SavedModel format in TensorFlow - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to save a TensorFlow model in SavedModel format.

TensorFlow

model.save('[1]')

Drag options to blanks, or click blank then click option'

Asaved_model/my_model

Bmodel.h5

Ccheckpoint.ckpt

Dmodel.json

Attempts:

3 left

💡 Hint

Common Mistakes

Using a filename with .h5 extension which saves in HDF5 format, not SavedModel.

Using checkpoint or JSON filenames which are not for SavedModel format.

To save a TensorFlow model in SavedModel format, you provide a directory path. The path 'saved_model/my_model' is a folder where the model will be saved.

2fill in blank

medium

Complete the code to load a TensorFlow model saved in SavedModel format.

TensorFlow

loaded_model = tf.keras.models.[1]('saved_model/my_model')

Drag options to blanks, or click blank then click option'

Asave_weights

Bload_weights

Csave_model

Dload_model

Attempts:

3 left

💡 Hint

Common Mistakes

Using load_weights which only loads weights, not the full model.

Using save_model or save_weights which are for saving, not loading.

To load a model saved in SavedModel format, use tf.keras.models.load_model() with the folder path.

3fill in blank

hard

Fix the error in the code to save the model in SavedModel format.

TensorFlow

model.save('my_model[1]')

Drag options to blanks, or click blank then click option'

A.h5

B/

C.json

D.ckpt

Attempts:

3 left

💡 Hint

Common Mistakes

Adding file extensions which save in other formats.

Not specifying a folder path causes errors or saves in wrong format.

To save in SavedModel format, the path should be a directory. Adding a slash '/' at the end ensures it's treated as a folder.

4fill in blank

hard

Fill both blanks to save a model and then load it back correctly.

TensorFlow

model.save('[1]')
loaded_model = tf.keras.models.[2]('[1]')

Drag options to blanks, or click blank then click option'

Asaved_model/my_model

Bload_model

Cload_weights

Dmodel.h5

Attempts:

3 left

💡 Hint

Common Mistakes

Using different paths for saving and loading.

Using load_weights instead of load_model for loading.

Use the same folder path to save and load the model. Use load_model to load the full model from SavedModel format.

5fill in blank

hard

Fill all three blanks to save a model, load it, and evaluate it on test data.

TensorFlow

model.save('[1]')
loaded_model = tf.keras.models.[2]('[1]')
loss, accuracy = loaded_model.evaluate([3])

Drag options to blanks, or click blank then click option'

Asaved_model/my_model

Bload_model

Ctest_data

Dload_weights

Attempts:

3 left

💡 Hint

Common Mistakes

Using load_weights instead of load_model before evaluation.

Passing training data instead of test data to evaluate.

Save and load the model using the same folder path and load_model. Then evaluate the loaded model on the test dataset.

Practice

1. What does the TensorFlow SavedModel format primarily store?

easy

A. Only the model's training data

B. Only the model's weights

C. Only the model's optimizer state

D. The complete model architecture, weights, and training configuration

Solution

1. Step 1: Understand what SavedModel stores

   The SavedModel format saves the entire model, including architecture, weights, and training configuration.

2. Step 2: Compare options with SavedModel features

   Options A, B, and D only mention parts of the model or incorrect elements, but SavedModel stores all parts together as in C.

3. Final Answer:

   The complete model architecture, weights, and training configuration -> Option D

4. Quick Check:

   SavedModel = full model save [OK]

Hint: SavedModel saves everything needed to reuse the model [OK]

Common Mistakes:

• Thinking SavedModel saves only weights
• Confusing SavedModel with checkpoint files
• Assuming training data is saved inside SavedModel

2. Which of the following is the correct way to save a TensorFlow Keras model in SavedModel format?

easy

A. model.save('path/to/model', save_format='tf')

B. model.save('path/to/model', save_format='h5')

C. tf.save_model(model, 'path/to/model')

D. model.save_weights('path/to/model')

Solution

1. Step 1: Recall the save_format options

   TensorFlow Keras models can be saved in 'h5' or 'tf' formats. 'tf' means SavedModel format.

