def step(self, features, labels, lrate):
# OPTIMIZATION PARAMETERS:
alpha = lrate #self.alpha
beta_1 = self.beta_1
beta_2 = self.beta_2
lam = self.lam
N = self.N #60000 #float(self.batch_size) # batch size
self.posterior_mean = self.model.get_weights()
init_weights = []
for i in range(len(self.posterior_mean)):
var = tf.math.add(tf.math.sqrt(N*self.posterior_var[i]), lam)
var = tf.math.reciprocal(var)
sample = tf.random.normal(shape=self.posterior_var[i].shape, mean=0, stddev=1.0)
sample = tf.math.multiply(var, sample)
sample = tf.math.add(self.posterior_mean[i], sample)
init_weights.append(sample)
self.model.set_weights(np.asarray(init_weights))
with tf.GradientTape(persistent=True) as tape:
# Get the probabilities
predictions = self.model(features)
# Calculate the loss
if(int(self.robust_train) == 0):
loss = self.loss_func(labels, predictions)
elif(int(self.robust_train) == 1):
logit_l, logit_u = analyzers.IBP(self, features, self.model.trainable_variables, eps=self.epsilon)
v1 = tf.one_hot(labels, depth=10)
v2 = 1 - tf.one_hot(labels, depth=10)
worst_case = tf.math.add(tf.math.multiply(v2, logit_u), tf.math.multiply(v1, logit_l))
worst_case = self.model.layers[-1].activation(worst_case)
output = (self.robust_lambda * predictions) + ((1-self.robust_lambda) * worst_case)
loss = self.loss_func(labels, output)
elif(int(self.robust_train) == 2):
features_adv = analyzers.PGD(self, features, self.attack_loss, eps=self.epsilon, num_models=-1)
worst_case = self.model(features_adv)
output = (self.robust_lambda * predictions) + ((1-self.robust_lambda) * worst_case)
loss = self.loss_func(labels, output)
elif(int(self.robust_train) == 3):
output = tf.zeros(predictions.shape)
self.epsilon = max(0.0001, self.epsilon)
self.eps_dist = tfp.distributions.Exponential(1.0/self.epsilon)
for _mc_ in range(self.loss_monte_carlo):
eps = self.eps_dist.sample()
logit_l, logit_u = analyzers.IBP(self, features, self.model.trainable_variables, eps=eps)
v1 = tf.one_hot(labels, depth=10)
v2 = 1 - tf.one_hot(labels, depth=10)
v1 = tf.squeeze(v1); v2 = tf.squeeze(v2)
worst_case = tf.math.add(tf.math.multiply(v2, logit_u), tf.math.multiply(v1, logit_l))
worst_case = self.model.layers[-1].activation(worst_case)
one_hot_cls = tf.one_hot(labels, depth=10)
output += (1.0/self.loss_monte_carlo) * worst_case
loss = self.loss_func(labels, output)
elif(int(self.robust_train) == 4):
output = tf.zeros(predictions.shape)
self.epsilon = max(0.0001, self.epsilon)
self.eps_dist = tfp.distributions.Exponential(1.0/self.epsilon)
for _mc_ in range(self.loss_monte_carlo):
#eps = tfp.random.rayleigh([1], scale=self.epsilon)
eps = self.eps_dist.sample()
features_adv = analyzers.FGSM(self, features, self.attack_loss, eps=self.epsilon, num_models=-1)
worst_case = self.model(features_adv)
output += (1.0/self.loss_monte_carlo) * worst_case
loss = self.loss_func(labels, output)
weight_gradient = tape.gradient(loss, self.model.trainable_variables)
g = np.asarray(weight_gradient)
#if(int(self.robust_train) == 1):
# print(g)
# We need to process the gradient according to the reparameterization given by Khan (2002.10060)
g_mu = []
g_s = []
m_hat = []
s_hat = []
t = self.learning_rate
for i in range(len(g)):
# Appropriately scaled updates to the gradients Khan (2002.10060)[ICLR2020]
g_mu.append((self.lam/60000)*self.posterior_mean[i] + g[i])
g_s_comp2 = tf.math.multiply((60000*self.posterior_var[i]), (init_weights[i] - self.posterior_mean[i]))
g_s_comp2 = tf.math.multiply(g_s_comp2, g[i])
g_s.append((self.lam/60000) - self.posterior_var[i] + g_s_comp2)
# Standard momentum updtae
self.m[i] = (beta_1*self.m[i]) + ((1-beta_1)*(g_mu[i]))
m_hat.append(self.m[i]/(1-beta_1))
s_hat.append(self.posterior_var[i]/(1-beta_2))
# Apply the effects from the updates
for i in range(len(g)):
self.posterior_mean[i] = self.posterior_mean[i] - t*(m_hat[i]/s_hat[i])
comp_1 = (0.5 * ((1-beta_2)**2) * g_s[i])
recip = tf.math.multiply(tf.math.reciprocal(self.posterior_var[i]), g_s[i])
self.posterior_var[i] = self.posterior_var[i] + tf.math.multiply(comp_1, recip)
