Updated March 14, 2023
Definition of TensorFlow save model
TensorFlow save model is used for saving the models that have been developed using machine learning algorithms and have been used as a tool. It provides developers an added advantage to reutilize and optimize any model with enhancement. TensorFlow save model basically makes use of internal functions and models for further modification and manipulation. Any important implementation or development work can be saved without any further hamper like in case of shut down, power off basically on any unwanted scenario as it maintains the memory management and loads off the load appropriately.
TensorFlow save model
- TensorFlow save models have their own characteristics and advantages where it will be having some model training that will have to save and resume accordingly.
- It helps in manipulating the models and their training as per requirement. It also makes user to drive the entire flow and enhance the flow as per need as changes can be done on the previous model easily.
- It gives others a privilege to make changes and resume wherever the work was last left off without compromising with the entire flow and last changes.
- It helps in publishing any new research models and techniques that are needed and will involve most of the machine learning techniques that will help in creating a code with a model and there will be some trained weights on top of it.
- Sharing this model or data helps others to understand some of the new data and its entitlement.
Example
There are certain examples with dataset that needs to be fixed for TensorFlow model to have dataset like:
(train_img, train_labls), (test_img, test_labls) = tf.keras.datasets.mnist.load_data()
train_labls = train_labls[:2000]
tst_labls = tst_labls[:2000]
train_img = train_img[:2000].reshape(-1, 20 * 20) / 255.0
test_img = test_img[:2000].reshape(-1, 20 * 20) / 255.0
Explanation:
The above dataset depicts the sample dataset that will be using mnist to load the sample dataset post which the labels and images provided will be used for manipulation and training.
# Options to use while saving TensorFlow Model:
- There are other ways to make the TensorFlow model save data depending on the API that is used. It too helps in making and using tf keras which in turn makes a high-level API to build and train using TensorFlow Save and Restore with saving in eager.
- There are certain modeling strategies that need to be taken care of while implementing with keras into incorporation.
Example:
chckpnt_callback = tf.keras.callbacks.ModelCheckpoint(
file_path=check_point_path,
verbose_sm=1,
save_weights_only=True,
save_freq_chck=6*batch_size)
Explanation:
This function will let users give the privilege to make use of unique names provided at the checkpoints to be unique with adjusting the checkpoint frequency.
# File formats used for saving model
- There is a way to save the entire model with a call that will have to save some data in a format using architectures, weight, and configuration for training with a single file or folder.
- Basically, the model can be saved with two different file formats namely HDF5 and Saved Model. SavedModel is the default file format for saving data inTF2.x whereas HDF5 is an alternate option to save the model for manipulation.
- Once the SavedModel is done and completed it must serialize the model with some formats and manipulations.
- Models saved with this format can be used for restoration using tf.keras.model.load_model that are made to be compatible with TensorFlow Serving.
- The SavedModel format is made compatible because it must be saved in HDF5 format.
Example:
model.save_weights('./checkpoints_formats/def_checkpoints')
new_model = create_model()
restore_model.load_weights('./checkpoints_formats/def_checkpoint')
loss, acc = model.evaluate(test_img, test_labls, verbose_sm=1)
print("Restored_model, accuracy: {:5.2f}%".format(100 * acc))
Explanation:
This code snippet is basically representing the model to be saved consisting of weights and callback function to restore and evaluate the defined model using which the loss or other variables with accuracy and formatting can be tested as shown.
- Saving a fully functional model is efficient and useful as it will help in loading the entire model with two file formats starting from training till running on the mobile devices with the help of TensorFlow lite.
- There are certain custom objects which need special attention while saving and loading.
- Keras as part of save and load model have a unique ability to save everything based on the inspection of architectures which includes, model’s architecture, weighted values, model’s training configuration to compile method and then optimizer with its state to compile and then recompile to model and load by optimizing the flow with data.
Using TensorFlow save model
- TensorFlow save model follows certain formats as mentioned earlier for complete TensorFlow program that involves a lot of parameters that are trained and is basically used for computation.
- It makes use of tf. Variables that help in manipulation with all these flows and model composition.
- It doesn’t require any model to build code and run it accordingly.
- It makes use of TFLite, TensorFlow Serving, or TensorFlow hub which is mostly used for sharing some useful insights and deployment activities.
- A model can be saved and load with a model in SavedModel format with some of the API’s that are almost mandate as part of any function or managing.
- Saved models are basically derived and used from Python but production environments typically make use of dedicated service for any inference that is used without python code running.
- It gives an ease in terms of implementation and flexibility to TensorFlow-serving example.
- The SavedModel acts as a directory that comprises of serialized signature and some state that needs to run this including variable values and vocabularies. This is basically saving the model format within a disk.
- This saved model.pb stores the actual TensorFlow program or model for manipulation and a set of named signatures that accepts tensor flow and produces significant tensor outputs with the defined set of tensor inputs being provided.
TensorFlow save model format
Following APIs are used using which the model can be load and saved in some format:
- Low-level API: tf.saved_model API
- To save any model API used: tf.saved_model.save (model, directory_path)
- To Load any model API used: tf.saved_model.load(model, directory_path)
- High Level model designing API: tf.keras.model
- To save and load weights during training involves checkpoints and its reference as mentioned the above save and load overview.
Example:
This example creates a new model then it is used for fitting the values with checkpoint frequency and then the model is saved in TensorFlow save model format as shown.
model_new = create_model()
model.fit(train_img, train_labls, epochs=6)
!mkdir -p saved_model
model.save('saved_model_dir/created_new_model')
Commands
The saved model makes use of CLI that makes use of the following two commands frequently:
– Show: this command is used for showing computations that are part of SavedModel for manipulation and compilation.
– Run: this command is used for computation running for a saved model.
# Show command:
– This command is used for showing the insights and details of the SaveModel which contains more than one variant of the signatures containing variations with inputs and outputs.
# Run command:
This command is used for running the graph, make computations and then pass inputs by displaying outputs for it.
– It also helps in providing various flags and parameters properly like inputs to it that are as follows:
– – – inputs : This parameter allows you to pass ndarray in files.
– –input_exprs: this allows you to pass expressions using Python.
– –input_exmpls: allows to pass tf.train.examples from code level.
Conclusion
TensorFlow saved model have a lot of efficiencies when it comes to training new models as this gets saved and helps in saving a lot of time and other complexities by providing a reusability feature. It has a lot of advantages when it comes to changing and making the same function within the model incorporated.
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