def generation_on_a_line(net,
n_points=100,
imsz=[28, 28],
nrows=10,
h_seeds=None):
if h_seeds is None:
h = net.sample_hiddens(2)
z = gnp.zeros((n_points, h.shape[1]))
diff = h[1] - h[0]
step = diff / (n_points - 1)
for i in range(n_points):
z[i] = h[0] + step * i
else:
n_seeds = h_seeds.shape[0]
z = gnp.zeros((n_points * n_seeds, h_seeds.shape[1]))
for i in range(n_seeds):
h0 = h_seeds[i]
h1 = h_seeds[(i + 1) % n_seeds]
diff = h1 - h0
step = diff / (n_points - 1)
for j in range(n_points):
z[i * n_points + j] = h0 + step * j
x = net.generate_samples(z=z)
vt.bwpatchview(x.asarray(), imsz, nrows, rowmajor=True, gridintensity=1)
def view_checkpoints(model_dir, sigma, imsz=[28, 28], figid=101):
"""
checkpoint files should have a name matching the following:
<model_dir>/checkpoint_<sigma>_<iter>.pdata
"""
prefix = '%s/checkpoint_%s' % (model_dir, str(sigma))
checkpoint_numbers = sorted([
int(fpath.split('.')[0].split('_')[-1])
for fpath in os.listdir(model_dir)
if fpath.startswith('checkpoint_%s' % str(sigma))
])
net = gen.StochasticGenerativeNet()
plt.figure(figid, figsize=(10, 8))
ax = plt.subplot(111)
for i in checkpoint_numbers:
net.load_model_from_file(prefix + '_%d.pdata' % i)
w = net.layers[-1].params.W.asarray()
ax.cla()
vt.bwpatchview(w[:400],
imsz,
int(np.sqrt(w[:400].shape[0])),
rowmajor=True,
gridintensity=1,
ax=ax)
plt.draw()
plt.show()
print 'Checkpoint %d' % i
time.sleep(0.04)
def generate_samples(dataset='mnist', mode='input_space'):
imsz = [28,28] if dataset=='mnist' else [48,48]
net = get_model(dataset=dataset, mode=mode)
plt.figure()
vt.bwpatchview(net.generate_samples(n_samples=30).asarray(), imsz, 5, gridintensity=1)
if not os.path.exists('figs'):
os.makedirs('figs')
plt.savefig('figs/samples_%s_%s.pdf' % (dataset, mode), bbox_inches='tight')
def generate_samples(dataset='mnist', mode='input_space'):
imsz = [28, 28] if dataset == 'mnist' else [48, 48]
net = get_model(dataset=dataset, mode=mode)
plt.figure()
vt.bwpatchview(net.generate_samples(n_samples=30).asarray(),
imsz,
5,
gridintensity=1)
if not os.path.exists('figs'):
os.makedirs('figs')
plt.savefig('figs/samples_%s_%s.pdf' % (dataset, mode),
bbox_inches='tight')
def generation_on_a_line(net, n_points=100, imsz=[28,28], nrows=10, h_seeds=None):
if h_seeds is None:
h = net.sample_hiddens(2)
z = gnp.zeros((n_points, h.shape[1]))
diff = h[1] - h[0]
step = diff / (n_points - 1)
for i in range(n_points):
z[i] = h[0] + step * i
else:
n_seeds = h_seeds.shape[0]
z = gnp.zeros((n_points * n_seeds, h_seeds.shape[1]))
for i in range(n_seeds):
h0 = h_seeds[i]
h1 = h_seeds[(i+1) % n_seeds]
diff = h1 - h0
step = diff / (n_points - 1)
for j in range(n_points):
z[i*n_points+j] = h0 + step * j
x = net.generate_samples(z=z)
vt.bwpatchview(x.asarray(), imsz, nrows, rowmajor=True, gridintensity=1)
def view_checkpoints(model_dir, sigma, imsz=[28,28], figid=101):
"""
checkpoint files should have a name matching the following:
<model_dir>/checkpoint_<sigma>_<iter>.pdata
"""
prefix = '%s/checkpoint_%s' % (model_dir, str(sigma))