self.model.set_weights(self.posterior_mean)
self.train_loss(loss)
self.train_metric(labels, predictions)
return self.posterior_mean, self.posterior_var
def step(self, features, labels, lrate):
# Define the GradientTape context
with tf.GradientTape(persistent=True) as tape:
tape.watch(self.posterior_mean)
predictions = self.model(features)
if (not self.robust_train):
worst_case = predictions
loss = self.loss_func(labels, predictions)
elif (int(self.robust_train) == 1):
logit_l, logit_u = analyzers.IBP(
self,
features,
self.model.trainable_variables,
eps=self.epsilon)
v1 = tf.one_hot(labels, depth=10)
v2 = 1 - tf.one_hot(labels, depth=10)
worst_case = tf.math.add(tf.math.multiply(v2, logit_u),
tf.math.multiply(v1, logit_l))
worst_case = self.model.layers[-1].activation(worst_case)
output = (self.robust_lambda * predictions) + (
(1 - self.robust_lambda) * worst_case)
loss = self.loss_func(labels, output)
elif (int(self.robust_train) == 2):
features_adv = analyzers.PGD(self,
features,
self.attack_loss,
eps=self.epsilon,
num_models=-1)
worst_case = self.model(features_adv)
output = (self.robust_lambda * predictions) + (
(1 - self.robust_lambda) * worst_case)
loss = self.loss_func(labels, output)
#self.train_rob(labels, worst_case)
elif (int(self.robust_train) == 3):
output = tf.zeros(predictions.shape)
self.epsilon = max(0.0001, self.epsilon)
self.eps_dist = tfp.distributions.Exponential(
1.0 / float(self.epsilon))
for _mc_ in range(self.loss_monte_carlo):
eps = self.eps_dist.sample()
#eps = tfp.random.rayleigh([1], scale=self.epsilon)
logit_l, logit_u = analyzers.IBP(
self,
features,
self.model.trainable_variables,
eps=eps)
v1 = tf.one_hot(labels, depth=10)
v2 = 1 - tf.one_hot(labels, depth=10)
v1 = tf.squeeze(v1)
v2 = tf.squeeze(v2)
worst_case = tf.math.add(tf.math.multiply(v2, logit_u),
tf.math.multiply(v1, logit_l))
worst_case = self.model.layers[-1].activation(worst_case)
one_hot_cls = tf.one_hot(labels, depth=10)
output += (1.0 / self.loss_monte_carlo) * worst_case
loss = self.loss_func(labels, output)
elif (int(self.robust_train) == 4):
output = tf.zeros(predictions.shape)
self.epsilon = max(0.0001, self.epsilon)
self.eps_dist = tfp.distributions.Exponential(
1.0 / float(self.epsilon))
for _mc_ in range(self.loss_monte_carlo):
#eps = tfp.random.rayleigh([1], scale=self.epsilon)
eps = self.eps_dist.sample()
features_adv = analyzers.FGSM(self,
features,
self.attack_loss,
eps=self.epsilon,
num_models=-1)
worst_case = self.model(features_adv)
output += (1.0 / self.loss_monte_carlo) * worst_case
loss = self.loss_func(labels, output)
# Get the gradients
weight_gradient = tape.gradient(loss, self.model.trainable_variables)
weights = self.model.get_weights()
new_weights = []
for i in range(len(weight_gradient)):
wg = tf.math.multiply(weight_gradient[i], lrate)
m = tf.math.subtract(weights[i], wg)
new_weights.append(m)
if (self.record == True):
self.weights_stack.append(new_weights)
self.model.set_weights(new_weights)
self.posterior_mean = new_weights
self.train_loss(loss)
self.train_metric(labels, predictions)
#self.train_rob(labels, worst_case)
return self.posterior_mean, self.posterior_var
def step(self, features, labels, lrate):
"""
Initial sampling for BBB
"""
init_weights = []
noise_used = []
for i in range(len(self.posterior_mean)):
noise = tf.random.normal(shape=self.posterior_var[i].shape,
mean=tf.zeros(
self.posterior_var[i].shape),
stddev=1.0)
var_add = tf.multiply(softplus(self.posterior_var[i]), noise)
#var_add = tf.multiply(self.posterior_mean[i], noise)
w = tf.math.add(self.posterior_mean[i], var_add)
noise_used.append(noise)
init_weights.append(w)
self.model.set_weights(init_weights)
# Define the GradientTape context
with tf.GradientTape(
persistent=True
) as tape: # Below we add an extra variable for IBP
tape.watch(self.posterior_mean)
tape.watch(self.posterior_var)
#tape.watch(init_weights)
predictions = self.model(features)
"""
We support a few different things for auto-diff including adversarial training.
