def test_linear_with_beta_ignore_bias(self):
# Prepare configuration of linear layer
config = LRPConfiguration()
config.set(LAYER.SPARSE_LINEAR, AlphaBetaConfiguration(alpha=2, beta=-1, bias_strategy=BIAS_STRATEGY.IGNORE))
config.set(LAYER.LINEAR, AlphaBetaConfiguration(alpha=2, beta=-1, bias_strategy=BIAS_STRATEGY.IGNORE))
expected_result = [[-83.6, 22.8, -57.79534884, 156.5953488]]
self._do_test(expected_result, config)
def test_linear_lrp_alpha_beta_active_bias(self):
# Prepare configuration of linear layer
config = LRPConfiguration()
config.set(LAYER.SPARSE_LINEAR, AlphaBetaConfiguration(alpha=2, beta=-1, bias_strategy=BIAS_STRATEGY.ACTIVE))
config.set(LAYER.LINEAR, AlphaBetaConfiguration(alpha=2, beta=-1, bias_strategy=BIAS_STRATEGY.ACTIVE))
expected_result = [[-83.6, 21.92307692, -47.5372549, 147.214178]]
self._do_test(expected_result, config)
def test_linear_with_beta_no_bias(self):
# Prepare configuration of linear layer
config = LRPConfiguration()
config.set(LAYER.SPARSE_LINEAR, AlphaBetaConfiguration(alpha=2, beta=-1))
config.set(LAYER.LINEAR, AlphaBetaConfiguration(alpha=2, beta=-1))
expected_result = [[-80.14545455, 9.566433566, -28.36791444, 125.5933087]]
self._do_test(expected_result, config)
def test_alpha_beta_no_bias(self):
# config
config = LRPConfiguration()
config.set(
LAYER.CONVOLUTIONAL,
AlphaBetaConfiguration(alpha=2,
beta=-1,
bias_strategy=BIAS_STRATEGY.NONE))
# Shape (3, 2, 2, 4, 2)
expected = [[[[[0., 0.], [0., 0.], [0.19799927, 0.58104888], [0., 0.]],
[[0., 0.], [0., 0.], [-0.89017772, 0.88278689], [0.,
0.]]],
[[[0., 0.], [0., 0.], [0., 0.], [0., 0.]],
[[0., 0.], [0., 0.], [0., 0.], [0.20427209,
0.61883095]]]],
[[[[0., 0.], [0., 0.], [0., 0.], [0.64397426, 0.63812658]],
[[0., 0.], [0., 0.], [0., 0.], [-0.87641592,
0.33343329]]],
[[[0., 0.], [0., 0.], [0., 0.], [0., 0.]],
[[0., 0.], [0.3990402, 0.37736268], [0., 0.], [0.,
0.]]]],
[[[[-0.35146675, 0.27085197], [0., 0.], [0., 0.], [0.,
0.]],
[[0.02872034, 0.31778188], [0., 0.], [0., 0.], [0.,
0.]]],
[[[0., 0.], [0., 0.], [0., 0.], [0., 0.]],
[[0., 0.], [0.44995264, 0.88703817], [0., 0.], [0.,
0.]]]]]
