4v)
patch_viewer = make_viewer(X[i*stride:(i+1)*stride], is_color= True)
patch_viewer.show()
print 'waiting...'
x = raw_input()
def show(self):
show(self.image)
H[i*batch_size:(i+1)*batch_size,:] = f(X)
h = H.mean(axis=0)
proj = np.dot(H,h)
#sort examples by projection along the mean vector
ranking = sorted(zip(proj,range(proj.shape[0])))
new_H = H.copy()
for i, t in enumerate(ranking):
new_H[i,:] = H[t[1],:]
H = new_H
#sort units by mean activation
ranking = sorted(zip(h,range(h.shape[0])))
new_H = H.copy()
for i, t in enumerate(ranking):
new_H[:,i] = H[:,t[1]]
H = new_H
from pylearn2.utils import image
image.show(H)
import numpy as np
from pylearn2.utils import image
imbase = '/data/lisatmp/goodfeli/esp/final_images'
ims = sorted(os.listdir(imbase))
for label, im in zip(labels, ims):
stem = label.split('.')[0]
assert stem in im
img = image.load(imbase + '/' + im)
image.show(img)
full_label_path = labels_dir + '/' + label
print 'True labels:'
fd = open(full_label_path,'r')
print fd.read()
fd.close()
full_l2_path = l2_path + '/' + label.split('.')[0] + '.npy'
l2 = np.load(full_l2_path)
y = f(l2)
print 'Predicted labels: '
print y.shape
f = function([X], features)
for i in xrange(num_batches):
X = dataset.get_batch_design(batch_size)
H[i * batch_size:(i + 1) * batch_size, :] = f(X)
h = H.mean(axis=0)
proj = np.dot(H, h)
#sort examples by projection along the mean vector
ranking = sorted(zip(proj, range(proj.shape[0])))
new_H = H.copy()
for i, t in enumerate(ranking):
new_H[i, :] = H[t[1], :]
H = new_H
#sort units by mean activation
ranking = sorted(zip(h, range(h.shape[0])))
new_H = H.copy()
for i, t in enumerate(ranking):
new_H[:, i] = H[:, t[1]]
H = new_H
from pylearn2.utils import image
image.show(H)
l2_path = '/data/lisatmp/goodfeli/esp/final_l2'
import numpy as np
from pylearn2.utils import image
imbase = '/data/lisatmp/goodfeli/esp/final_images'
ims = sorted(os.listdir(imbase))
for label, im in zip(labels, ims):
stem = label.split('.')[0]
assert stem in im
img = image.load(imbase + '/' + im)
image.show(img)
full_label_path = labels_dir + '/' + label
print 'True labels:'
fd = open(full_label_path, 'r')
print fd.read()
fd.close()
full_l2_path = l2_path + '/' + label.split('.')[0] + '.npy'
l2 = np.load(full_l2_path)
y = f(l2)
print 'Predicted labels: '
print y.shape
def show(self):
#image.imview_async(self.image)
show(self.image)
r = 6
c = 6
dataset = CIFAR10(which_set='train', one_hot=True, gcn=55.)
ten4 = dataset.get_batch_topo(m)
from pylearn2.utils import sharedX
ten4th = sharedX(ten4)
X = cifar10neighbs(ten4, (r, c))
from theano import function
X = function([], X)()
print X.shape
from pylearn2.gui.patch_viewer import make_viewer
from pylearn2.utils.image import show
stride = (32 - r + 1) * (32 - c + 1)
for i in xrange(m):
ten4v = ten4[i, :, :, :]
ten4v -= ten4v.min()
ten4v /= ten4v.max()
show(ten4v)
patch_viewer = make_viewer(X[i * stride:(i + 1) * stride], is_color=True)
patch_viewer.show()
print 'waiting...'
x = raw_input()
grad = T.grad(g, X)
step_X = X + alpha * grad
norm = T.sqrt(T.sqr(X).sum())
renormed_X = step_X #/ norm
from theano import function
f = function([], [g, norm], updates={X: renormed_X})
while True:
a, b = f()
print a, ' ', b
if a > .75:
break
from pylearn2.utils import image
from pylearn2.config import yaml_parse
dataset = yaml_parse.load(dataset_yaml_src)
X = dataset.get_topological_view(X.get_value())
X /= np.abs(X).max()
image.show(X[0, ...])
act = p[0,filter_idx,i,j]
obj = - act + norm_penalty * T.square(X).sum()
assert obj.ndim == 0
optimizer = BatchGradientDescent(objective = obj,
params = [X],
inputs = None,
param_constrainers = None,
max_iter = 1000,
verbose = True,
tol = None,
init_alpha = (.001, .005, .01, .05, .1))
optimizer.minimize()
img = X.get_value()[0,:,:,:]
print 'max mag: ',np.abs(img).max()
print 'norm: ',np.square(img).sum()
print 'min: ',img.min()
print 'max: ',img.max()
img /= np.abs(img).max()
img *= .5
img += 1
show(img)
for j in xrange(mr):
for k in xrange(mc):
r = 0.
g = 0.
b = 0.
r_count = 0
g_count = 0
b_count = 0
for l in xrange(ch):
elem = convmap[i,j,k,l]
if elem < 32:
r_count += 1
r += float(elem)/31.
if elem >= 32 and elem < 64:
g_count += 1
g += float(elem-32)/31.
if elem >= 64:
b_count += 1
b += float(elem-64)/float(m)
decoded_map[i,j,k,:] = np.asarray( [ r/(1e-12+float(r_count)),
g/(1e-12+float(g_count)),
b/(1e-12+float(b_count))])
for i in xrange(m):
show(decoded_img[i,:,:,:])
show(decoded_map[i,:,:,:])
x = raw_input()
norm = T.sqrt(T.sqr(X).sum())
renormed_X = step_X #/ norm
from theano import function
f = function([],[g,norm],updates = { X : renormed_X })
while True:
a,b = f()
print a,' ',b
if a > .75:
break
from pylearn2.utils import image
from pylearn2.config import yaml_parse
dataset = yaml_parse.load( dataset_yaml_src )
X = dataset.get_topological_view( X.get_value())
X /= np.abs(X).max()
image.show(X[0,...])
from pylearn2.optimization.batch_gradient_descent import BatchGradientDescent
bgd = BatchGradientDescent(objective=-neuron,
params=[X],
inputs=None,
max_iter=100,
lr_scalers=None,
verbose=3,
tol=None,
init_alpha=None,
min_init_alpha=1e-3,
reset_alpha=True,
conjugate=True,
gradients=None,
gradient_updates=None,
accumulate=False,
theano_function_mode=None,
param_constrainers=None)
bgd.minimize()
X = normed.eval()[:,:,:,0].transpose(1,2,0)
import numpy as np
X /= np.abs(X).max()
print (X.min(), X.max())
from pylearn2.utils.image import show
show(X)
