前言
在TensorFlow中graph由op构成，前面已经定义了构建一个完整graph需要的op，现在将这些op有序组合起来构成graph，然后启动一个Session并运行graph。
在TensorFlow中，采用计算图的方式构建模型并训练。
TensorFlow separates definition of computations from their execution
- assemble a graph
- use a session to execute operations in the graph.
准备工作
前面主要是定义一些文件路径：
def training():
"""Traing model using train iamge and label, validation using val image and label."""
# project dir
project_dir = os.getcwd()
# train data val data path
train_data_path = os.path.join(project_dir, 'cache', 'train.tfrecords')
val_data_path = os.path.join(project_dir, 'cache', 'val.tfrecords')
# logs path
logs_train_dir = os.path.join(project_dir, 'logs', 'train/')
logs_val_dir = os.path.join(project_dir, 'logs', 'val/')
构造graph
将op联系起来构成完整的graph，代码中定义了两个 placehoder 主要是为了实现训练和验证同时进行。
注意：用定义的placeholder构造graph。
with tf.Graph().as_default():
# 利用read_and_decode()函数批量读取数据输入到graph中
train_img_batch, train_label_batch = read_and_decode(train_data_path,
batch_size=BATCH_SIZE,
one_hot=ONE_HOT)
val_img_batch, val_label_batch = read_and_decode(val_data_path,
batch_size=N_VAL,
one_hot=ONE_HOT)
# placeholder
image = tf.placeholder(tf.float32, shape=[None, IMG_W, IMG_H, 3], name='image')
label_ = tf.placeholder(tf.int32, shape=[None, N_CLASSES], name='label')
# model
logits = frame_model.inference(image,
N_CLASSES,
visualize)
# loss function
loss = frame_model.losses(train_logits, label_)
# optimizer
optimizer = frame_model.trainning(train_loss, learning_rate)
# evaluation
accuracy = frame_model.evaluation(train_logits, label_)
# init op
init_op = tf.global_variables_initializer()
# merge summary
summary_op = tf.summary.merge_all()
在Session中运行graph
启动一个Session并运行graph，其中每100步打印训练信息；每500步验证模型，并运行summary_op，并写入disk，需要注意的是运行summary_op时，也要通过feed_dict供给数据；每2000步保存模型。
# initial a Session()
with tf.Session() as sess:
# initial tf.train.Saver() class
saver = tf.train.Saver()
# run init op
sess.run(init_op)
# start input enqueue threads to read data
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
# initial tf.summary.FileWriter() class
train_writer = tf.summary.FileWriter(logs_train_dir, sess.graph)
val_writer = tf.summary.FileWriter(logs_val_dir, sess.graph)
try:
for step in xrange(MAX_STEP):
if coord.should_stop():
break
# get train batch data
train_img, train_label = sess.run([train_img_batch, train_label_batch])
start_time = time.time()
# run ops
_, tra_loss, tra_accuracy = sess.run([optimizer, loss, accuracy],
feed_dict={image: train_img, label_: train_label})
duration = time.time() - start_time
# print info of training
if step % 100 == 0 or (step + 1) == MAX_STEP:
sec_per_batch = float(duration) # training time of a batch
print('Step {}, train loss = {:.2f}, train accuracy = {:.2f}%, sec_per_batch = {:.2f}s'.format(step,
tra_loss,
tra_accuracy,
sec_per_batch))
# run summary_op时也要用feed_dict供给数据
if step % 500 == 0 or (step + 1) == MAX_STEP:
# run summary op and write train summary to disk
summary_str = sess.run(summary_op,
feed_dict={image: train_img, label_: train_label})
train_writer.add_summary(summary_str, step)
# get val data batch
val_img, val_label = sess.run([val_img_batch, val_label_batch])
# run ops
val_loss, val_acc, summary_str = sess.run([loss, accuracy, summary_op],
feed_dict={image: val_img, label_:val_label})
print('*** Step {}, val loss = {:.2f}, val accuracy = {:.2f}% ***'.format(step, val_loss, val_acc))
# run summary op and write val summary to disk
val_writer.add_summary(summary_str, step)
# save model
if step % 2000 == 0 or (step + 1) == MAX_STEP:
checkpoint_path = os.path.join(logs_train_dir, 'model.ckpt')
saver.save(sess, checkpoint_path, global_step=step)
# 捕捉异常
except tf.errors.OutOfRangeError:
print('Done training -- epoch limit reached')
finally:
coord.request_stop()
coord.join(threads)
总结
思路很清晰，但是真正上手编还是会出现各种意想不到的问题，在TensorFlow中debug也不方便，感觉结果不正常首先要看代码实现是否正确，确保代码正确后再去考虑是否有其他错误。
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