"""
if (self.robust_train == 0):
loss, kl_comp = losses.KL_Loss(labels, predictions,
self.model.trainable_variables,
self.prior_mean, self.prior_var,
self.posterior_mean,
self.posterior_var,
self.loss_func, self.kl_weight)
elif (int(self.robust_train) == 1):
# Get the probabilities
logit_l, logit_u = analyzers.IBP(
self,
features,
self.model.trainable_variables,
eps=self.epsilon)
#!*! TODO: Undo the hardcoding of depth in this function
v1 = tf.one_hot(labels, depth=10)
v2 = 1 - tf.one_hot(labels, depth=10)
worst_case = tf.math.add(tf.math.multiply(v2, logit_u),
tf.math.multiply(v1, logit_l))
# Now we have the worst case softmax probabilities (or output)
worst_case = self.model.layers[-1].activation(worst_case)
output = (self.robust_lambda * predictions) + (
(1 - self.robust_lambda) * worst_case)
# Calculate the loss
loss, kl_comp = losses.KL_Loss(labels, output,
self.model.trainable_variables,
self.prior_mean, self.prior_var,
self.posterior_mean,
self.posterior_var,
self.loss_func, self.kl_weight)
elif (int(self.robust_train) == 2):
features_adv = analyzers.FGSM(self,
features,
self.attack_loss,
eps=self.epsilon,
num_models=-1)
# Get the probabilities
worst_case = self.model(features_adv)
output = (self.robust_lambda * predictions) + (
(1 - self.robust_lambda) * worst_case)
# Calculate the loss
loss, kl_comp = losses.KL_Loss(labels, output,
self.model.trainable_variables,
self.prior_mean, self.prior_var,
self.posterior_mean,
self.posterior_var,
self.loss_func, self.kl_weight)
elif (int(self.robust_train) == 3):
output = tf.zeros(predictions.shape)
self.epsilon = max(0.0001, self.epsilon)
self.eps_dist = tfp.distributions.Exponential(1.0 /
self.epsilon)
for _mc_ in range(self.loss_monte_carlo):
#eps = tfp.random.rayleigh([1], scale=self.epsilon)
eps = self.eps_dist.sample()
logit_l, logit_u = analyzers.IBP(
self,
features,
self.model.trainable_variables,
eps=eps)
v1 = tf.one_hot(labels, depth=10)
v2 = 1 - tf.one_hot(labels, depth=10)
v1 = tf.squeeze(v1)
v2 = tf.squeeze(v2)
worst_case = tf.math.add(tf.math.multiply(v2, logit_u),
tf.math.multiply(v1, logit_l))
worst_case = self.model.layers[-1].activation(worst_case)
one_hot_cls = tf.one_hot(labels, depth=10)
output += (1.0 / self.loss_monte_carlo) * worst_case
loss, kl_comp = losses.KL_Loss(labels, output,
self.model.trainable_variables,
self.prior_mean, self.prior_var,
self.posterior_mean,
self.posterior_var,
self.loss_func, self.kl_weight)
elif (int(self.robust_train) == 4):
output = tf.zeros(predictions.shape)
self.epsilon = max(0.0001, self.epsilon)
self.eps_dist = tfp.distributions.Exponential(1.0 /
self.epsilon)
for _mc_ in range(self.loss_monte_carlo):
#eps = tfp.random.rayleigh([1], scale=self.epsilon)
eps = self.eps_dist.sample()
features_adv = analyzers.FGSM(self,
features,
self.attack_loss,
eps=self.epsilon,
num_models=-1)
worst_case = self.model(features_adv)
output += (1.0 / self.loss_monte_carlo) * worst_case
loss, kl_comp = losses.KL_Loss(labels, output,
self.model.trainable_variables,
self.prior_mean, self.prior_var,
self.posterior_mean,
self.posterior_var,
self.loss_func, self.kl_weight)
# Get the gradients
weight_gradient = tape.gradient(loss, self.model.trainable_variables)
mean_gradient = tape.gradient(loss, self.posterior_mean)
var_gradient = tape.gradient(loss, self.posterior_var)
posti_mean_grad = []
posti_var_grad = []
for i in range(len(mean_gradient)):
weight_gradient[i] = tf.cast(weight_gradient[i], 'float32')
mean_gradient[i] = tf.cast(mean_gradient[i], 'float32')