self._do_convolutional_test_with_config(config, expected)
def test_softmax(self):
with tf.Graph().as_default():
inp = tf.constant([[[1, 2, 3], [1, 2, 3]], [[1, 2, 3], [1, 2, 3]]], dtype=tf.float32)
out = tf.nn.softmax(inp)
config = LRPConfiguration()
config.set(LAYER.SOFTMAX, AlphaBetaConfiguration(alpha=2, beta=-1))
expl = lrp.lrp(inp, out, config)
with tf.Session() as s:
explanation = s.run(expl)
expected = np.array([[[[-0.05989202454, -0.162803402, 0.8879363823],
[0, 0, 0]],
[[0, 0, 0],
[-0.05989202454, -0.162803402, 0.8879363823]]],
[[[-0.05989202454, -0.162803402, 0.8879363823],
[0, 0, 0]],
[[0, 0, 0],
[-0.05989202454, -0.162803402, 0.8879363823]]]])
# Check if the relevance scores are correct (the correct values are found by
# calculating the example by hand)
self.assertTrue(
np.allclose(expected, explanation, rtol=1e-03, atol=1e-03),
msg="Should be a good explanation")
def test_alpha_beta_active_bias(self):
# config
config = LRPConfiguration()
config.set(
LAYER.CONVOLUTIONAL,
AlphaBetaConfiguration(alpha=2,
beta=-1,
bias_strategy=BIAS_STRATEGY.ACTIVE))
# Shape (3, 2, 2, 4, 2)
expected = [[[[[0., 0.], [0., 0.], [0.23750607, 0.62055568], [0., 0.]],
[[0., 0.], [0., 0.], [-0.89017772, 0.92229369], [0.,
0.]]],
[[[0., 0.], [0., 0.], [0., 0.], [0., 0.]],
[[0., 0.], [0., 0.], [0., 0.], [0.25841425,
0.67297311]]]],
[[[[0., 0.], [0., 0.], [0., 0.], [0.68974016, 0.68389248]],
[[0., 0.], [0., 0.], [0., 0.], [-0.87641592,
0.37919919]]],
[[[0., 0.], [0., 0.], [0., 0.], [0., 0.]],
[[0., 0.], [0.48797808, 0.46630056], [0., 0.], [0.,
0.]]]],
[[[[-0.35146675, 0.29937841], [0., 0.], [0., 0.], [0.,
0.]],
[[0.05724678, 0.34630832], [0., 0.], [0., 0.], [0.,
0.]]],
[[[0., 0.], [0., 0.], [0., 0.], [0., 0.]],
[[0., 0.], [0.53026941, 0.96735494], [0., 0.], [0.,
0.]]]]]
self._do_convolutional_test_with_config(config, expected)
def test_alpha_beta_all_bias(self):
# config
config = LRPConfiguration()
config.set(
LAYER.CONVOLUTIONAL,
AlphaBetaConfiguration(alpha=2,
beta=-1,
bias_strategy=BIAS_STRATEGY.ALL))
# Shape (3, 2, 2, 4, 2)
expected = [[[[[0., 0.], [0., 0.], [0.22762937, 0.61067898], [0., 0.]],
[[0., 0.], [0., 0.], [-0.86054762, 0.91241699], [0.,
0.]]],
[[[0., 0.], [0., 0.], [0., 0.], [0., 0.]],
[[0., 0.], [0., 0.], [0., 0.], [0.23134317,
0.64590203]]]],
[[[[0., 0.], [0., 0.], [0., 0.], [0.67829868, 0.67245101]],
[[0., 0.], [0., 0.], [0., 0.], [-0.84209149,
0.36775772]]],
[[[0., 0.], [0., 0.], [0., 0.], [0., 0.]],
[[0., 0.], [0.44350914, 0.42183162], [0., 0.], [0.,
0.]]]],
[[[[-0.33007192, 0.2922468], [0., 0.], [0., 0.], [0., 0.]],
[[0.05011517, 0.33917671], [0., 0.], [0., 0.], [0.,
0.]]],
[[[0., 0.], [0., 0.], [0., 0.], [0., 0.]],
[[0., 0.], [0.49011103, 0.92719656], [0., 0.], [0.,
0.]]]]]
