def on_train_begin(self, logs=None):
sess = K.get_session()
# Split variables based on type -> float32 vs all else
test_v = tf.Variable([0], dtype=tf.float32)
all_vars = tf.trainable_variables()
float_vars = [v for v in all_vars if v.dtype == test_v.dtype]
other_vars = [v for v in all_vars if v.dtype != test_v.dtype]
# Initialize variables and broadcast from head node
sess.run(tf.variables_initializer(all_vars))
new_vars = mc.broadcast(float_vars, 0)
bcast = tf.group(
*[tf.assign(v, new_vars[k]) for k, v in enumerate(float_vars)])
sess.run(bcast)
# Validate Broadcast
if self.validate:
py_all_vars = [sess.run(v) for v in float_vars]
var_types = [
np.array([v]) if type(v) == np.float32 else v
for v in py_all_vars
]
if mc.get_rank() is 0:
if (mc.check_buffers_match(var_types, 1) != 0):
tf.logging.error(
"Not all processes have the same initial model!")
else:
tf.logging.info("Initial model is consistent on all ranks")
def begin(self):
if not self.bcast:
new_vars = mc.broadcast(tf.trainable_variables(), 0)
self.bcast = tf.group(*[
tf.assign(v, new_vars[k])
for k, v in enumerate(tf.trainable_variables())
])
def train(self):
train_step, loss, lossL1Train,train_true,train_predict = self.optimize()
lossL1Val,val_true,val_predict = self.validation_loss()
lossL1Test,test_true,test_predict = self.test_loss()
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.4
### taking config from the MKL benchmarks.
config.allow_soft_placement = True
config.intra_op_parallelism_threads = 1 ## default
config.inter_op_parallelism_threads = 2 ## Default
#used to save the model
saver = tf.train.Saver()
global best_validation_accuracy
global last_improvement
global total_iterations
best_validation_accuracy = 1.0 #Best validation accuracy seen so far
last_improvement = 0 #Iteration-number for last improvement to validation accuracy.
require_improvement = hp.RUNPARAM['require_improvement'] #Stop optimization if no improvement found in this many iterations.
total_iterations = 0 #Counter for total number of iterations performed so far.
#initialize the CPE ML Plugin with one team (single thread for now) and the model size
totsize = sum([reduce(lambda x, y: x*y, v.get_shape().as_list()) for v in tf.trainable_variables()])
mc.init(1, 1, totsize, "tensorflow")
hp.RUNPARAM['batch_per_epoch'] = hp.RUNPARAM['batch_per_epoch'] / mc.get_nranks()
hp.RUNPARAM['batch_per_epoch_val'] = hp.RUNPARAM['batch_per_epoch_val'] / mc.get_nranks()
totsteps = hp.RUNPARAM['num_epoch'] * hp.RUNPARAM['batch_per_epoch']
mc.config_team(0, 0, totsteps, totsteps, 2, 50)
if (mc.get_rank() == 0):
print("+------------------------------+")
print("| CosmoFlow |")
print("| # Ranks = {:5d} |".format(mc.get_nranks()))
print("| Global Batch = {:6d} |".format(mc.get_nranks() * hp.Input['BATCH_SIZE']))
print("| # Parameters = {:9d} |".format(totsize))
print("+------------------------------+")
#use the CPE ML Plugin to broadcast initial model parameter values
new_vars = mc.broadcast(tf.trainable_variables(),0)
bcast = tf.group(*[tf.assign(v,new_vars[k]) for k,v in enumerate(tf.trainable_variables())])
if(self.is_train):
with tf.Session(config=config) as sess:
losses_train = []
losses_val = []
losses = []
val_accuracys = []
data_accuracys = []
#do all parameter initializations
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
sess.run(bcast)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
elapsed_time = 0.
