Saving weights only in TensorFlow - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to save only the weights of the model.

TensorFlow

model.[1]('weights.h5')

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

Asave_all

Bsave

Csave_model

Dsave_weights

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'save' saves the whole model, not just weights.

Using 'save_model' is incorrect for saving weights only.

Use save_weights to save only the weights of a TensorFlow model.

2fill in blank

medium

Complete the code to load weights from a file into the model.

TensorFlow

model.[1]('weights.h5')

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

Aload_model

Bload

Cload_weights

Drestore_weights

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'load_model' tries to load a full model, not just weights.

Using 'restore_weights' is not a valid TensorFlow method.

Use load_weights to load saved weights into a TensorFlow model.

3fill in blank

hard

Fix the error in saving weights by completing the method name correctly.

TensorFlow

model.[1]('my_weights.h5')

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

Asave_weights

Bsaveweight

Csave_weights_only

Dsaveweightfile

Attempts:

3 left

💡 Hint

Common Mistakes

Misspelling the method name causes AttributeError.

Adding extra words like 'only' is incorrect.

The correct method to save weights is save_weights. Other options are invalid.

4fill in blank

hard

Fill both blanks to save weights in TensorFlow and specify the file format.

TensorFlow

model.[1]('weights.[2]')

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

Asave_weights

Bsave

Ch5

Dckpt

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'save' saves the whole model, not just weights.

Using '.ckpt' extension requires different saving/loading methods.

Use save_weights to save weights and h5 as the file extension for HDF5 format.

5fill in blank

hard

Fill all three blanks to load weights from a checkpoint file and verify the model's accuracy.

TensorFlow

model.[1]('checkpoint.[2]')
loss, accuracy = model.evaluate([3], verbose=0)

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

Aload_weights

Bckpt

Ctest_data

Dload_model

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'load_model' instead of 'load_weights' for weights only.

Using wrong file extension for checkpoint files.

Not passing test data to evaluate method.

Use load_weights to load weights from a checkpoint file with extension .ckpt. Then evaluate the model on test_data to get accuracy.

Practice

1. What does saving weights only in TensorFlow mean?

easy

A. Saving the entire model including architecture and optimizer

B. Saving just the learned parameters without the model structure

C. Saving only the model architecture without weights

D. Saving the training dataset along with the model

Solution

1. Step 1: Understand what weights represent

   Weights are the learned parameters of a model after training.

2. Step 2: Differentiate saving weights only vs full model

   Saving weights only means storing these parameters without saving the model's structure or optimizer state.

3. Final Answer:

   Saving just the learned parameters without the model structure -> Option B

4. Quick Check:

   Saving weights only = learned parameters only [OK]

Hint: Weights only means parameters, not architecture or optimizer [OK]

Common Mistakes:

• Confusing weights with full model saving
• Thinking architecture is saved with weights only
• Assuming dataset is saved with weights

2. Which of the following is the correct syntax to save only weights of a TensorFlow model named model to a file weights.h5?

easy

A. model.save_weights(weights='weights.h5')

B. model.save('weights.h5')

C. model.save_weights('weights.h5')

D. tf.save_weights(model, 'weights.h5')

Solution

1. Step 1: Recall TensorFlow method to save weights

   The method to save weights is model.save_weights(filepath).

2. Step 2: Check syntax correctness

   model.save_weights('weights.h5') uses correct method and argument format. model.save('weights.h5') saves full model, C is invalid TensorFlow syntax, A uses wrong argument name.

3. Final Answer:

   model.save_weights('weights.h5') -> Option C

4. Quick Check:

   Use model.save_weights(filepath) to save weights [OK]

Hint: Use model.save_weights('filename') to save weights only [OK]

Common Mistakes:

• Using model.save() instead of save_weights()
• Incorrect argument names or order
• Trying to use non-existent TensorFlow functions

3. Consider this code snippet:

import tensorflow as tf
model = tf.keras.Sequential([
  tf.keras.layers.Dense(5, input_shape=(3,))
])
model.save_weights('w.h5')

new_model = tf.keras.Sequential([
  tf.keras.layers.Dense(5, input_shape=(3,))
])
new_model.load_weights('w.h5')
print(new_model.get_weights()[0].shape)

What will be the printed output?

medium

A. (3, 5)

B. (5, 3)

C. (5,)

D. Error: weights shape mismatch

Solution

1. Step 1: Understand model architecture

   The model has one Dense layer with 5 units and input shape (3,), so weights shape is (input_dim, units) = (3, 5).

2. Step 2: Loading weights into identical architecture

   Since new_model has the same architecture, loading weights works and weights shape remains (3, 5).

3. Final Answer:

   (3, 5) -> Option A

4. Quick Check:

   Weights shape = (input_dim, units) = (3, 5) [OK]

Hint: Weights shape = (input features, output units) in Dense layers [OK]

Common Mistakes:

• Confusing input and output dimensions order
• Assuming error when architectures match
• Expecting bias shape instead of weights

4. You saved weights using model.save_weights('w.h5'). When loading weights with new_model.load_weights('w.h5'), you get an error about mismatched shapes. What is the most likely cause?

medium

A. TensorFlow does not support loading weights from .h5 files

B. The weights file is corrupted

C. You forgot to compile the new model before loading weights

D. The new model architecture is different from the original model

Solution

1. Step 1: Understand weight loading requirements

   Weights can only be loaded if the new model has the same architecture as the original model.

2. Step 2: Analyze error cause

   Mismatched shapes error usually means layer sizes differ between saved and new model.

3. Final Answer:

   The new model architecture is different from the original model -> Option D

4. Quick Check:

   Model architectures must match to load weights [OK]

Hint: Check model architectures match before loading weights [OK]

Common Mistakes:

• Assuming file corruption without checking architecture
• Thinking compile is needed before loading weights
• Believing .h5 files are unsupported for weights

5. You want to share your trained model's learned parameters with a friend, but not the full model code. Which approach is best to save and share only the weights, and what must your friend do to use them?

hard

A. Save weights with model.save_weights(); friend recreates model architecture then loads weights

B. Save full model with model.save(); friend loads full model directly

C. Save weights as a numpy file; friend loads weights without model code

D. Save model architecture as JSON; friend loads architecture and trains from scratch

Solution

1. Step 1: Identify saving weights only method

   Use model.save_weights() to save only learned parameters without model structure.

2. Step 2: Explain how friend uses weights

   Friend must recreate the exact model architecture in code, then load weights with load_weights().

3. Final Answer:

   Save weights with model.save_weights(); friend recreates model architecture then loads weights -> Option A

4. Quick Check:

   Save weights only + recreate model to load weights [OK]

Hint: Save weights only, friend rebuilds model then loads weights [OK]

Common Mistakes:

• Thinking weights can be loaded without model code
• Confusing full model save with weights only
• Assuming JSON architecture saves weights