self._do_convolutional_test_with_config(config, expected)
def test_lstm_alpha_beta_active_bias(self):
config = LRPConfiguration()
config.set(
LAYER.LSTM,
AlphaBetaConfiguration(alpha=2,
beta=-1,
bias_strategy=BIAS_STRATEGY.ACTIVE))
expected_result = np.array([[[[1.17243838, -1.42132187, 1.67020536],
[0., 0., 0.]],
[[1.47216972, -3.08726835, 4.70236698],
[0.52473047, 0.76832693, 0.14187811]]]])
self._do_test_with_config_and_expected_result(config, expected_result)
def test_lstm_alpha_beta_all_bias(self):
config = LRPConfiguration()
config.set(
LAYER.LSTM,
AlphaBetaConfiguration(alpha=2,
beta=-1,
bias_strategy=BIAS_STRATEGY.ALL))
expected_result = np.array([[[[0.90404336, -1.24239185, 1.40181034],
[0., 0., 0.]],
[[0.58241955, -1.00368592, 3.04841668],
[0.30076316, 0.54435962, -0.08208919]]]])
self._do_test_with_config_and_expected_result(config, expected_result)
def test_lstm_alpha_beta_no_bias(self):
config = LRPConfiguration()
config.set(
LAYER.LSTM,
AlphaBetaConfiguration(alpha=2,
beta=-1,
bias_strategy=BIAS_STRATEGY.NONE))
expected_result = np.array([[[[0.72511334, -1.42132187, 1.22288032],
[0., 0., 0.]],
[[1.17308824, -1.30891597, 4.40328549],
[0.52473047, 0.76832693, 0.14187811]]]])
self._do_test_with_config_and_expected_result(config, expected_result)
def test_linear_lrp_alpha_beta_equal_bias(self):
# Prepare configuration of linear layer
config = LRPConfiguration()
config.set(
LAYER.LINEAR,
AlphaBetaConfiguration(alpha=2,
beta=-1,
bias_strategy=BIAS_STRATEGY.ALL))
expected_result = [[
-70.90120207, 14.94277618, -27.09395311, 121.052379
]]
self.do_test_with_config_and_result(expected_result, config)
def test_alpha_beta_ignore_bias(self):
config = LRPConfiguration()
config.set(LAYER.LINEAR,
AlphaBetaConfiguration(bias_strategy=BIAS_STRATEGY.IGNORE))
expected_result = [
[[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]],
[[0.05056302043, 0.001782476642, 0.4564389581, 0, 0.2856364171],
[
0.0006553275046, 0.2230518547, 0.1905802792, 0.09459906389,
0.08207159722
]],
[[0, 0, 0, 0, 0],
[0.1729084504, 0, 0.0773933278, 0.2900907282, 0.006641839018]]
]
self._do_linear_test(config, np.array(expected_result))
def test_zb_ignore_bias(self):
config = LRPConfiguration()
config.set(LAYER.LINEAR,
AlphaBetaConfiguration(bias_strategy=BIAS_STRATEGY.IGNORE))
config.set_first_layer_zb(_l(-1), _l(1), BIAS_STRATEGY.IGNORE)
expected_result = [[[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]],
[[
0.08941490911, 0.001847394735, 0.3857157564,
0.04130132575, 0.2761414863
],
[
0.06524084895, 0.2049935833, 0.1593686046,
0.08435578325, 0.07699930241
]],
[[0, 0, 0, 0, 0],
[
0.1842489119, 0.02888073415, 0.04590480826,
0.2341442052, 0.05385568583
]]]
self._do_linear_test(config, np.array(expected_result))
def test_one_prediction_per_sample(self):
g = tf.Graph()
with g.as_default():
inp = tf.placeholder(tf.float32, (2, 1, 5))
x = tf.contrib.layers.batch_norm(inp, is_training=False, scale=True)
vars = tf.global_variables()
beta = next(i for i in vars if 'beta' in i.name)
gamma = next(i for i in vars if 'gamma' in i.name)
mean = next(i for i in vars if 'mean' in i.name)
variance = next(i for i in vars if 'variance' in i.name)
b = tf.constant([0, 1, 0, 1, 0], dtype=tf.float32)
assign_beta = tf.assign(beta, b)
g = tf.constant([0.1, 0.2, 0.3, 0.4, 0.5], dtype=tf.float32)
assign_gamma = tf.assign(gamma, g)
m = tf.constant([1, 2, 3, 4, 4.5], dtype=tf.float32)
assign_mean = tf.assign(mean, m)
v = tf.constant([0.2, 0.2, 0.2, 0.2, 0.2], dtype=tf.float32)
assign_variance = tf.assign(variance, v)
# Get the explanation
config = LRPConfiguration()
config.set(LAYER.ELEMENTWISE_LINEAR, AlphaBetaConfiguration())
explanation = lrp.lrp(inp, x, config)
with tf.Session() as s:
s.run(tf.global_variables_initializer())
s.run([assign_beta, assign_gamma, assign_mean, assign_variance])
