当我使用 tensorflow ops 编写函数时会发生什么

Question

I write a function using tensorflow ops. I know the fact when I run the function, it will add many ops to the graph. But I am confused with how to get access of these ops.

for example:

def assign_weights():
    with tf.name_scope('zheng'):
        v = tf.Variable(0, 'v', dtype=tf.float32)
        b = tf.placeholder(tf.float32, shape=())
        z = tf.assign(v, b)
    return z, b

I can use feed_dict to pass a value to b , only if I set b as a return value. Otherwise, there is no way to access b . If we want to access many ops in the function scope, we should set many return values. This is very ugly.

I want to know what happens under the hood when I run functions using tensorflow and how to get access of the ops in the function scope.

Thank you!

Answer 1

Obviously, it's true that to access an op (or tensor) we need some reference to it. IMHO, one standard workaround is to build your graph in a class and make certain tensors attributes of the class and access them through the object.

Alternatively, if you're more inclined to the functional approach, a better way than returning all relevant ops and tensors separately would be to return a dict (or namedtuple).

Additionally, there are also specialized functions that return ops by name: eg get_operation_by_name .

As an aside to this question, you might also want to try out eager execution , which is imperative.

Answer 2

3 things happen when you use op function:

• create and add a compute node to default graph
• set your input as the node input tensor
• set node output tensor as return value

for example, a = tf.add(b, c, name='add') ,

• add a node with op Add to default graph, with name 'add'
• set b and c as node input tensor
• set node output, with name 'add:0', to a

So you can access nodes via sess.graph , there are many functions to access nodes, say, get_operation_by_name.

Also, you can operate the graph via sess.graph_def , which is serialized graph with protobuf, you can find the protobuf definition in the tensorflow source code, tensorflow/core/framework , some .proto files there.