TenorFlow Tutorial
TensorFlow Tutorial
TensorFlow Useful Resources
Selected Reading
TensorFlow Tutorial
- Recommendations for Neural Network Training
- Image Recognition using TensorFlow
- TensorFlow - Forming Graphs
- Gradient Descent Optimization
- TensorFlow - XOR Implementation
- TensorFlow - Optimizers
- Hidden Layers of Perceptron
- Multi-Layer Perceptron Learning
- TensorFlow - Exporting
- TensorFlow - Distributed Computing
- TensorFlow - Keras
- CNN and RNN Difference
- TFLearn and its installation
- TensorFlow - Linear Regression
- Single Layer Perceptron
- TensorFlow - Word Embedding
- TensorBoard Visualization
- Recurrent Neural Networks
- Convolutional Neural Networks
- TensorFlow - Basics
TensorFlow Useful Resources
Selected Reading
- Who is Who
- Computer Glossary
- HR Interview Questions
- Effective Resume Writing
- Questions and Answers
- UPSC IAS Exams Notes
TensorFlow - Exporting
TensorFlow - Exporting
Here, we will focus on MetaGraph formation in TensorFlow. This will help us understand export module in TensorFlow. The MetaGraph contains the basic information, which is required to train, perform evaluation, or run inference on a previously trained graph.
Following is the code snippet for the same −
def export_meta_graph(filename = None, collection_pst = None, as_text = False): """this code writes `MetaGraphDef` to save_path/filename. Arguments: filename: Optional meta_graph filename including the path. collection_pst: List of string keys to collect. as_text: If `True`, writes the meta_graph as an ASCII proto. Returns: A `MetaGraphDef` proto. """
One of the typical usage model for the same is mentioned below −
# Build the model ... with tf.Session() as sess: # Use the model ... # Export the model to /tmp/my-model.meta. meta_graph_def = tf.train.export_meta_graph(filename = /tmp/my-model.meta )Advertisements