f = tf.math.add(weight_gradient[i], mean_gradient[i])
posti_mean_grad.append(f)
v = tf.math.divide(
noise_used[i],
1 + tf.math.exp(tf.math.multiply(self.posterior_var[i], -1)))
v = tf.math.multiply(v, weight_gradient[i])
v = tf.math.add(v, var_gradient[i])
posti_var_grad.append(v)
# APPLICATION OF WEIGHTS
new_posti_var = []
new_posti_mean = []
for i in range(len(mean_gradient)):
pdv = tf.math.multiply(posti_var_grad[i], lrate)
pdm = tf.math.multiply(posti_mean_grad[i], lrate)
v = tf.math.subtract(self.posterior_var[i], pdv)
m = tf.math.subtract(self.posterior_mean[i], pdm)
new_posti_var.append(v)
new_posti_mean.append(m)
self.train_loss(loss)
self.train_metric(labels, predictions)
#self.train_rob(labels, worst_case)
self.kl_component(kl_comp)
self.posterior_mean = new_posti_mean
self.posterior_var = new_posti_var
return new_posti_mean, new_posti_var
def step(self, features, labels, lrate):
for features, labels in self.train_ds.take(self.batches):
#features, labels = self.train_ds.take()
# Define the GradientTape context
with tf.GradientTape(persistent=True) as tape: # Below we add an extra variable for IBP
tape.watch(self.posterior_mean)
predictions = self.model(features)
if(self.robust_train == 0):
loss = losses.normal_potential_energy(labels, predictions, self.prior_mean,
self.prior_var, self.q, self.loss_func)
elif(int(self.robust_train) == 1):
# Get the probabilities
predictions = self.model(features)
logit_l, logit_u = analyzers.IBP(self, features, self.model.trainable_variables, eps=self.epsilon)
#!*! TODO: Undo the hardcoding of depth in this function
v1 = tf.one_hot(labels, depth=10)
v2 = 1 - tf.one_hot(labels, depth=10)
worst_case = tf.math.add(tf.math.multiply(v2, logit_u), tf.math.multiply(v1, logit_l))
# Now we have the worst case softmax probabilities
worst_case = self.model.layers[-1].activation(worst_case)
output = (self.robust_lambda * predictions) + ((1-self.robust_lambda) * worst_case)
#loss = self.loss_func(labels, output)
loss = losses.normal_potential_energy(labels, predictions, self.prior_mean,
self.prior_var, self.q, self.loss_func)
elif(int(self.robust_train) == 2):
predictions = self.model(features)
features_adv = analyzers.FGSM(self, features, self.attack_loss, eps=self.epsilon, num_models=-1)
# Get the probabilities
worst_case = self.model(features_adv)
# Calculate the loss
output = (self.robust_lambda * predictions) + ((1-self.robust_lambda) * worst_case)
#loss = self.loss_func(labels, output)
loss = losses.normal_potential_energy(labels, predictions, self.prior_mean,
self.prior_var, self.q, self.loss_func)
elif(int(self.robust_train) == 3):
output = tf.zeros(predictions.shape)
self.epsilon = max(0.0001, self.epsilon )
self.eps_dist= tfp.distributions.Exponential(1.0/self.epsilon)
for _mc_ in range(self.loss_monte_carlo):
#eps = tfp.random.rayleigh([1], scale=self.epsilon)
eps = self.eps_dist.sample()
logit_l, logit_u = analyzers.IBP(self, features, self.model.trainable_variables, eps=eps)
v1 = tf.one_hot(labels, depth=10)
v2 = 1 - tf.one_hot(labels, depth=10)
v1 = tf.squeeze(v1); v2 = tf.squeeze(v2)
worst_case = tf.math.add(tf.math.multiply(v2, logit_u), tf.math.multiply(v1, logit_l))
worst_case = self.model.layers[-1].activation(worst_case)
one_hot_cls = tf.one_hot(labels, depth=10)
output += (1.0/self.loss_monte_carlo) * worst_case
loss = losses.normal_potential_energy(labels, predictions, self.prior_mean,
self.prior_var, self.q, self.loss_func)
