def get_model_params(self,name_list,sess=None):
sess=assert_session(sess)
if isinstance(name_list,str):
name_list=[name_list]
elif not isinstance(name_list,(list,tuple)):
raise TypeError('name_list must be str, list or tuple')
value_list=sess.run([self.tf_variables[v] for v in name_list])
return dict(zip(name_list,value_list))
def assign_to_variables(tf_assign_ops,tf_assign_phs,value_dict,sess=None):
sess=assert_session(sess)
assign_ops=[]
feed_dict={}
for k,v in value_dict.items():
assign_ops.append(tf_assign_ops[k])
feed_dict[tf_assign_phs[k]]=v
sess.run(assign_ops,feed_dict=feed_dict)
def compute_fisher_information(self,X_train,sess=None):
#may be only applicable when the last layer goes train with softmax function
sess=assert_session(sess)
rv_grad_keys=list(self.rv_grads.keys())
rv_grad_tensors=[self.rv_grads[k] for k in rv_grad_keys]
F_accum={}
for v in rv_grad_keys:
F_accum[v]=np.zeros(self.rv_grads[v].shape.as_list())
for i in range(len(X_train)):
rv_grads_val=sess.run(rv_grad_tensors,feed_dict={self.tf_tensors['input']:X_train[i:i+1]})
for j,v in enumerate(rv_grad_keys):
F_accum[v]+=rv_grads_val[j]**2
for k in F_accum.keys():
F_accum[k]/=len(X_train)
return F_accum
def reset_ewc_variables(self,sess=None):
sess=assert_session(sess)
sess.run(tf.variables_initializer(list(self.fisher_variables.values())+list(self.prev_variables.values())))
def get_all_fisher_variables(self,sess=None):
sess=assert_session(sess)
keys_list=list(self.fisher_variables.keys())
value_list=sess.run([self.fisher_variables[v] for v in keys_list])
return dict(zip(keys_list,value_list))
def set_fisher_variables(self,fisher_var,sess=None):
sess=assert_session(sess)
assign_to_variables(self.fisher_variables_ops,self.fisher_variables_phs,fisher_var,sess)
def get_all_regularizable_variables(self,sess=None):
sess=assert_session(sess)
return self.get_model_params(list(self.regularizable_variables.keys()),sess)
def set_prev_variables(self,prev_rv,sess=None):
sess=assert_session(sess)
assign_to_variables(self.prev_variables_ops,self.prev_variables_phs,prev_rv,sess)
def load_model(self,file_name,sess=None):
sess=assert_session(sess)
model_params=pickle.load(open(file_name,'rb'))
self.set_model_params(model_params,sess)
def save_model(self,file_name,sess=None):
sess=assert_session(sess)
model_params=self.get_all_model_params(sess)
pickle.dump(model_params,open(file_name,'wb'))
def set_model_params(self,value_dict,sess=None):
sess=assert_session(sess)
assign_to_variables(self.tf_variables_ops,self.tf_variables_phs,value_dict,sess)