- #Tensorflow board view values how to#
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With tf.gfile.GFile(model_filepath, 'rb') as f: Self.load_graph(model_filepath = self.model_filepath) We wrote a object to load model from pb files. The model files generated in the model directory are the follows: 1 The pb files generated from the two methods both pass the accuracy tests that I am going to show below. I believe the first method is just a higher-level wrapper for the second method. The second method is to serialization yourself. The pb file will be saved to output_graph path you provided. Leave the rest of the arguments the same as mine should be fine. restore_op_name and filename_tensor_name are being deprecated, using the values provided should be universal to all models. It can be a string if you only have one output, or a list of strings if you have multiple outputs. You will also need to specify the name of your output node. If input_graph is binary pb file, input_binaryshould be True. If input_graph is human-readable pbtxt file, input_binaryshould be False. The argument description of freeze_graph could be found here.
The first method is to use freeze_graph function. We then need to freeze and combine graph and parameters to pb file. But this pb file will not contain the parameters you trained in your model. By convention, if it is human-readable, the file extension we use will be. as_text is a boolean value indicating whether the saved graph is human-readable or not. There are two arguments which might be confusing to the new users, name and as_text. Saving graph is to use tf.train.write_graph. In my code, I wrapped saving checkpoint using tf.train.Saver in self.save method. Saving checkpoint is easy, you just have to use tf.train.Saver and everything should be straightforward. You are required to save checkpoint of your model first, followed by saving the graph. With tf.gfile.GFile(pb_filepath, 'wb') as f:į.write(output_graph_def.SerializeToString())
# output_graph_def = graph_util.remove_training_nodes(output_graph_def, protected_nodes=None) # For some models, we would like to remove training nodes Output_graph_def = graph_nvert_variables_to_constants(ss, input_graph_def, output_node_names) Tf.train.write_graph(graph_or_graph_def=_def, logdir=directory, name=pbtxt_filename, as_text= True)įreeze_eeze_graph(input_graph=pbtxt_filepath, input_saver= '', input_binary= False, input_checkpoint=ckpt_filepath, output_node_names= 'cnn/output', restore_op_name= 'save/restore_all', filename_tensor_name= 'save/Const:0', output_graph=pb_filepath, clear_devices= True, initializer_nodes= '') # This will only save the graph but the variables will not be saved. Pb_filepath = os.path.join(directory, filename + '.pb') Pbtxt_filepath = os.path.join(directory, pbtxt_filename) # Save check point for graph frozen laterĬkpt_filepath = self.save(directory=directory, filename=filename)
#Tensorflow board view values download#
Otherwise, people download your pb file and they will not be able to deploy it.įrom import freeze_graphįilepath = os.path.join(directory, filename + '.ckpt')ĭef save_as_pb( self, directory, filename): While the parameters are optional for pb file, you need it for our task since we need to use parameters to do inference. The major component of pb file is graph structure and also the parameters of your model. The test accuracy after training is around 0.793900. $ python main.py -train - test -epoch 30 -lr_decay 0.9 -dropout 0.5 Train the model using the following command: 1 It was modified from my previous simple CNN model to classify CIFAR10 dataset.
#Tensorflow board view values code#
This sample code was available on my GitHub. For doing the equivalent tasks in TensorFlow 2.x, please read the other blog post “Save, Load and Inference From TensorFlow 2.x Frozen Graph”.
#Tensorflow board view values how to#
In this blog post, I am going to introduce how to save, load, and run inference for frozen graph in TensorFlow 1.x. While pb format models seem to be important, there is lack of systematic tutorials on how to save, load and do inference on pb format models in TensorFlow. It is widely used in model deployment, such as fast inference tool TensorRT. pb stands for Protocol Buffers, it is a language-neutral, platform-neutral extensible mechanism for serializing structured data.
There is another model format called pb which is frequently seen in model zoos but hardly mentioned by TensorFlow official channels. You can find a lot of instructions on TensorFlow official tutorials. Loading those saved models are also easy. Now you can either use Keras to save h5 format model or use tf.train.Saver to save the check point files. TensorFlow model saving has become easier than it was in the early days.