def dnn_train_mnist_cmb(self, mnist, train_configs):
"""Train the network on MNIST data"""
self.get_learning_rate()
self.get_loss()
self.get_accuracy()
self.get_opt_ce()
self.get_opt_mse()
# Init sess
self.sess = tf.InteractiveSession()
self.sess.run(tf.global_variables_initializer())
numbatch_trn = mnist.train.images.shape[0] // train_configs.batchsize
numbatch_val = mnist.validation.images.shape[0] // train_configs.batchsize
x_epoch = np.arange(0,train_configs.epochs,1)
y_loss_trn_m = np.zeros(x_epoch.shape)
y_loss_val_m = np.zeros(x_epoch.shape)
y_loss_trn = np.zeros(x_epoch.shape)
y_loss_val = np.zeros(x_epoch.shape)
y_acc_trn = np.zeros(x_epoch.shape)
y_acc_val = np.zeros(x_epoch.shape)
# Init all variables
timestamp = utils.get_timestamp()
print("[%s]: Epochs Trn_loss_mse Val_loss_mse Trn_loss_ce Val_loss_ce Trn_acc Val_acc" % (timestamp))
for i in range(train_configs.epochs):
lr_dict = {self.init_lr: train_configs.lr_init,
self.global_step: i,
self.decay_step: train_configs.batchsize,
self.decay_rate: train_configs.decay_rate}
loss_trn_all_m = 0.0
loss_val_all_m = 0.0
loss_trn_all = 0.0
loss_val_all = 0.0
acc_trn_all = 0.0
acc_val_all = 0.0
for i_trn in range(numbatch_trn):
data_trn, label_trn = mnist.train.next_batch(
batch_size=train_configs.batchsize)
train_dict = {
self.inputs: data_trn,
self.labels: label_trn,
self.outputs: data_trn,
self.is_training: True,
self.keep_prob: train_configs.keep_prob}
train_dict.update(lr_dict)
# train
_, loss_trn_m = self.sess.run(
[self.train_op_mse, self.cost_mse],
feed_dict=train_dict)
_, loss_trn, acc_trn = self.sess.run(
[self.train_op_ce, self.cost_ce, self.accuracy],
feed_dict=train_dict)
loss_trn_all_m += loss_trn_m
acc_trn_all += acc_trn
loss_trn_all += loss_trn
y_loss_trn_m[i] = loss_trn_all_m / numbatch_trn
y_acc_trn[i] = acc_trn_all / numbatch_trn
y_loss_trn[i] = loss_trn_all / numbatch_trn
# validation
for i_trn in range(numbatch_val):
data_val, label_val = mnist.validation.next_batch(
batch_size=train_configs.batchsize)
val_dict = {
self.inputs: data_val,
self.labels: label_val,
self.outputs: data_val,
self.is_training: False,
self.keep_prob: 1.0}
loss_val_m = self.sess.run(
self.cost_mse,
feed_dict=val_dict)
loss_val, acc_val = self.sess.run(
[self.cost_ce, self.accuracy],
feed_dict=val_dict)
loss_val_all_m += loss_val_m
acc_val_all += acc_val
loss_val_all += loss_val
y_loss_val_m[i] = loss_val_all_m / numbatch_val
y_acc_val[i] = acc_val_all / numbatch_val
y_loss_val[i] = loss_val_all / numbatch_val
# print results
if i % 5 == 0:
timestamp = utils.get_timestamp()
print('[%s]: %d %.8f %.8f %.8f %.8f %.4f %.4f' % (
timestamp, i,
y_loss_trn_m[i], y_loss_val_m[i],
y_loss_trn[i], y_loss_val[i],
y_acc_trn[i], y_acc_val[i]))
self.train_dict = {
"epochs": x_epoch,
"trn_loss_mse": y_loss_trn_m,
"val_loss_mse": y_loss_val_m,
"trn_loss_ce": y_loss_trn,
"val_loss_ce": y_loss_val,
"trn_acc_ce": y_acc_trn,
"val_acc_ce": y_acc_val}
def dnn_train_cmb(self, data, train_configs, labels=None):
"""Train the network with combined loss functions"""
self.get_learning_rate()
self.get_loss()
self.get_accuracy()
self.get_opt_ce()
self.get_opt_mse()
# get validation
data_trn,data_val = utils.gen_validation(