for epoch in range(hp.RUNPARAM['num_epoch']):
save_path = os.path.join(hp.Path['Model_path'], 'best_validation')
total_iterations += 1
start_time = time.time()
loss_per_epoch_val = 0
loss_per_epoch_train = 0
for i in range(hp.RUNPARAM['batch_per_epoch']):
step_start_time = time.time()
_,lossTrain,lossL1Train_,train_true_,train_predict_ = sess.run([train_step,loss,lossL1Train,train_true,train_predict])
step_finish_time = time.time()
elapsed_time += (step_finish_time-step_start_time)
samps_per_sec = mc.get_nranks() * (epoch * hp.RUNPARAM['batch_per_epoch'] * hp.Input['BATCH_SIZE'] + (i+1) * hp.Input['BATCH_SIZE']) / elapsed_time
if (mc.get_rank() == 0):
print("Train Step: " + str(i) + ", Samples/Sec = " + str(samps_per_sec) + ", Loss = " + str(lossTrain))
loss_per_epoch_train +=lossL1Train_
global_loss = np.array([loss_per_epoch_train],dtype=np.float32)
mc.average(global_loss)
loss_per_epoch_train = global_loss / hp.RUNPARAM['batch_per_epoch']
losses.append(loss_per_epoch_train)
losses_train.append(loss_per_epoch_train)
for i in range(hp.RUNPARAM['batch_per_epoch_val']):
if (mc.get_rank() == 0):
print("Val Step = " + str(i))
loss_,val_true_,val_predict_ = sess.run([lossL1Val,val_true,val_predict])
loss_per_epoch_val += loss_
global_loss = np.array([loss_per_epoch_val],dtype=np.float32)
mc.average(global_loss)
loss_per_epoch_val = global_loss / hp.RUNPARAM['batch_per_epoch_val']
losses_val.append(loss_per_epoch_val)
if(loss_per_epoch_val < best_validation_accuracy):
best_validation_accuracy = loss_per_epoch_val
last_improvement = total_iterations
if (mc.get_rank() == 0):
saver.save(sess=sess, save_path=save_path)
if (mc.get_rank() == 0):
print("Epoch {} took {:.3f}s".format(epoch, time.time() - start_time))
print " training loss: %.3f" %(loss_per_epoch_train)
print " validation loss: %.3f" %(loss_per_epoch_val)
print " best loss: %.3f"%best_validation_accuracy
np.savetxt(os.path.join(hp.Path['train_result'],'loss_train.txt'),losses_train)
np.savetxt(os.path.join(hp.Path['val_result'],'loss_val.txt'),losses_val)
np.savetxt(os.path.join(hp.Path['train_result'],'losses.txt'),losses)
#np.savetxt(os.path.join(hp.Path['train_result'],'train_pred'+str(epoch)+'.txt'),np.c_[train_true_,train_predict_])
#np.savetxt(os.path.join(hp.Path['val_result'],'val_pred'+str(epoch)+'.txt'),np.c_[val_true_,val_predict_])
if(total_iterations - last_improvement > require_improvement):
if (mc.get_rank() == 0):
print ("No improvement found in a while, stopping optimization.")
break
coord.request_stop();
coord.join(threads);
if(self.is_test and mc.get_rank() == 0):
save_path = os.path.join(hp.Path['Model_path'], 'best_validation')
if self.save_path != None:
save_path = self.save_path
with tf.Session() as sess:
saver.restore(sess=sess,save_path=save_path)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
loss_test = []
for i in range(0,hp.RUNPARAM['iter_test']):
start_time = time.time()
lossL1Test_,test_true_,test_predict_ = sess.run([lossL1Test,test_true,test_predict])
loss_test.append(lossL1Test_)
print("Box {} took {:.3f}s".format(i, time.time() - start_time))
print " test loss: %.3f"%lossL1Test_
np.savetxt(os.path.join(hp.Path['test_result'],'test_batch_'+str(i)+'.txt'),np.c_[test_true_,test_predict_])
np.savetxt(os.path.join(hp.Path['test_result'],'loss_test.txt'),loss_test)
coord.request_stop()
coord.join(threads)
#cleanup the CPE ML Plugin
mc.finalize()