def test_fast__visualizations():
def get_X():
return np.random.rand(1, 28, 28, 3)
ivis.project(get_X())
ivis.heatmap(get_X())
ivis.graymap(get_X())
ivis.gamma(get_X())
ivis.clip_quantile(get_X(), 0.95)
def explain_image(self, model, data, labels):
with DeepExplain(session=K.get_session()) as de:
model_wo_sm = iutils.keras.graph.model_wo_softmax(model)
input_tensor = model_wo_sm.layers[0].input
output_tensor = model_wo_sm.layers[-1].output
fModel = keras.models.Model(inputs=input_tensor,
outputs=output_tensor)
target_tensor = fModel(input_tensor)
attributions = de.explain('occlusion',
target_tensor * labels,
input_tensor,
data,
window_shape=self.window_shape,
step=self.step)
attributions = np.nan_to_num(attributions)
analysis = attributions
analysis = iutils.postprocess_images(analysis,
color_coding='BGRtoRGB',
channels_first=False)
analysis = ivis.gamma(analysis, minamp=0, gamma=0.95)
analysis = ivis.heatmap(analysis)
return analysis[0]
def explain_image_innvestigate(self, model, data):
try:
# Build the model
model = keras.models.Model(inputs=model.inputs,
outputs=model.outputs)
model.compile(optimizer="adam", loss="categorical_crossentropy")
model_wo_sm = iutils.keras.graph.model_wo_softmax(model)
analyzer = innvestigate.create_analyzer(self.gradient_method,
model_wo_sm)
analysis = analyzer.analyze(data)
analysis = iutils.postprocess_images(analysis,
color_coding='BGRtoRGB',
channels_first=False)
analysis = ivis.gamma(analysis, minamp=0, gamma=0.95)
analysis = ivis.heatmap(analysis)
return analysis[0]
except innvestigate.NotAnalyzeableModelException:
return None
except Exception:
return None
def explain_image_deepexplain(self, model, data, labels):
with DeepExplain(session=K.get_session()) as de:
model_wo_sm = iutils.keras.graph.model_wo_softmax(model)
input_tensor = model_wo_sm.layers[0].input
output_tensor = model_wo_sm.layers[-1].output
fModel = keras.models.Model(inputs=input_tensor,
outputs=output_tensor)
target_tensor = fModel(input_tensor)
attributions = de.explain(self.lrp_method, target_tensor * labels,
input_tensor, data, **self.kwargs)
analysis = attributions
analysis = iutils.postprocess_images(analysis,
color_coding='BGRtoRGB',
channels_first=False)
analysis = ivis.gamma(analysis, minamp=0, gamma=0.95)
analysis = ivis.heatmap(analysis)
return analysis[0]
def heatmap(X):
X = ivis.gamma(X, minamp=0, gamma=0.95)
return ivis.heatmap(X)
#get one CT scan and extract image component
cbatch = sample_cancer_train.next_batch(1,
n_epochs=1,
drop_last=True,
shuffle=True)
cim = cbatch.unpack(component='images')
#create analyzer from CNN and analyze input
analyzer = innvestigate.create_analyzer("lrp.epsilon", cnn)
analysis = analyzer.analyze(cim)
analysis_viz = np.squeeze(analysis)
slices.multi_slice_viewer(cbatch.images)
slices.multi_slice_viewer(analysis_viz)
#apply different vizualization to output
X = ivis.gamma(analysis, minamp=0, gamma=0.5)
new = ivis.heatmap(X, cmap_type="seismic")
new2 = sns.heatmap(X[0, 8, :, :, 0])
slices.multi_slice_viewer(new[0, :, :, :, :])
slices.multi_slice_viewer(new2[0, :, :, :, :])
im = new[0, 8, :, :, :]
plt.imshow(im, vmin=0, vmax=1)
fig = plt.figure(frameon=False)
im1 = plt.imshow(cbatch.images[8, :, :], cmap='gray')
im2 = plt.imshow(im, alpha=0.6)
plt.show()
