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src/p/y/pylearn2-HEAD/pylearn2/scripts/show_binocular_greyscale_examples.py   pylearn2(Download)
        # (Ian can't do the magic eye thing where you focus your eyes past the screen, must
        # focus eyes in front of screen)
        pv.add_patch(examples[i,:,:,1], activation = 0.0, rescale = patch_rescale)
        pv.add_patch(examples[i,:,:,0], activation = 0.0, rescale = patch_rescale)
 

src/p/y/pylearn2-HEAD/pylearn2/scripts/show_examples.py   pylearn2(Download)
 
    for i in xrange(rows*cols):
        pv.add_patch(examples[i,:,:,:], activation=0.0, rescale=patch_rescale)
 
    if out is None:

src/p/y/pylearn2-HEAD/pylearn2/scripts/dbm/show_samples.py   pylearn2(Download)
        row_start = cols * i
        for j in xrange(cols):
            pv.add_patch(display_batch[row_start+j,:,:,:], rescale = False)
            if mapback:
                pv.add_patch(mapped_batch[row_start+j,:,:,:], rescale = False)

src/p/y/pylearn2-HEAD/pylearn2/scripts/dbm/top_filters.py   pylearn2(Download)
            act = (0, -mag)
 
        pv.add_patch( imgs[idx,...], rescale = True, activation = act)
 
if out_prefix is None:

src/p/y/pylearn2-HEAD/pylearn2/gui/get_weights_report.py   pylearn2(Download)
    for i in range(0,h):
        patch = weights_view[idx[i],...]
        pv.add_patch(patch, rescale=patch_rescale, activation=act)
 
    abs_weights = np.abs(weights_view)
        if patch_rescale:
            patch = patch / np.abs(patch).max()
        pv.add_patch(patch[:,:,1], rescale=False, activation=act)
        pv.add_patch(patch[:,:,0], rescale=False, activation=act)
 

src/p/y/pylearn2-HEAD/pylearn2/scripts/dbm/show_reconstructions.py   pylearn2(Download)
            adjusted_vis_patch = dataset.adjust_for_viewer(vis_patch)
            if vis_patch.shape[-1] == 2:
                pv.add_patch(adjusted_vis_patch[:,:,1], rescale=False)
                pv.add_patch(adjusted_vis_patch[:,:,0], rescale=False)
            else:
                pv.add_patch(adjusted_vis_patch, rescale = False)
            #    print '\t',ch,(chv.min(),chv.mean(),chv.max())
            if mapback:
                pv.add_patch(dataset.adjust_for_viewer(
                    mapped_batch[row_start+j,:,:,:].copy()), rescale = False)
            if recons_batch.shape[-1] == 2:
                pv.add_patch(dataset.adjust_to_be_viewed_with(

src/p/y/pylearn2-HEAD/pylearn2/scripts/datasets/step_through_norb_foveated.py   pylearn2(Download)
    patch = patch / np.abs(patch).max()
 
    pv.add_patch(patch[:,:,1], rescale=False)
    pv.add_patch(patch[:,:,0], rescale=False)
 

src/p/y/pylearn2-HEAD/pylearn2/scripts/dbm/show_negative_chains.py   pylearn2(Download)
 
for i in xrange(m):
    pv.add_patch(vis_chains[i,:], rescale = False)
 
pv.show()

src/t/h/theano_exercises-HEAD/04_machine_learning/02_autoencoder/show_reconstructions.py   theano_exercises(Download)
pv = PatchViewer((10, 20), X.shape[1:3], is_color=False)
for i in xrange(100):
    pv.add_patch(X[i, :, :, :] - 0.5)
    pv.add_patch(R[i, :, :, :] - 0.5)
pv.show()