def init_nets(self):
self.model = NN_class.learners(config=self.get_session_config())
self.model = self.model.init_NN_custom(self.process_id, self.nr_processes, self.classes, \
self.depth, self.batch_size)
z_file_dir = dir_path+'Layer_'+str(self.process_id)+'/'
z_file_path = 'z_'+str(self.process_id)+'_'+'0'
write_file(z_file_dir, z_file_path, self.model.sess.run(self.model.classifier['z_self']), self.process_id)
if self.process_id == 1:
w_self_file_dir = dir_path+'Layer_'+str(self.process_id)+'/'
Weights = []
w_self_file_path = 'w_'+str(self.process_id)+'_'+'0'
weight = self.model.sess.run(self.model.classifier['w_self'])
bias = self.model.sess.run(self.model.classifier['b_self'])
Weights.append(weight)
Weights.append(bias)
write_file(w_self_file_dir, w_self_file_path, Weights, self.process_id)
w_next_file_dir = dir_path+'Layer_'+str(self.process_id+1)+'/'
Weights = []
w_next_file_path = 'w_'+str(self.process_id+1)+'_'+'0'
weight = self.model.sess.run(self.model.classifier['w_next'])
bias = self.model.sess.run(self.model.classifier['b_next'])
Weights.append(weight)
Weights.append(bias)
write_file(w_next_file_dir, w_next_file_path, Weights, self.process_id)
def init_nets(self, act):
#self.Layers.append(self.depth[self.process_id-1])
#self.Layers.append(self.depth[self.process_id])
#print ("Process " + str(self.process_id))
self.Layers = [
self.depth[self.process_id - 1], self.depth[self.process_id]
]
#print ("Layers:", self.Layers)
self.model = NN_class.learners(config=self.get_session_config())
self.model = self.model.init_NN_custom(self.process_id, self.nr_processes, self.classes, self.Layers,\
self.depth, self.batch_size, self.lambda_value, act_function=act, optimizer=self.optimizer)
para_file_dir = dir_path + 'Layer_' + str(self.process_id) + '/'
if not self.process_id == self.nr_processes:
z_file_path = 'z_' + str(self.process_id) + '_' + '0'
write_file(para_file_dir, z_file_path,
self.model.sess.run(self.model.classifier['z_self']))
Weights = []
w_file_path = 'w_' + str(self.process_id) + '_' + '0'
weight = self.model.sess.run(self.model.classifier['w_self'])
bias = self.model.sess.run(self.model.classifier['b_self'])
Weights.append(weight)
Weights.append(bias)
write_file(para_file_dir, w_file_path, Weights)
def init_nets(self):
self.Layers = [self.depth[self.process_id-1], self.depth[self.process_id]]
self.model = NN_class.learners(config=self.get_session_config())
self.model = self.model.init_NN_custom(self.process_id, self.nr_processes, self.classes, self.Layers,\
self.depth, self.batch_size)
para_file_dir = dir_path+'Layer_'+str(self.process_id)+'/'
if not self.process_id == self.nr_processes:
z_file_path = 'z_'+str(self.process_id)+'_'+'0'
write_file(para_file_dir, z_file_path, self.model.sess.run(self.model.classifier['z_self']))
Weights = []
w_file_path = 'w_'+str(self.process_id)+'_'+'0'
weight = self.model.sess.run(self.model.classifier['w_self'])
bias = self.model.sess.run(self.model.classifier['b_self'])
Weights.append(weight)
Weights.append(bias)
write_file(para_file_dir, w_file_path, Weights)
def init_nets(self):
self.model = NN_class.learners(config=self.get_session_config())
self.model = self.model.init_NN_custom(self.process_id, \
self.nr_processes, self.classes, self.depth, self.batch_size, self.activation, self.optimizer)
if self.process_id == 2:
# The Z2
z_file_dir = dir_path + 'Layer_' + str(self.process_id) + '/'
z_file_path = 'z_' + str(self.process_id) + '_' + '0'
write_file(z_file_dir, z_file_path,
self.model.sess.run(self.model.classifier['z_self']),
self.process_id)
# The W1
w_prev_file_dir = dir_path + 'Layer_' + str(self.process_id -
1) + '/'
Weights = []
w_prev_file_path = 'w_' + str(self.process_id - 1) + '_' + '0'
weight = self.model.sess.run(self.model.classifier['w_prev'])
bias = self.model.sess.run(self.model.classifier['b_prev'])
Weights.append(weight)
Weights.append(bias)
write_file(w_prev_file_dir, w_prev_file_path, Weights,
self.process_id)
# The W2
w_self_file_dir = dir_path + 'Layer_' + str(self.process_id) + '/'
Weights = []
w_self_file_path = 'w_' + str(self.process_id) + '_' + '0'
weight = self.model.sess.run(self.model.classifier['w_self'])
bias = self.model.sess.run(self.model.classifier['b_self'])
Weights.append(weight)
Weights.append(bias)
write_file(w_self_file_dir, w_self_file_path, Weights,
self.process_id)
elif self.process_id == self.nr_processes + 1:
# The W4
w_self_file_dir = dir_path + 'Layer_' + str(self.process_id) + '/'
Weights = []
w_self_file_path = 'w_' + str(self.process_id) + '_' + '0'
weight = self.model.sess.run(self.model.classifier['w_self'])
bias = self.model.sess.run(self.model.classifier['b_self'])
Weights.append(weight)
Weights.append(bias)
write_file(w_self_file_dir, w_self_file_path, Weights,
self.process_id)
else:
# The Z3
z_file_dir = dir_path + 'Layer_' + str(self.process_id) + '/'
z_file_path = 'z_' + str(self.process_id) + '_' + '0'
write_file(z_file_dir, z_file_path,
self.model.sess.run(self.model.classifier['z_self']),
self.process_id)
# The W3
w_self_file_dir = dir_path + 'Layer_' + str(self.process_id) + '/'
Weights = []
w_self_file_path = 'w_' + str(self.process_id) + '_' + '0'
weight = self.model.sess.run(self.model.classifier['w_self'])
bias = self.model.sess.run(self.model.classifier['b_self'])
Weights.append(weight)
Weights.append(bias)
write_file(w_self_file_dir, w_self_file_path, Weights,
self.process_id)
def init_nets(self):
self.model = NN_class.learners(config=self.get_session_config())
self.model = self.model.init_NN_custom(self.process_id, \
self.nr_processes, self.classes, self.depth, self.batch_size, self.activation, self.optimizer)
self.print_stuff(self.process_id, 0)