# Shape: (2, 1, 1, 5)
expected_relevances = np.array(
[[[[0, 0, 0, 1, 0]]],
[[[0, 0.8921994513, 0, 0, 0]]]])
relevances = s.run(explanation, feed_dict={inp: [[[1, 0, 3, 4, 5]],
[[1, 2, 3, 0, 4]]]})
self.assertTrue(np.allclose(expected_relevances, relevances, rtol=1e-03, atol=1e-03),
msg="The relevances do not match the expected")
def test_zb_no_bias(self):
config = LRPConfiguration()
config.set(LAYER.LINEAR,
AlphaBetaConfiguration(bias_strategy=BIAS_STRATEGY.IGNORE))
config.set_first_layer_zb(_l(-1), _l(1), BIAS_STRATEGY.NONE)
expected_result = [[[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]],
[[
0.1145575292, 0.002366864524, 0.4941753504,
0.05291486486, 0.3537898399
],
[
0.07031890972, 0.2048122289, 0.1691654014,
0.1035978445, 0.08054911078
]],
[[0, 0, 0, 0, 0],
[
0.2102658664, 0.0283220131, 0.04511325643,
0.2887187793, 0.05874777231
]]]
self._do_linear_test(config, np.array(expected_result))
def test_zb_all_bias(self):
config = LRPConfiguration()
config.set(LAYER.LINEAR,
AlphaBetaConfiguration(bias_strategy=BIAS_STRATEGY.IGNORE))
config.set_first_layer_zb(_l(-1), _l(1), BIAS_STRATEGY.ALL)
expected_result = [[[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]],
[[
0.0698808139, -0.0423098508, 0.449498635,
0.008238149531, 0.3091131246
],
[
0.0628218352, 0.1973151543, 0.1616683269,
0.09610076993, 0.07305203625
]],
[[0, 0, 0, 0, 0],
[
0.193439198, 0.01149534468, 0.02828658801,
0.2718921108, 0.04192110389
]]]
self._do_linear_test(config, np.array(expected_result))
def test_four_predictions_per_sample(self):
g = tf.Graph()
with g.as_default():
inp = tf.placeholder(tf.float32, (2, 4, 5))
x = tf.contrib.layers.batch_norm(inp, is_training=False, scale=True)
vars = tf.global_variables()
beta = next(i for i in vars if 'beta' in i.name)
gamma = next(i for i in vars if 'gamma' in i.name)
mean = next(i for i in vars if 'mean' in i.name)
variance = next(i for i in vars if 'variance' in i.name)
b = tf.constant([0, 1, 0, 1, 0], dtype=tf.float32)
assign_beta = tf.assign(beta, b)
g = tf.constant([0.1, 0.2, 0.3, 0.4, 0.5], dtype=tf.float32)
assign_gamma = tf.assign(gamma, g)
m = tf.constant([1, 2, 3, 4, 4.5], dtype=tf.float32)
assign_mean = tf.assign(mean, m)
v = tf.constant([0.2, 0.2, 0.2, 0.2, 0.2], dtype=tf.float32)
assign_variance = tf.assign(variance, v)
# Get the explanation
config = LRPConfiguration()
config.set(LAYER.ELEMENTWISE_LINEAR, AlphaBetaConfiguration())
explanation = lrp.lrp(inp, x, config)
with tf.Session() as s:
s.run(tf.global_variables_initializer())
s.run([assign_beta, assign_gamma, assign_mean, assign_variance])
input = np.array([[[-0.3597203655, 2.416366089, -0.7543762749, 0.8718006654, -2.221761776],
[1.099448308, 0.4108163696, 1.798067039, -0.6544576652, -0.6968107745],
[-0.5612699962, -0.2597267932, 0.06325442832, -1.236885473, 0.9369620591],
[-0.06784464057, -0.004403155247, -1.195337879, -0.528265092, -0.1020843691]],
[[0.8766405077, 0.522839272, 0.4197016166, -1.497174712, 0.05348117451],
[0.08739119149, -0.9997059536, -0.6212993685, 0.04027413639, 0.3979749684],
[0.06180908495, -0.5322826252, -1.585670194, -0.5220654844, -1.096597863],
[1.003261811, -1.865129316, -1.134796217, -0.1038509194, -0.8933464003]]])
# Shape: (2, 4, 4, 5)
expected_relevances = np.array(
[[[[0.0, 1.07794025, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.1832650698, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0]]],
[[[0.0, 0.2332384558, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0, 0.0, 0.0],
[-0.2035572695, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0007275465407, 0.0, 0.0, 0.0, 0.0]]]])
relevances = s.run(explanation, feed_dict={inp: input})
self.assertTrue(np.allclose(expected_relevances, relevances, rtol=1e-03, atol=1e-03),
msg="The relevances do not match the expected")