2. Step 2: Check each option's correctness

   model.save('path/to/model', save_format='h5') saves in HDF5 format, not SavedModel. model.save('path/to/model', save_format='tf') correctly uses save_format='tf'. tf.save_model(model, 'path/to/model') is invalid syntax. model.save_weights('path/to/model') saves only weights, not full model.

3. Final Answer:

   model.save('path/to/model', save_format='tf') -> Option A

4. Quick Check:

   save_format='tf' means SavedModel [OK]

Hint: Use save_format='tf' to save in SavedModel format [OK]

Common Mistakes:

• Using save_format='h5' instead of 'tf'
• Trying to use tf.save_model which doesn't exist
• Saving only weights instead of full model

3. What will be the output of the following code snippet?

import tensorflow as tf
model = tf.keras.Sequential([tf.keras.layers.Dense(1)])
model.save('my_model')
loaded_model = tf.keras.models.load_model('my_model')
print(type(loaded_model))

medium

A. <class 'tensorflow.python.keras.engine.sequential.Sequential'>

B. <class 'tensorflow.python.keras.engine.functional.Functional'>

C. AttributeError

D. None

Solution

1. Step 1: Understand model saving and loading

   The model is a Sequential model saved in SavedModel format and then loaded back.

2. Step 2: Check the type of loaded model

   Loading a Sequential model returns an instance of tf.keras.Sequential class, so the type is Sequential.

3. Final Answer:

   <class 'tensorflow.python.keras.engine.sequential.Sequential'> -> Option A

4. Quick Check:

   Loaded model type matches original Sequential [OK]

Hint: Loaded model keeps original class type (Sequential here) [OK]

Common Mistakes:

• Expecting Functional model type after loading Sequential
• Thinking load_model returns None
• Confusing error with AttributeError

4. You saved a model using model.save('model_path') but when loading with tf.keras.models.load_model('wrong_path'), you get an error. What is the likely cause?

medium

A. The model was not compiled before saving

B. The model was saved in HDF5 format, so load_model cannot find SavedModel files

C. The load path is incorrect or does not exist

D. The model weights were not saved

Solution

1. Step 1: Analyze the error cause

   Loading a model from a wrong or non-existing path causes a file not found error.

2. Step 2: Check other options

   The model was saved in HDF5 format, so load_model cannot find SavedModel files is unrelated to path errors. The model was not compiled before saving and D do not cause loading errors but affect training or evaluation.

3. Final Answer:

   The load path is incorrect or does not exist -> Option C

4. Quick Check:

   Wrong path causes load_model error [OK]

Hint: Check file path carefully when loading models [OK]

Common Mistakes:

• Assuming model must be compiled before saving
• Confusing missing weights with loading errors
• Ignoring file path correctness

5. You want to deploy a TensorFlow model to a server. Which of these steps correctly uses the SavedModel format to save and load the model for deployment?

hard

A. Save with tf.saved_model.save(model, 'deploy_model') and load with tf.saved_model.load('deploy_model')

B. Save with model.save('deploy_model', save_format='tf') and load with tf.keras.models.load_model('deploy_model')

C. Save with model.save('deploy_model.h5') and load with tf.keras.models.load_model('deploy_model.h5')

D. Save with model.save_weights('deploy_model') and load with model.load_weights('deploy_model')

Solution

1. Step 1: Identify correct save and load methods for deployment

   Using model.save with save_format='tf' saves in SavedModel format, suitable for deployment. Loading with tf.keras.models.load_model restores the full model.

2. Step 2: Evaluate other options

   Save with model.save_weights('deploy_model') and load with model.load_weights('deploy_model') saves only weights, not full model. Save with model.save('deploy_model.h5') and load with tf.keras.models.load_model('deploy_model.h5') saves in HDF5, which is less preferred for deployment. Save with tf.saved_model.save(model, 'deploy_model') and load with tf.saved_model.load('deploy_model') uses low-level API which returns a different object, not a Keras model.

3. Final Answer:

   Save with model.save('deploy_model', save_format='tf') and load with tf.keras.models.load_model('deploy_model') -> Option B

4. Quick Check:

   Use model.save with save_format='tf' for deployment [OK]

Hint: Use model.save with save_format='tf' and load_model for deployment [OK]

Common Mistakes:

• Saving only weights for deployment
• Using tf.saved_model.load which returns non-Keras object
• Saving in HDF5 format instead of SavedModel