How to get a tensorflow op by name?

In TensorFlow, you can retrieve operations by name using the Graph functionality. The graph consists of operations and tensors, each of which can have a unique name. To access an operation by name, you can use the tf.Graph.get_operation_by_name(name) method.

Here is a specific example:

Suppose you name an operation while building your model, such as naming a convolutional layer 'conv1':

python
import tensorflow as tf

# Build the graph
graph = tf.Graph()
with graph.as_default():
    input_tensor = tf.placeholder(tf.float32, shape=[None, 28, 28, 1], name='input')
    conv = tf.layers.conv2d(inputs=input_tensor, filters=32, kernel_size=(3, 3), name='conv1')

Then, if you need to retrieve this convolutional layer operation later, you can do the following:

python
with graph.as_default():
    conv1_op = graph.get_operation_by_name('conv1')

conv1_op represents the operation named 'conv1' for the convolutional layer. This approach allows you to conveniently retrieve any operation in the graph for further processing, such as accessing its inputs and outputs or modifying its attributes.

Using this method to retrieve operations by name is highly valuable, especially when working with complex models or during model conversion and optimization. It enables developers to directly reference specific components of the model without having to rebuild the entire network structure.