def train_model(batchsize, floder_log, floder_model):
# 数据读入
X_train, y_train = readcifar10.read(batchsize=batchsize,
type="train",
aug_data=True)
X_test, y_test = readcifar10.read(batchsize=batchsize,
type="test",
aug_data=False)
def train():
#定义batchsize
batchsize = 64
#定义日志存放目录
floder_log = 'logdirs-resnet'
#定义model存放路径
floder_model = 'model-resnet'
if not os.path.exists(floder_log):
os.mkdir(floder_log)
if not os.path.exists(floder_model):
os.mkdir(floder_model)
#定义训练样本日志
tr_summary = set()
#定义测试样本日志
te_summary = set()
##data
tr_im, tr_label = readcifar10.read(batchsize, 0, 1)
te_im, te_label = readcifar10.read(batchsize, 1, 0)
##net
#定义输入数据
input_data = tf.placeholder(tf.float32, shape=[None, 32, 32, 3],
name='input_data')
input_label = tf.placeholder(tf.int64, shape=[None],
name='input_label')
keep_prob = tf.placeholder(tf.float32, shape=None,
name='keep_prob')
is_training = tf.placeholder(tf.bool, shape=None,
name='is_training')
logits = resnet.model_resnet(input_data, keep_prob=keep_prob, is_training=is_training)
##loss
total_loss, l2_loss = loss(logits, input_label)
#记录loss的日志信息
tr_summary.add(tf.summary.scalar('train total loss', total_loss))
tr_summary.add(tf.summary.scalar('test l2_loss', l2_loss))
te_summary.add(tf.summary.scalar('train total loss', total_loss))
te_summary.add(tf.summary.scalar('test l2_loss', l2_loss))
##accurancy
#获取当前概率分布中最大的值所对应的索引
pred_max = tf.argmax(logits, 1)
#判断这个值是否和label相等
correct = tf.equal(pred_max, input_label)
#定义精度
accurancy = tf.reduce_mean(tf.cast(correct, tf.float32))
#记录精度的日志信息
tr_summary.add(tf.summary.scalar('train accurancy', accurancy))
def train():
batchsize = 64
floder_log = 'logdirs-resnet'
floder_model = 'model-resnet'
if not os.path.exists(floder_log):
os.mkdir(floder_log)
if not os.path.exists(floder_model):
os.mkdir(floder_model)
tr_summary = set()
te_summary = set()
##data
tr_im, tr_label = readcifar10.read(batchsize, 0, 1)
te_im, te_label = readcifar10.read(batchsize, 1, 0)
##net
input_data = tf.placeholder(tf.float32,
shape=[None, 32, 32, 3],
name='input_data')
input_label = tf.placeholder(tf.int64, shape=[None], name='input_label')
keep_prob = tf.placeholder(tf.float32, shape=None, name='keep_prob')
is_training = tf.placeholder(tf.bool, shape=None, name='is_training')
logits = resnet.model_resnet(input_data,
keep_prob=keep_prob,
is_training=is_training)
##loss
total_loss, l2_loss = loss(logits, input_label)
tr_summary.add(tf.summary.scalar('train total loss', total_loss))
tr_summary.add(tf.summary.scalar('test l2_loss', l2_loss))
te_summary.add(tf.summary.scalar('train total loss', total_loss))
te_summary.add(tf.summary.scalar('test l2_loss', l2_loss))
##accurancy
pred_max = tf.argmax(logits, 1)
correct = tf.equal(pred_max, input_label)
accurancy = tf.reduce_mean(tf.cast(correct, tf.float32))
tr_summary.add(tf.summary.scalar('train accurancy', accurancy))
te_summary.add(tf.summary.scalar('test accurancy', accurancy))
##op
global_step, op, lr = func_optimal(batchsize, total_loss)
tr_summary.add(tf.summary.scalar('train lr', lr))
te_summary.add(tf.summary.scalar('test lr', lr))
tr_summary.add(tf.summary.image('train image', input_data * 128 + 128))
te_summary.add(tf.summary.image('test image', input_data * 128 + 128))
with tf.Session() as sess:
sess.run(
tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer()))
tf.train.start_queue_runners(sess=sess, coord=tf.train.Coordinator())
saver = tf.train.Saver(tf.global_variables(), max_to_keep=5)
ckpt = tf.train.latest_checkpoint(floder_model)
if ckpt:
saver.restore(sess, ckpt)
epoch_val = 100
tr_summary_op = tf.summary.merge(list(tr_summary))
te_summary_op = tf.summary.merge(list(te_summary))
summary_writer = tf.summary.FileWriter(floder_log, sess.graph)
for i in range(50000 * epoch_val):
train_im_batch, train_label_batch = \
sess.run([tr_im, tr_label])
feed_dict = {
input_data: train_im_batch,
input_label: train_label_batch,
keep_prob: 0.8,
is_training: True
}
_, global_step_val, \
lr_val, \
total_loss_val, \
accurancy_val, tr_summary_str = sess.run([op,
global_step,
lr,
total_loss,
accurancy, tr_summary_op],
feed_dict=feed_dict)
summary_writer.add_summary(tr_summary_str, global_step_val)
if i % 100 == 0:
print("{},{},{},{}".format(global_step_val, lr_val,
total_loss_val, accurancy_val))
if i % (50000 // batchsize) == 0:
test_loss = 0
test_acc = 0
for ii in range(10000 // batchsize):
test_im_batch, test_label_batch = \
sess.run([te_im, te_label])
feed_dict = {
input_data: test_im_batch,
input_label: test_label_batch,
keep_prob: 1.0,
is_training: False
}
total_loss_val, global_step_val, \
accurancy_val, te_summary_str = sess.run([total_loss,global_step,
accurancy, te_summary_op],
feed_dict=feed_dict)
summary_writer.add_summary(te_summary_str, global_step_val)
test_loss += total_loss_val
test_acc += accurancy_val
print('test：', test_loss * batchsize / 10000,
test_acc * batchsize / 10000)
if i % 1000 == 0:
saver.save(
sess, "{}/model.ckpt{}".format(floder_model,
str(global_step_val)))
return