1、原始版本

import tensorflow as tf
print(tf.__version__)
1.13.1

2、安装TensorFlow2.0

pip install tensorflow==2.0.0-beta0

2.1 Cannot uninstall 'wrapt'.

解决：

pip install -U --ignore-installed wrapt enum34 simplejson netaddr

2.2 Consider using the `--user` option or check the permissions.

pip install tensorflow==2.0.0-beta0 --user

3  graphviz加载不上

     用：conda install graphviz

tensorflow模型的加载和训练

saver=tf.train.Saver(max_to_keep=1)##保留最新的模型 
with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        ckpt = tf.train.get_checkpoint_state('E:/201904/tensorflow_base/model/')  
# 这两个函数之间最大的区别是当父目录不存在的时候os.mkdir(path)不会创建，#os.makedirs(path)则会创建父目录。

        if ckpt and ckpt.model_checkpoint_path:
            saver.restore(sess,ckpt.model_checkpoint_path)
###
saver.save(sess,os.path.join(path,model_name))

tf.train.Saver(
    ['var_list=None', 'reshape=False', 'sharded=False', 'max_to_keep=5', 'keep_checkpoint_every_n_hours=10000.0', 'name=None', 'restore_sequentially=False', 'saver_def=None', 'builder=None', 'defer_build=False', 'allow_empty=False', 'write_version=2', 'pad_step_number=False', 'save_relative_paths=False', 'filename=None'],
)

官方例子：

saver = tf.train.Saver(...variables...)
# Launch the graph and train, saving the model every 1,000 steps.
sess = tf.Session()
for step in xrange(1000000):
    sess.run(..training_op..)
    if step % 1000 == 0:
        # Append the step number to the checkpoint name:
        saver.save(sess, 'my-model', global_step=step)