Python PatchViewer.show方法代碼示例

# 需要導入模塊: from pylearn2.gui.patch_viewer import PatchViewer [as 別名]
# 或者: from pylearn2.gui.patch_viewer.PatchViewer import show [as 別名]
    def on_monitor(self, *args, **kwargs):

        if not hasattr(self, 'record'):
            self.record = {}
            self.size = {}
            for dataset in self.datasets:
                assert tuple(dataset.view_converter.axes) == ('c', 0, 1, 'b')
                self.record[dataset] = dataset.get_topological_view().copy()
                self.size[dataset] = dataset.X.shape[0]
        else:
            for i, dataset in enumerate(self.datasets):
                size = self.size[dataset]
                assert dataset.X.shape[0] == size
                self.record[dataset] = np.concatenate((self.record[dataset], dataset.get_topological_view().copy()),
                        axis=-1)
                record_view = self.record[dataset].copy()
                record_view /= np.abs(record_view).max()
                pv = PatchViewer(grid_shape=(record_view.shape[3]/size, size),
                        patch_shape = record_view.shape[1:3], is_color = record_view.shape[0] == 3)
                for j in xrange(record_view.shape[3]):
                    pv.add_patch(np.transpose(record_view[:,:,:,j], (1, 2, 0)), rescale=False)
                print 'Dataset %d: ' % i
                pv.show()
                x = raw_input()

# 需要導入模塊: from pylearn2.gui.patch_viewer import PatchViewer [as 別名]
# 或者: from pylearn2.gui.patch_viewer.PatchViewer import show [as 別名]
for i in xrange(Wv.shape[1]-1):
    for j in xrange(i+1,Wv.shape[1]):
        dot = abs(np.dot(Wv[:,i],Wv[:,j]))
        to_sort.append( (-dot, (i,j) ) )


to_sort = sorted(to_sort)[0:100]

print -to_sort[0][0]
print -to_sort[99][0]

from pylearn2.config import yaml_parse

dataset = yaml_parse.load(model.dataset_yaml_src)

weights_view = dataset.get_weights_view(Wv.T)

from pylearn2.gui.patch_viewer import PatchViewer

pv = PatchViewer((100,2),(28,28),is_color=False)

for i in xrange(100):
    l, r = to_sort[i][1]
    l = weights_view[l]
    r = weights_view[r]
    pv.add_patch(l,rescale=True)
    pv.add_patch(r,rescale=True)

pv.show()

# 需要導入模塊: from pylearn2.gui.patch_viewer import PatchViewer [as 別名]
# 或者: from pylearn2.gui.patch_viewer.PatchViewer import show [as 別名]
F = T.matrix()
recons_func = function([F], model.energy_function.reconstruct(F))

def reconstruct(X, use_noise):

    corrupt_X = X.copy()
    if use_noise:
        noise = N.random.randn(*corrupt_X.shape)
        scaled_noise = noise * sigma
        corrupt_X += scaled_noise
    R = recons_func(corrupt_X)

    return X, corrupt_X, R

for i in range(0,rows):
    for j in range(0, examplesPerRow):
        x = dataset.get_batch_design(1)

        p.add_patch( reshape(x),rescale=True)
        truth, noise, reconstruction = reconstruct(x, use_noise = True)

        p.add_patch(reshape(truth ),rescale=True)
        p.add_patch(reshape(noise ), rescale=True)
        p.add_patch(reshape(reconstruction) , rescale=True)
        print ( 'mse', N.square(reconstruction-truth).mean(), \
                'mae', N.abs(reconstruction-truth).mean() )
        truth, noise, reconstruction = reconstruct(x, use_noise = False)
        p.add_patch( reshape(reconstruction) , rescale=True)
p.show()

# 需要導入模塊: from pylearn2.gui.patch_viewer import PatchViewer [as 別名]
# 或者: from pylearn2.gui.patch_viewer.PatchViewer import show [as 別名]
        pv4.add_patch(weights_view[cur_idx,:], rescale = True,
                activation = activation)

        if j >= filter_start and j < filter_start + num_filters:
            pv5.add_patch(weights_view[cur_idx,:], rescale = True,
                activation = activation)


    if feature_type == 'exp_h':
        print 'features are not rescaled; showing expectation of h without adjustment'
        topo_feat *= 2.
        topo_feat -= 1.
    else:
        print "features are contrast normalized so that each column uses 0.5 grey " \
                "for 0 and its maximal absolute value is at the edge of the dynamic range"
        for i in xrange(filter_start, filter_start + num_filters):
            topo_feat[:,:,:,idx[i]] /= np.abs(topo_feat[:,:,:,idx[i]]).max()

    for i in xrange(X.shape[0]):
        #indices of channels sorted in order of descending standard deviation on this example
        #plot the k most interesting channels
        for j in xrange(filter_start, filter_start+num_filters):
            pv3.add_patch(topo_feat[i,:,:,idx[j]], rescale = False, activation = 0.)

