def __init__(self, layer, state, low=-1, high=1, copy_weights=False):
self._low = low
self._high = high
# This rule works with three variants of the layer, all without biases.
# One is the original form and two with only the positive or
# negative weights.
if copy_weights:
weights = layer.get_weights()
if layer.use_bias:
weights = weights[:-1]
positive_weights = [x * (x > 0) for x in weights]
negative_weights = [x * (x < 0) for x in weights]
else:
weights = layer.weights
if layer.use_bias:
weights = weights[:-1]
positive_weights = [x * iK.to_floatx(x > 0) for x in weights]
negative_weights = [x * iK.to_floatx(x < 0) for x in weights]
self._layer_wo_act = kgraph.copy_layer_wo_activation(
layer, keep_bias=False, name_template="reversed_kernel_%s")
self._layer_wo_act_positive = kgraph.copy_layer_wo_activation(
layer,
keep_bias=False,
weights=positive_weights,
name_template="reversed_kernel_positive_%s")
self._layer_wo_act_negative = kgraph.copy_layer_wo_activation(
layer,
keep_bias=False,
weights=negative_weights,
name_template="reversed_kernel_negative_%s")
def __init__(self,
layer,
state,
alpha=None,
beta=None,
bias=True,
copy_weights=False):
alpha, beta = rutils.assert_infer_lrp_alpha_beta_param(
alpha, beta, self)
self._alpha = alpha
self._beta = beta
# prepare positive and negative weights for computing positive
# and negative preactivations z in apply_accordingly.
if copy_weights:
weights = layer.get_weights()
if not bias and layer.use_bias:
weights = weights[:-1]
positive_weights = [x * (x > 0) for x in weights]
negative_weights = [x * (x < 0) for x in weights]
else:
weights = layer.weights
if not bias and layer.use_bias:
weights = weights[:-1]
positive_weights = [x * iK.to_floatx(x > 0) for x in weights]
negative_weights = [x * iK.to_floatx(x < 0) for x in weights]
self._layer_wo_act_positive = kgraph.copy_layer_wo_activation(
layer,
keep_bias=bias,
weights=positive_weights,
name_template="reversed_kernel_positive_%s")
self._layer_wo_act_negative = kgraph.copy_layer_wo_activation(
layer,
keep_bias=bias,
weights=negative_weights,
name_template="reversed_kernel_negative_%s")
def __init__(self, layer, state, copy_weights=False):
# W-square rule works with squared weights and no biases.
if copy_weights:
weights = layer.get_weights()
else:
weights = layer.weights
if layer.use_bias:
weights = weights[:-1]
weights = [x**2 for x in weights]
self._layer_wo_act_b = kgraph.copy_layer_wo_activation(
layer,
keep_bias=False,
weights=weights,
name_template="reversed_kernel_%s")
def __init__(self, layer, state, copy_weights=False):
# The flat rule works with weights equal to one and
# no biases.
if copy_weights:
weights = layer.get_weights()
if layer.use_bias:
weights = weights[:-1]
weights = [np.ones_like(x) for x in weights]
else:
weights = layer.weights
if layer.use_bias:
weights = weights[:-1]
weights = [K.ones_like(x) for x in weights]
self._layer_wo_act_b = kgraph.copy_layer_wo_activation(
layer,
keep_bias=False,
weights=weights,
name_template="reversed_kernel_%s")
def __init__(self, layer, state, copy_weights=False):
# The z-plus rule only works with positive weights and
# no biases.
#TODO: assert that layer inputs are always >= 0
if copy_weights:
weights = layer.get_weights()
if layer.use_bias:
weights = weights[:-1]
weights = [x * (x > 0) for x in weights]
else:
weights = layer.weights
if layer.use_bias:
weights = weights[:-1]
weights = [x * iK.to_floatx(x > 0) for x in weights]
self._layer_wo_act_b_positive = kgraph.copy_layer_wo_activation(
layer,
keep_bias=False,
weights=weights,
name_template="reversed_kernel_positive_%s")
def __init__(self, layer, state, epsilon=1e-7, bias=True):
self._epsilon = rutils.assert_lrp_epsilon_param(epsilon, self)
self._layer_wo_act = kgraph.copy_layer_wo_activation(
layer, keep_bias=bias, name_template="reversed_kernel_%s")
def __init__(self, layer, state, bias=True):
self._layer_wo_act = kgraph.copy_layer_wo_activation(
layer, keep_bias=bias, name_template="reversed_kernel_%s")
def __init__(self, layer, state):
##print("in AveragePoolingRerseLayer.init:", layer.__class__.__name__,"-> Dedicated ReverseLayer class" ) #debug
self._layer_wo_act = kgraph.copy_layer_wo_activation(
layer, name_template="reversed_kernel_%s")