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_innvestigate(self, model, data):
# 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('deconvnet',
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)
analysis = ivis.graymap(analysis)
return analysis[0]
def graymap(X):
return ivis.graymap(np.abs(X), input_is_postive_only=True)
def graymap(X):
return ivis.graymap(np.abs(X))
def bk_proj(X):
return ivis.graymap(X)
def image(X):
X = X.copy()
X = iutils.postprocess_images(X)
return ivis.graymap(X, input_is_postive_only=True)
