def train(self):
self.sess.run(tf.global_variables_initializer())
self.itr_epoch = len(self.source_training_data[0]) // self.bs
source_training_acc = 0.0
source_training_loss = 0.0
for e in range(1, self.eps + 1):
for itr in range(self.itr_epoch):
feed_dict_train, feed_dict_eval = self.getBatchData()
_ = self.sess.run(self.train_op, feed_dict=feed_dict_train)
_training_accuracy, _training_loss = self.sess.run([self.accuracy_source, self.loss],
feed_dict=feed_dict_eval)
source_training_acc += _training_accuracy
source_training_loss += _training_loss
summary = self.sess.run(self.merged, feed_dict=feed_dict_eval)
source_training_acc = float(source_training_acc / self.itr_epoch)
source_training_loss = float(source_training_loss / self.itr_epoch)
source_validation_acc, source_validation_loss = self.validation_procedure(
validation_data=self.source_validation_data, distribution_op=self.distribution_source,
loss_op=self.loss, inputX=self.x_source, inputY=self.y_source)
log1 = "Epoch: [%d], Domain: Source, Training Accuracy: [%g], Validation Accuracy: [%g], " \
"Training Loss: [%g], Validation Loss: [%g], Time: [%s]" % (
e, source_training_acc, source_validation_acc, source_training_loss, source_validation_loss,
time.ctime(time.time()))
self.plt_epoch.append(e)
self.plt_training_accuracy.append(source_training_acc)
self.plt_training_loss.append(source_training_loss)
self.plt_validation_accuracy.append(source_validation_acc)
self.plt_validation_loss.append(source_validation_loss)
CoGAN_utils.plotAccuracy(x=self.plt_epoch,
y1=self.plt_training_accuracy,
y2=self.plt_validation_accuracy,
figName=self.model,
line1Name='training',
line2Name='validation',
savePath=self.ckptDir)
CoGAN_utils.plotLoss(x=self.plt_epoch,
y1=self.plt_training_loss,
y2=self.plt_validation_loss,
figName=self.model,
line1Name='training',
line2Name='validation',
savePath=self.ckptDir)
CoGAN_utils.save2file(log1, self.ckptDir, self.model)
self.writer.add_summary(summary, e)
self.saver.save(self.sess, self.ckptDir + self.model + '-' + str(e))
self.test_procedure(self.source_test_data, distribution_op=self.distribution_source,
inputX=self.x_source,
inputY=self.y_source, mode='source')
source_training_acc = 0.0
source_training_loss = 0.0
def train(self):
print('Initialize parameters')
self.sess.run(tf.global_variables_initializer())
print('Global variables initialization finished')
print('Reload parameters')
dict_var = {}
for i in self.src_var:
for j in self.tar_var:
if i.name[i.name.find('/') + 1:] in j.name[j.name.find('/') +
1:]:
dict_var[i.name[:-2]] = j
self.src_encoder_reloadSaver = tf.train.Saver(var_list=self.src_var)
self.tar_encoder_reloadSaver = tf.train.Saver(var_list=dict_var)
self.classifier_reloadSaver = tf.train.Saver(var_list=self.cla_var)
self.src_encoder_reloadSaver.restore(self.sess, self.reloadPath)
self.tar_encoder_reloadSaver.restore(self.sess, self.reloadPath)
self.classifier_reloadSaver.restore(self.sess, self.reloadPath)
print(
'source encoder, target encoder and classifier have been successfully reloaded !'
)
self.itr_epoch = len(self.source_training_data[0]) // self.bs
source_training_acc = 0.0
source_training_loss = 0.0
for e in range(1, self.eps + 1):
for itr in range(self.itr_epoch):
feed_dict_train, feed_dict_eval = self.getBatchData()
_ = self.sess.run(self.d_train_op, feed_dict=feed_dict_train)
_ = self.sess.run(self.g_train_op, feed_dict=feed_dict_train)
_training_accuracy, _training_loss = self.sess.run(
[self.accuracy_source, self.supervised_loss],
feed_dict=feed_dict_eval)
source_training_acc += _training_accuracy
source_training_loss += _training_loss
summary = self.sess.run(self.merged, feed_dict=feed_dict_eval)
source_training_acc = float(source_training_acc / self.itr_epoch)
source_training_loss = float(source_training_loss / self.itr_epoch)
source_validation_acc, source_validation_loss = self.validation_procedure(
validation_data=self.source_validation_data,
distribution_op=self.distribution_source,
loss_op=self.supervised_loss,
inputX=self.x_source,
inputY=self.y_source)
log1 = "Epoch: [%d], Domain: Source, Training Accuracy: [%g], Validation Accuracy: [%g], " \
"Training Loss: [%g], Validation Loss: [%g], Time: [%s]" % (
e, source_training_acc, source_validation_acc, source_training_loss, source_validation_loss,
time.ctime(time.time()))
self.plt_epoch.append(e)
self.plt_training_accuracy.append(source_training_acc)
self.plt_training_loss.append(source_training_loss)
self.plt_validation_accuracy.append(source_validation_acc)
self.plt_validation_loss.append(source_validation_loss)
CoGAN_utils.plotAccuracy(x=self.plt_epoch,
y1=self.plt_training_accuracy,
y2=self.plt_validation_accuracy,
figName=self.model,
line1Name='training',
line2Name='validation',
savePath=self.ckptDir)
CoGAN_utils.plotLoss(x=self.plt_epoch,
y1=self.plt_training_loss,
y2=self.plt_validation_loss,
figName=self.model,
line1Name='training',
line2Name='validation',
savePath=self.ckptDir)
CoGAN_utils.save2file(log1, self.ckptDir, self.model)
self.writer.add_summary(summary, e)
self.saver.save(self.sess,
self.ckptDir + self.model + '-' + str(e))
self.test_procedure(self.source_test_data,
distribution_op=self.distribution_source,
inputX=self.x_source,
inputY=self.y_source,
mode='source')
self.test_procedure(self.target_test_data,
distribution_op=self.distribution_target,
inputX=self.x_target,
inputY=self.y_target,
mode='target')
source_training_acc = 0.0
source_training_loss = 0.0