SavedModel format in TensorFlow - ML Experiment: Train & Evaluate

Experiment - SavedModel format

Problem:You have trained a TensorFlow model and saved it using the SavedModel format. However, when you reload the model, it does not produce the same predictions as before saving.

Current Metrics:Original model prediction on test input: [0.85, 0.15]; Reloaded model prediction on same input: [0.60, 0.40]

Issue:The reloaded model predictions differ significantly from the original model, indicating a problem with saving or loading the model.

Your Task

Ensure that the TensorFlow model saved in SavedModel format can be reloaded and produce the same predictions as the original model.

Use TensorFlow 2.x APIs only

Do not retrain the model after loading

Use the SavedModel format for saving and loading

Hint 1

Hint 2

Hint 3

Solution

TensorFlow

import tensorflow as tf
import numpy as np

# Create a simple model
model = tf.keras.Sequential([
    tf.keras.layers.Dense(10, activation='relu', input_shape=(5,)),
    tf.keras.layers.Dense(2, activation='softmax')
])

# Compile and train the model on dummy data
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

X_train = np.random.random((100, 5))
y_train = np.random.randint(0, 2, 100)
model.fit(X_train, y_train, epochs=3, batch_size=10, verbose=0)

# Test input
X_test = np.random.random((1, 5))
original_pred = model.predict(X_test)

# Save the model in SavedModel format
model.save('saved_model_dir')

# Load the model back
loaded_model = tf.keras.models.load_model('saved_model_dir')

# Predict with loaded model
loaded_pred = loaded_model.predict(X_test)

print(f'Original prediction: {original_pred}')
print(f'Loaded model prediction: {loaded_pred}')

Used model.save() with a directory path to save in SavedModel format

Used tf.keras.models.load_model() to reload the model correctly

Verified predictions before and after saving to ensure they match

Results Interpretation

Before saving, the model predicted probabilities like [0.85, 0.15]. After saving and loading, the predictions changed to [0.60, 0.40], which is incorrect.

After applying the correct saving and loading methods, both predictions match closely, for example, [0.72, 0.28] before and after.

Using TensorFlow's SavedModel format with proper save and load functions preserves the model's architecture and weights, ensuring consistent predictions before and after saving.

Bonus Experiment

Try saving and loading a model that includes a custom layer or custom loss function using the SavedModel format.

💡 Hint

You will need to provide a custom_objects dictionary when loading the model to tell TensorFlow how to handle the custom parts.

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