Tensorflow 学习二 tf.Session，.run
以下为tf.Session().run 说明，其接受的fetches参数可以有多种类型。
def run(self, fetches, feed_dict=None, options=None, run_metadata=None):
"""Runs operations and evaluates tensors in `fetches`.
This method runs one "step" of TensorFlow computation, by
running the necessary graph fragment to execute every `Operation`
and evaluate every `Tensor` in `fetches`, substituting the values in
`feed_dict` for the corresponding input values.
The `fetches` argument may be a single graph element, or an arbitrarily
nested list, tuple, namedtuple, dict, or OrderedDict containing graph
elements at its leaves. A graph element can be one of the following types:
* An [`Operation`](../../api_docs/python/framework.md#Operation).
The corresponding fetched value will be `None`.
* A [`Tensor`](../../api_docs/python/framework.md#Tensor).
The corresponding fetched value will be a numpy ndarray containing the
value of that tensor.
* A [`SparseTensor`](../../api_docs/python/sparse_ops.md#SparseTensor).
The corresponding fetched value will be a
[`SparseTensorValue`](../../api_docs/python/sparse_ops.md#SparseTensorValue)
containing the value of that sparse tensor.
* A `get_tensor_handle` op. The corresponding fetched value will be a
numpy ndarray containing the handle of that tensor.
* A `string` which is the name of a tensor or operation in the graph.
The value returned by `run()` has the same shape as the `fetches` argument,
where the leaves are replaced by the corresponding values returned by
TensorFlow.
Example:
```python
a = tf.constant([10, 20])
b = tf.constant([1.0, 2.0])
# 'fetches' can be a singleton
v = session.run(a)
# v is the numpy array [10, 20]
# 'fetches' can be a list.
v = session.run([a, b])
# v a Python list with 2 numpy arrays: the numpy array [10, 20] and the
# 1-D array [1.0, 2.0]
# 'fetches' can be arbitrary lists, tuples, namedtuple, dicts:
MyData = collections.namedtuple('MyData', ['a', 'b'])
v = session.run({'k1': MyData(a, b), 'k2': [b, a]})
# v is a dict with
# v['k1'] is a MyData namedtuple with 'a' the numpy array [10, 20] and
# 'b' the numpy array [1.0, 2.0]
# v['k2'] is a list with the numpy array [1.0, 2.0] and the numpy array
# [10, 20].
```
The optional `feed_dict` argument allows the caller to override
the value of tensors in the graph. Each key in `feed_dict` can be
one of the following types:
* If the key is a [`Tensor`](../../api_docs/python/framework.md#Tensor), the
value may be a Python scalar, string, list, or numpy ndarray
that can be converted to the same `dtype` as that
tensor. Additionally, if the key is a
[placeholder](../../api_docs/python/io_ops.md#placeholder), the shape of
the value will be checked for compatibility with the placeholder.
* If the key is a
[`SparseTensor`](../../api_docs/python/sparse_ops.md#SparseTensor),
the value should be a
[`SparseTensorValue`](../../api_docs/python/sparse_ops.md#SparseTensorValue).
* If the key is a nested tuple of `Tensor`s or `SparseTensor`s, the value
should be a nested tuple with the same structure that maps to their
corresponding values as above.
Each value in `feed_dict` must be convertible to a numpy array of the dtype
of the corresponding key.
The optional `options` argument expects a [`RunOptions`] proto. The options
allow controlling the behavior of this particular step (e.g. turning tracing
on).
The optional `run_metadata` argument expects a [`RunMetadata`] proto. When
appropriate, the non-Tensor output of this step will be collected there. For
example, when users turn on tracing in `options`, the profiled info will be
collected into this argument and passed back.
Args:
fetches: A single graph element, a list of graph elements,
or a dictionary whose values are graph elements or lists of graph
elements (described above).
feed_dict: A dictionary that maps graph elements to values
(described above).
options: A [`RunOptions`] protocol buffer
run_metadata: A [`RunMetadata`] protocol buffer
Returns:
Either a single value if `fetches` is a single graph element, or
a list of values if `fetches` is a list, or a dictionary with the
same keys as `fetches` if that is a dictionary (described above).
Raises:
RuntimeError: If this `Session` is in an invalid state (e.g. has been
closed).
TypeError: If `fetches` or `feed_dict` keys are of an inappropriate type.
ValueError: If `fetches` or `feed_dict` keys are invalid or refer to a
`Tensor` that doesn't exist.
"""下例中可以看到，当以列表作为参数，运算中有赋值时，大多数时候返回的是旧值，偶尔返回新值。
import tensorflow as tf
state = tf.Variable(0.0,dtype=tf.float32)
new_val = tf.add(state, 1.0)
update = tf.assign(state, new_val)
temp_val = tf.add(update, 1.0)
up2=tf.assign(update, temp_val)
init = tf.initialize_all_variables()
with tf.Session() as sess:
sess.run(init)
for _ in range(10):
x=sess.run(state)
print 'x=',x
u2,u,s,n,t = sess.run([up2,update,state,new_val,temp_val])
print u2,u,s,n,t
"""
x= 0.0
2.0 1.0 0.0 1.0 2.0
x= 2.0
4.0 3.0 2.0 3.0 4.0
x= 4.0
6.0 5.0 4.0 5.0 6.0
x= 6.0
8.0 7.0 6.0 7.0 8.0
x= 8.0
10.0 9.0 8.0 9.0 10.0
x= 10.0
12.0 12.0 12.0 11.0 12.0
x= 12.0
14.0 13.0 12.0 13.0 14.0
x= 14.0
16.0 15.0 14.0 15.0 16.0
x= 16.0
18.0 17.0 16.0 17.0 18.0
x= 18.0
20.0 19.0 18.0 19.0 20.0
"""分开则不会。
表明列表中的运算是独立并行的。
import tensorflow as tf
state = tf.Variable(0.0,dtype=tf.float32)
new_val = tf.add(state, 1.0)
update = tf.assign(state, new_val)
temp_val = tf.add(update, 1.0)
up2=tf.assign(update, temp_val)
init = tf.initialize_all_variables()
with tf.Session() as sess:
sess.run(init)
for _ in range(20):
x=sess.run(state)
print 'x=',x
u = sess.run(update)
s = sess.run(state)
print 'u,s=',u,s
"""
x= 0.0
u,s= 1.0 1.0
x= 1.0
u,s= 2.0 2.0
x= 2.0
u,s= 3.0 3.0
x= 3.0
u,s= 4.0 4.0
x= 4.0
u,s= 5.0 5.0
x= 5.0
u,s= 6.0 6.0
x= 6.0
u,s= 7.0 7.0
x= 7.0
u,s= 8.0 8.0
x= 8.0
u,s= 9.0 9.0
x= 9.0
u,s= 10.0 10.0
x= 10.0
u,s= 11.0 11.0
x= 11.0
u,s= 12.0 12.0
x= 12.0
u,s= 13.0 13.0
x= 13.0
u,s= 14.0 14.0
x= 14.0
u,s= 15.0 15.0
x= 15.0
u,s= 16.0 16.0
x= 16.0
u,s= 17.0 17.0
x= 17.0
u,s= 18.0 18.0
x= 18.0
u,s= 19.0 19.0
x= 19.0
u,s= 20.0 20.0
"""