we will zero in on MetaGraph development in TensorFlow. This will assist us with understanding product module in TensorFlow. The MetaGraph contains the fundamental data, which is expected to prepare, perform assessment, or run induction on a formerly prepared diagram.
Following is the code piece for something similar −
ef export_meta_graph(filename = None, collection_list = None, as_text = False>
: """this code writes `MetaGraphDef` to save_path/filename. Arguments: filename: Optional meta_graph filename including the path. collection_list: 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 ...
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'>
Could you at any point utilize Yolo with TensorFlow?
Now that we've designed TensorFlow, we'll utilize the YOLO engineering to prepare the article identification model.
What is XLA in TensorFlow?
XLA (Accelerated Linear Algebra>
is an area explicit compiler for straight variable based math that can speed up TensorFlow models with possibly no source code changes.
Does Tensorflow work with AMD?
AMD has delivered ROCm, a Deep Learning driver to run Tensorflow and PyTorch on AMD GPUs.
ResNet 50 Example here