tensorflow_Variable.txt

# Create a variable with a random value.
weights = tf.Variable(tf.random_normal([784, 200], stddev=0.35),
                      name="weights")
# Create another variable with the same value as 'weights'.
w2 = tf.Variable(weights.initialized_value(), name="w2")
# Create another variable with twice the value of 'weights'
w_twice = tf.Variable(weights.initialized_value() * 2.0, name="w_twice")


https://www.tensorflow.org/versions/r0.10/api_docs/python/state_ops.html




//===
https://www.tensorflow.org/versions/r0.9/how_tos/variables/index.html

use variables to hold and update parameters. 
Variables are in-memory buffers containing tensors. 
They must be explicitly initialized and can be saved to disk during and after training. 


* pass a Tensor as its initial value to the Variable() constructor




//=== Calling tf.Variable() adds several ops to the graph:

- A variable op that holds the variable value.
- An initializer op that sets the variable to its initial value. This is actually a tf.assign op.
- The ops for the initial value, such as the zeros op for the biases variable in the example 
are also added to the graph.

The value returned by tf.Variable() value is an instance of the Python class tf.Variable.


//===
# Create two variables.
weights = tf.Variable(tf.random_normal([784, 200], stddev=0.35),
                      name="weights")
biases = tf.Variable(tf.zeros([200]), name="biases")
...
# Add an op to initialize the variables.
init_op = tf.initialize_all_variables()

# Later, when launching the model
with tf.Session() as sess:
  # Run the init operation.
  sess.run(init_op)
  ...
  # Use the model
  ...
  
  
//===  
To initialize a new variable from the value of another variable use the other variable's initialized_value() property. You can use the initialized value directly as the initial value for the new variable, or you can use it as any other tensor to compute a value for the new variable.

# Create a variable with a random value.
weights = tf.Variable(tf.random_normal([784, 200], stddev=0.35),
                      name="weights")
# Create another variable with the same value as 'weights'.
w2 = tf.Variable(weights.initialized_value(), name="w2")
# Create another variable with twice the value of 'weights'
w_twice = tf.Variable(weights.initialized_value() * 2.0, name="w_twice")  


//===  tf.train.Saver 
Saving and Restoring

The easiest way to save and restore a model is to use a tf.train.Saver object. The constructor 
adds save and restore ops to the graph for all, or a specified list, of the variables in the graph

To understand what variables are in a checkpoint, you can use the inspect_checkpoint library, 
and in particular, the print_tensors_in_checkpoint_file function.