elif(int(self.robust_train) == 4):
predictions = self.model(features)
self.epsilon = max(0.0001, self.epsilon)
self.eps_dist = tfp.distributions.Exponential(1.0/self.epsilon)
output = tf.zeros(predictions.shape)
for _mc_ in range(self.loss_monte_carlo):
#eps = tfp.random.rayleigh([1], scale=self.epsilon)
eps = self.eps_dist.sample()
features_adv = analyzers.FGSM(self, features, self.attack_loss, eps=self.epsilon, num_models=-1)
worst_case = self.model(features_adv)
output += (1.0/self.loss_monte_carlo) * worst_case
loss = losses.normal_potential_energy(labels, predictions, self.prior_mean,
self.prior_var, self.q, self.loss_func)
# Get the gradients
weight_gradient = tape.gradient(loss, self.model.trainable_variables)
temp_p = []
for i in range(len(weight_gradient)):
wg = tf.math.multiply(weight_gradient[i], lrate/self.batches)
temp_p.append(tf.math.add(self.p[i], wg)) # maybe come back and make this subtraction
self.p = np.asarray(temp_p)
self.train_loss(loss)
self.train_metric(labels, predictions)
#self.train_rob(labels, worst_case)
return self.posterior_mean, self.posterior_var
def step(self, features, labels, lrate):
alpha = lrate
beta_1 = self.beta_1
beta_2 = self.beta_2
lam = self.lam
posti_var = self.posterior_var
posti_mean = self.posterior_mean
N = float(self.batch_size) # batch size
with tf.GradientTape(persistent=True) as tape:
# Get the probabilities
predictions = self.model(features)
# Calculate the loss
if (int(self.robust_train) == 0):
loss = self.loss_func(labels, predictions)
elif (int(self.robust_train) == 1):
logit_l, logit_u = analyzers.IBP(
self,
features,
self.model.trainable_variables,
eps=self.epsilon)
v1 = tf.one_hot(labels, depth=10)
v2 = 1 - tf.one_hot(labels, depth=10)
worst_case = tf.math.add(tf.math.multiply(v2, logit_u),
tf.math.multiply(v1, logit_l))
worst_case = self.model.layers[-1].activation(worst_case)
loss = self.loss_func(labels, predictions, worst_case,
self.robust_lambda)
#self.train_rob(labels, worst_case)
elif (int(self.robust_train) == 2):
features_adv = analyzers.FGSM(self,
features,
self.attack_loss,
eps=self.epsilon,
num_models=-1)
# Get the probabilities
worst_case = self.model(features_adv)
# Calculate the loss
loss = self.loss_func(labels, predictions, worst_case,
self.robust_lambda)
weight_gradient = tape.gradient(loss, self.model.trainable_variables)
g = np.asarray(weight_gradient)
sq_grad = []
for i in range(len(weight_gradient)):
sq_grad.append(
tf.math.multiply(weight_gradient[i], weight_gradient[i]))
self.m[i] = (beta_1 * self.m[i]) + ((1 - beta_1) *
(g[i] +
((lam * posti_mean[i]) / N)))
posti_var[i] = (beta_2 * posti_var[i]) + ((1 - beta_2) *
(sq_grad[i]))
sq_grad = np.asarray(sq_grad)
self.m = np.asarray(self.m)
posti_var = np.asarray(posti_var)
for i in range(len(weight_gradient)):
m_ = self.m[i] / (1 - beta_1)
s_ = np.sqrt(posti_var[i]) + lam / N
posti_mean[i] = posti_mean[i] - (alpha * (m_ / s_))
self.model.set_weights(posti_mean)
self.train_loss(loss)
self.train_metric(labels, predictions)
return posti_mean, posti_var
def step(self, features, labels, lrate):
# OPTIMIZATION PARAMETERS:
alpha = lrate #self.alpha
beta_1 = self.beta_1
beta_2 = self.beta_2
lam = self.lam
N = self.N #60000
#N = float(self.batch_size) # batch size
self.posterior_mean = self.model.get_weights()
v1 = tf.one_hot(labels, depth=10)
v2 = 1 - tf.one_hot(labels, depth=10)
init_weights = []
for i in range(len(self.posterior_mean)):
var = tf.math.add(tf.math.sqrt(N * self.posterior_var[i]), lam)