data, valrate=train_configs.valrate, label=labels)
# Init sess
self.sess = tf.InteractiveSession()
self.sess.run(tf.global_variables_initializer())
numbatch_trn = len(data_trn["data"]) // train_configs.batchsize
numbatch_val = len(data_val["data"]) // train_configs.batchsize
x_epoch = np.arange(0,train_configs.epochs,1)
y_loss_trn_m = np.zeros(x_epoch.shape)
y_loss_val_m = np.zeros(x_epoch.shape)
y_loss_trn = np.zeros(x_epoch.shape)
y_loss_val = np.zeros(x_epoch.shape)
y_acc_trn = np.zeros(x_epoch.shape)
y_acc_val = np.zeros(x_epoch.shape)
# Init all variables
timestamp = utils.get_timestamp()
print("[%s]: Epochs Trn_loss_mse Val_loss_mse Trn_loss_ce Val_loss_ce Trn_acc Val_acc" % (timestamp))
for i in range(train_configs.epochs):
lr_dict = {self.init_lr: train_configs.lr_init,
self.global_step: i,
self.decay_step: train_configs.batchsize,
self.decay_rate: train_configs.decay_rate}
loss_trn_all_m = 0.0
loss_val_all_m = 0.0
loss_trn_all = 0.0
loss_val_all = 0.0
acc_trn_all = 0.0
acc_val_all = 0.0
indices_trn = utils.gen_BatchIterator_label(
data_trn['data'],
data_trn['label'],
batch_size=train_configs.batchsize,
shuffle=True)
for i_trn in range(numbatch_trn):
idx_trn = indices_trn[i_trn*train_configs.batchsize:
(i_trn+1)*train_configs.batchsize]
train_dict = {
self.inputs: data_trn['data'][idx_trn],
self.labels: data_trn['label'][idx_trn],
self.outputs: data_trn['data'][idx_trn],
self.is_training: True,
self.keep_prob: train_configs.keep_prob}
train_dict.update(lr_dict)
# train
_, loss_trn_m = self.sess.run(
[self.train_op_mse, self.cost_mse],
feed_dict=train_dict)
_, loss_trn, acc_trn = self.sess.run(
[self.train_op_ce, self.cost_ce, self.accuracy],
feed_dict=train_dict)
loss_trn_all_m += loss_trn_m
acc_trn_all += acc_trn
loss_trn_all += loss_trn
y_loss_trn_m[i] = loss_trn_all_m / numbatch_trn
y_acc_trn[i] = acc_trn_all / numbatch_trn
y_loss_trn[i] = loss_trn_all / numbatch_trn
# validation
indices_val = utils.gen_BatchIterator_label(
data_val['data'],
data_val['label'],
batch_size=train_configs.batchsize,
shuffle=True)
for i_val in range(numbatch_val):
idx_val = indices_val[i_val*train_configs.batchsize:
(i_val+1)*train_configs.batchsize]
val_dict = {
self.inputs: data_val['data'][idx_val],
self.labels: data_val['label'][idx_val],
self.outputs: data_val['data'][idx_val],
self.is_training: False,
self.keep_prob: 1.0}
loss_val_m = self.sess.run(
self.cost_mse,
feed_dict=val_dict)
loss_val, acc_val = self.sess.run(
[self.cost_ce, self.accuracy],
feed_dict=val_dict)
loss_val_all_m += loss_val_m
acc_val_all += acc_val
loss_val_all += loss_val
y_loss_val_m[i] = loss_val_all_m / numbatch_val
y_acc_val[i] = acc_val_all / numbatch_val
y_loss_val[i] = loss_val_all / numbatch_val
# print results
if i % 5 == 0:
timestamp = utils.get_timestamp()
print('[%s]: %d %.8f %.8f %.8f %.8f %.4f %.4f' % (
timestamp, i,
y_loss_trn_m[i], y_loss_val_m[i],
y_loss_trn[i], y_loss_val[i],
y_acc_trn[i], y_acc_val[i]))
self.train_dict = {
"epochs": x_epoch,
"trn_loss_mse": y_loss_trn_m,
"val_loss_mse": y_loss_val_m,
"trn_loss_ce": y_loss_trn,
"val_loss_ce": y_loss_val,
"trn_acc_ce": y_acc_trn,
"val_acc_ce": y_acc_val}