    pv1.show()
    pv3.show()
    pv4.show()
    pv5.show()

print 'weights viewer dimensions: '+str((n,n))

# 需要導入模塊: from pylearn2.gui.patch_viewer import PatchViewer [as 別名]
# 或者: from pylearn2.gui.patch_viewer.PatchViewer import show [as 別名]
        main_viewer.add_patch(chan_viewer.image[:,:,viewer_dims] - 0.5)
        chan_viewer.clear()

    return copy.copy(main_viewer.image)


w_image = plot(wv)
if opts.phi:
    phi_image = plot(phi)

nplots = 2 if opts.phi else 1
viewer = PatchViewer((1,nplots), 
            (w_image.shape[0], 
             w_image.shape[1]),
            is_color = opts.color,
            pad=(20,20))

viewer_dims = slice(0, None) if opts.color else 0
viewer.add_patch(w_image[:,:, viewer_dims] - 0.5)
if opts.phi:
    viewer_dims = slice(0, None) if opts.color else 1
    viewer.add_patch(phi_image[:,:,0] - 0.5)

pl.imshow(viewer.image, interpolation='nearest')
pl.savefig('filters_%s.png' % opts.path)
pl.close()

if not opts.noshow:
    viewer.show()

# 需要導入模塊: from pylearn2.gui.patch_viewer import PatchViewer [as 別名]
# 或者: from pylearn2.gui.patch_viewer.PatchViewer import show [as 別名]
viewconv = DefaultViewConverter(topo_shape)
viewdims = slice(0, None) if opts.color else 0

# load model and retrieve parameters
model = serial.load(opts.path)
wv = model.Wv.get_value().T
if opts.mu:
    wv = wv * model.mu.get_value()[:, None]

wv_viewer = PatchViewer(get_dims(len(wv)), (opts.height, opts.width),
                        is_color = opts.color, pad=(2,2))
for i in xrange(len(wv)):
    topo_wvi = viewconv.design_mat_to_topo_view(wv[i:i+1])
    wv_viewer.add_patch(topo_wvi[0])
if opts.wv_only:
    wv_viewer.show()
    os.sys.exit()

wg = model.Wg.get_value()
wh = model.Wh.get_value()
wg_viewer2 = PatchViewer((opts.top, opts.top), (opts.height, opts.width),
                         is_color = opts.color, pad=(2,2))
wg_viewer1 = PatchViewer(get_dims(len(wg)/opts.top),
                         (wg_viewer2.image.shape[0], wg_viewer2.image.shape[1]),
                         is_color = opts.color, pad=(2,2))
for i in xrange(0, len(wg), opts.top):
    for j in xrange(i, i + opts.top):
        idx = numpy.argsort(wg[j])[-opts.top:][::-1]
        for idx_j in idx:
            topo_wgi = viewconv.design_mat_to_topo_view(wv[idx_j:idx_j+1])
            wg_viewer2.add_patch(topo_wgi[0])

# 需要導入模塊: from pylearn2.gui.patch_viewer import PatchViewer [as 別名]
# 或者: from pylearn2.gui.patch_viewer.PatchViewer import show [as 別名]
dataset = yaml_parse.load(model.dataset_yaml_src)


W = model.W.get_value()

T = dataset.get_topological_view(W.T)

from pylearn2.gui.patch_viewer import PatchViewer

pv1 = PatchViewer((3,3),(32,32),is_color = True)

pv2 = PatchViewer((3,4),(32,32),is_color=True)

import numpy as np
rng = np.random.RandomState([1,2,3])

for i in xrange(12):
    print i
    while True:
        print 'looping'
        idxs = rng.randint(0,T.shape[0],(9,))
        for j in xrange(9):
            pv1.add_patch(T[idxs[j],:],activation=0.)
        pv1.show()
        x = raw_input('use which? (0-9): ')
        idx = idxs[eval(x)]
        break
    pv2.add_patch(T[idx,:],activation=0.)

pv2.show()

# 需要導入模塊: from pylearn2.gui.patch_viewer import PatchViewer [as 別名]
# 或者: from pylearn2.gui.patch_viewer.PatchViewer import show [as 別名]
wv = rbm.Wv.get_value().T
if opts.rings:
    rings = eval(opts.rings)
    wv = retina.decode(wv, (opts.height, opts.width, opts.chans), rings)


### Build channels for individual channels ###
chans_viewer = []
for chani in range(opts.chans):

    patch_viewer = PatchViewer(
            get_dims(len(wv)),
            (opts.height, opts.width),
            is_color = opts.color,
            pad=(2,2))
    chans_viewer += [patch_viewer]

    for i in range(len(wv)):
        patch_viewer.add_patch(wv[i,:,:,chani])


main_viewer = PatchViewer((1,opts.chans),
                          (chans_viewer[0].image.shape[0],
                           chans_viewer[0].image.shape[1]),
                          is_color = opts.color,
                          pad=(10,10))
for chan_viewer in chans_viewer:
    main_viewer.add_patch(chan_viewer.image[:,:,viewdims] - 0.5)
main_viewer.show()