checkpoint_numbers = sorted([int(fpath.split('.')[0].split('_')[-1]) for fpath in os.listdir(model_dir) if fpath.startswith('checkpoint_%s' % str(sigma))])
net = gen.StochasticGenerativeNet()
plt.figure(figid, figsize=(10,8))
ax = plt.subplot(111)
for i in checkpoint_numbers:
net.load_model_from_file(prefix + '_%d.pdata' % i)
w = net.layers[-1].params.W.asarray()
ax.cla()
vt.bwpatchview(w[:400], imsz, int(np.sqrt(w[:400].shape[0])), rowmajor=True, gridintensity=1, ax=ax)
plt.draw()
plt.show()
print 'Checkpoint %d' % i
time.sleep(0.04)
def nn_search(samples,
database,
top_k=1,
imsz=[28, 28],
orientation='horizontal',
output_file=None,
pad=0.1):
if orientation not in ['horizontal', 'vertical']:
print '[Error] orientation must be either horizontal or vertical'
return
g_samples = util.to_garray(samples)
g_database = util.to_garray(database)
if isinstance(database, gnp.garray):
database = database.asarray()
if isinstance(samples, gnp.garray):
samples = samples.asarray()
n_samples, n_dims = samples.shape
nn = np.empty((n_samples * top_k, n_dims), dtype=np.float)
for i in range(n_samples):
v = g_samples[i]
d = ((g_database - v)**2).sum(axis=1)
idx = d.asarray().argsort()
top_candidates = database[idx[:top_k]]
if orientation == 'horizontal':
nn[np.arange(i, i + n_samples * top_k, n_samples)] = top_candidates
elif orientation == 'vertical':
nn[i * top_k:(i + 1) * top_k] = top_candidates
f = plt.figure()
grid = AxesGrid(f, 111, nrows_ncols=(2, 1), axes_pad=pad)
vt.bwpatchview(samples, imsz, 1, gridintensity=1, ax=grid[0])
if orientation == 'horizontal':
vt.bwpatchview(nn, imsz, top_k, gridintensity=1, ax=grid[1])
elif orientation == 'vertical':
vt.bwpatchview(nn, imsz, n_samples, gridintensity=1, ax=grid[1])
if output_file is not None:
plt.savefig(output_file, bbox_inches='tight')
def nn_search(samples, database, top_k=1, imsz=[28,28], orientation='horizontal', output_file=None, pad=0.1):
if orientation not in ['horizontal', 'vertical']:
print '[Error] orientation must be either horizontal or vertical'
return
g_samples = util.to_garray(samples)
g_database = util.to_garray(database)
if isinstance(database, gnp.garray):
database = database.asarray()
if isinstance(samples, gnp.garray):
samples = samples.asarray()
n_samples, n_dims = samples.shape
nn = np.empty((n_samples * top_k, n_dims), dtype=np.float)
for i in range(n_samples):
v = g_samples[i]
d = ((g_database - v)**2).sum(axis=1)
idx = d.asarray().argsort()
top_candidates = database[idx[:top_k]]
if orientation == 'horizontal':
nn[np.arange(i, i+n_samples*top_k, n_samples)] = top_candidates
elif orientation == 'vertical':
nn[i*top_k:(i+1)*top_k] = top_candidates
f = plt.figure()
grid = AxesGrid(f, 111, nrows_ncols=(2,1), axes_pad=pad)
vt.bwpatchview(samples, imsz, 1, gridintensity=1, ax=grid[0])
if orientation == 'horizontal':
vt.bwpatchview(nn, imsz, top_k, gridintensity=1, ax=grid[1])
elif orientation == 'vertical':
vt.bwpatchview(nn, imsz, n_samples, gridintensity=1, ax=grid[1])
if output_file is not None:
plt.savefig(output_file, bbox_inches='tight')