var = tf.math.reciprocal(var)
sample = tf.random.normal(shape=self.posterior_var[i].shape,
mean=0,
stddev=1.0)
sample = tf.math.multiply(var, sample)
sample = tf.math.add(self.posterior_mean[i], sample)
init_weights.append(sample)
self.model.set_weights(init_weights)
with tf.GradientTape(persistent=True) as tape:
# Get the probabilities
predictions = self.model(features)
# Calculate the loss
if (int(self.robust_train) == 0):
loss = self.loss_func(labels, predictions)
elif (int(self.robust_train) == 1):
logit_l, logit_u = analyzers.IBP(
self,
features,
self.model.trainable_variables,
eps=self.epsilon)
worst_case = tf.math.add(tf.math.multiply(v2, logit_u),
tf.math.multiply(v1, logit_l))
worst_case = self.model.layers[-1].activation(worst_case)
output = (self.robust_lambda * predictions) + (
(1 - self.robust_lambda) * worst_case)
loss = self.loss_func(labels, output)
elif (int(self.robust_train) == 2):
features_adv = analyzers.PGD(self,
features,
self.attack_loss,
eps=self.epsilon,
num_models=-1)
# Get the probabilities
worst_case = self.model(features_adv)
# Calculate the loss
output = (self.robust_lambda * predictions) + (
(1 - self.robust_lambda) * worst_case)
loss = self.loss_func(labels, output)
elif (int(self.robust_train) == 3):
output = tf.zeros(predictions.shape)
self.epsilon = max(0.0001, self.epsilon)
self.eps_dist = tfp.distributions.Exponential(1.0 /
self.epsilon)
for _mc_ in range(self.loss_monte_carlo):
#eps = tfp.random.rayleigh([1], scale=self.epsilon)
eps = self.eps_dist.sample()
logit_l, logit_u = analyzers.IBP(
self,
features,
self.model.trainable_variables,
eps=eps)
v1 = tf.one_hot(labels, depth=10)
v2 = 1 - tf.one_hot(labels, depth=10)
v1 = tf.squeeze(v1)
v2 = tf.squeeze(v2)
worst_case = tf.math.add(tf.math.multiply(v2, logit_u),
tf.math.multiply(v1, logit_l))
worst_case = self.model.layers[-1].activation(worst_case)
one_hot_cls = tf.one_hot(labels, depth=10)
output += (1.0 / self.loss_monte_carlo) * worst_case
loss = self.loss_func(labels, output)
elif (int(self.robust_train) == 4):
output = tf.zeros(predictions.shape)
self.epsilon = max(0.0001, self.epsilon)
self.eps_dist = tfp.distributions.Exponential(1.0 /
self.epsilon)
for _mc_ in range(self.loss_monte_carlo):
#eps = tfp.random.rayleigh([1], scale=self.epsilon)
eps = self.eps_dist.sample()
features_adv = analyzers.FGSM(self,
features,
self.attack_loss,
eps=self.epsilon,
num_models=-1)
worst_case = self.model(features_adv)
output += (1.0 / self.loss_monte_carlo) * worst_case
loss = self.loss_func(labels, output)
weight_gradient = tape.gradient(loss, self.model.trainable_variables)
g = np.asarray(weight_gradient)
sq_grad = []
for i in range(len(weight_gradient)):
sq_grad.append(
tf.math.multiply(weight_gradient[i], weight_gradient[i]))
self.m[i] = (beta_1 * self.m[i]) + ((1 - beta_1) * (g[i] + (
(lam * self.posterior_mean[i]) / N)))
self.posterior_var[i] = (beta_2 * self.posterior_var[i]) + (
(1 - beta_2) * (sq_grad[i]))
#print("sq: ", sq_grad)
sq_grad = np.asarray(sq_grad)
self.m = np.asarray(self.m)
self.posterior_var = np.asarray(self.posterior_var)
for i in range(len(weight_gradient)):
m_ = self.m[i] / (1 - beta_1)
s_ = np.sqrt(self.posterior_var[i]) + lam / N
self.posterior_mean[i] = self.posterior_mean[i] - (alpha *
(m_ / s_))
self.model.set_weights(self.posterior_mean)
self.train_loss(loss)
self.train_metric(labels, predictions)
#self.posterior_mean = posti_mean
#self.posterior_var = posti_var
return self.posterior_mean, self.posterior_var