def plot_lrp(fig, im):
global method
typeMethod = method
nnPred = nn.forward(np.array([im]))
#lrpScores = nn.lrp(nnPred, 'alphabeta', 2)
lrpScores = nn.lrp(nnPred, typeMethod, 2)
if np.isnan(np.sum(lrpScores[0])):
print('Warning: NaN values. Most likely because score after first layer'
' gives two negative numbers and you get division by 0.')
caxNnPred = axNnPred.imshow(render.vec2im(lrpScores[0]), cmap='jet')
fig.colorbar(caxNnPred, cax=axColormapLRP)
infoNNPred = 'NN probabilities: square = {}, triangle = {}'.format(round(nnPred[0][0], 2), round(nnPred[0][1], 2))
axNnPred.set_title(infoNNPred)
print('Done plotting the LRP map')
return(fig)
def plot_shape(val):
global rotateIdx, shape
shapeColor = sShapeColor.val
backgroundColor = sBackgroundColor.val
im = np.array(uniqShapeRot[shape][rotateIdx])
im[im == 1] = shapeColor
im[im == 0] = backgroundColor
info = 'shape color = {}, background color = {}, rotateIdx = {}, shape = {}.'.format(shapeColor, backgroundColor, rotateIdx, shape)
fig.suptitle(info)
axOrigImage.imshow(render.vec2im(im), cmap='gray_r', vmin=0, vmax=1)
print('Done plotting the shape')
return(fig, im)
y = Y['train'][[idx]]
nnPred = nn.forward(x)
# print nnPred
# lrpScores = nn.lrp(nnPred, 'alphabeta', 2)
# print np.sum(lrpScores)
#plt.matshow(render.vec2im(x[0] + innerCircleSq))
# inspect first linear layer
# --------------------------
W1 = nn.modules[0].W
B = nn.modules[0].B
dim = int(W1.shape[1])
W11 = W1[:, 0]
W12 = W1[:, 1]
W11Im = render.vec2im(W11)
W12Im = render.vec2im(W12)
# init figure
fig = plt.figure()
grid = axes_grid1.AxesGrid(
fig,
111,
nrows_ncols=(1, dim),
axes_pad=0.55,
share_all=True,
cbar_location="right",
cbar_mode="each",
cbar_size="5%",
cbar_pad="2%",
)
# load trained neural network (nn)
nnName = 'nn_Linear_1024_2_Rect_Linear_2_2_SoftMax_(batchsize_10_number_iterations_10000).txt'
nn = model_io.read(settings.modelPath + nnName)
# I do not want to load the data every time, therefore the if statement
if 'X' not in locals():
# load data
X, Y = data_loader.load_data()
# load first layer weights
W1 = nn.modules[0].W
B1 = nn.modules[0].B
dim = int(W1.shape[1])
W11 = W1[:, 0]
W12 = W1[:, 1]
W11Image = render.vec2im(W11)
W12Image = render.vec2im(W12)
n = len(W11)
idxMiddleImage = int(n / 2 + math.sqrt(n) / 2)
# calculate innercircles to find (hidden) relationships
innerCircleSq, innerCircleTr, ring = data_analysis.inner_circles()
unionSq, unionTr = data_analysis.union_shapes()
shapeAreaOutsideRing = unionSq - innerCircleSq
weightShapeAreaOutsideRing1 = W11.dot(shapeAreaOutsideRing)
weightShapeAreaOutsideRing2 = W12.dot(shapeAreaOutsideRing)
# unique shape rotation of squares and triangles
uniqShapeRot = data_analysis.unique_shapes()
uniqShapeRotSq = uniqShapeRot['square']
uniqShapeRotTr = uniqShapeRot['triangle']
# lrp evaluation
nnPred = nn.forward(x)
relevanceValues = {
'nn prediction': nnPred,
'square': np.array([[1., 0.]]),
'triangle': np.array([[0., 1.]])
}
lrpRelevance = {}
for idx, (key, relVal) in enumerate(relevanceValues.items()):
# find and save lrp relevance
lrpRelevance[key] = nn.lrp(relVal, 'alphabeta', 2)
# generate compound heatmap
hmComp, R = render.hm_to_rgb(render.vec2im(lrpRelevance[key]),
X=render.vec2im(x),
scaling=3,
shape=(),
sigma=2,
cmap='jet',
normalize=True)
# plot result
axes[idx].set_title(key + ': ' + str(np.round(relVal[0], 2)))
img = axes[idx].imshow(hmComp)
axes[idx].axis('off')
fig.colorbar(img, ax=axes[idx])
# just a simple plot of weights without normalization
titlePlot = 'LRP Heatmaps for Varying Choices (Without Scaling at SoftMax Layer)'