Saving and loading models in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to save a trained model using joblib.

ML Python

import joblib

# Assume model is already trained
joblib.[1](model, 'model.pkl')

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

Aload

Bsave

Cdump

Dwrite

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'load' instead of 'dump' to save the model.

Using 'save' which is not a joblib function.

The joblib.dump() function is used to save a model to a file.

2fill in blank

medium

Complete the code to load a saved model using joblib.

ML Python

import joblib

model = joblib.[1]('model.pkl')

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

Asave

Bload

Cdump

Dread

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'dump' instead of 'load' to load the model.

Using 'save' which is not a joblib function.

The joblib.load() function reads a saved model from a file.

3fill in blank

hard

Fix the error in saving a Keras model to HDF5 format.

ML Python

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

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

Awrite

Bload

Cdump

Dsave

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'dump' which is not a Keras model method.

Using 'load' instead of 'save'.

Keras models are saved using the save() method.

4fill in blank

hard

Fill both blanks to load a Keras model and evaluate it.

ML Python

from tensorflow.keras.models import [1]

model = [2]('model.h5')
loss, accuracy = model.evaluate(x_test, y_test)

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

Aload_model

Bsave_model

Cload

Dsave

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'save_model' which is for saving, not loading.

Using 'load' which is not imported from keras.models.

The function load_model from Keras loads a saved model from a file.

5fill in blank

hard

Fill all three blanks to save a scikit-learn model, load it, and make predictions.

ML Python

import joblib

joblib.[1](model, 'model.pkl')
loaded_model = joblib.[2]('model.pkl')
predictions = loaded_model.[3](X_test)

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

Adump

Bload

Cpredict

Dsave

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'save' instead of 'dump' to save the model.

Using 'load' incorrectly as a method on the model.

Using 'fit' instead of 'predict' to get predictions.

Use dump to save, load to load, and predict to get model predictions.

Practice

1. What is the main purpose of saving a trained machine learning model?

easy

A. To increase the model's training speed

B. To reuse the model later without retraining

C. To improve the model's accuracy automatically

D. To delete the training data after use

Solution

1. Step 1: Understand model saving

   Saving a model stores its learned parameters so it can be reused later.

2. Step 2: Identify the main benefit

   This avoids retraining, saving time and resources.

3. Final Answer:

   To reuse the model later without retraining -> Option B

4. Quick Check:

   Saving = reuse without retraining [OK]

Hint: Saving means keep model for later use without retraining [OK]

Common Mistakes:

• Thinking saving improves accuracy automatically
• Confusing saving with speeding up training
• Believing saving deletes data

2. Which of the following is the correct way to save a Keras model named model to a file called my_model.h5?

easy

A. model.save('my_model.h5')

B. save_model('my_model.h5', model)

C. model.save_model('my_model.h5')

D. save('model', 'my_model.h5')

Solution

1. Step 1: Recall Keras save syntax

   Keras models are saved using model.save(filename).

2. Step 2: Match correct syntax

   model.save('my_model.h5') matches this syntax exactly.

3. Final Answer:

   model.save('my_model.h5') -> Option A

4. Quick Check:

   model.save(filename) saves model [OK]

Hint: Use model.save('filename') to save Keras models [OK]

Common Mistakes:

• Using incorrect function names like save_model or save
• Swapping argument order
• Calling save_model as a method on model

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

from tensorflow.keras.models import load_model
model = load_model('saved_model.h5')
predictions = model.predict([[5.1, 3.5, 1.4, 0.2]])
print(predictions)

medium

A. The model architecture printed as text

B. An error because load_model requires a directory, not a file

C. A list of class labels as strings

D. A numpy array of prediction probabilities

Solution

1. Step 1: Understand load_model usage

   load_model loads a saved Keras model from a file.

2. Step 2: Predict method output

   model.predict returns a numpy array of prediction probabilities for input data.

3. Final Answer:

   A numpy array of prediction probabilities -> Option D

4. Quick Check:

   load_model + predict = numpy array output [OK]

Hint: load_model loads model; predict returns probabilities array [OK]

Common Mistakes:

• Thinking load_model only loads from directories
• Expecting printed model architecture from predict
• Assuming predict returns class labels directly

4. You try to load a saved model using load_model('model.h5') but get an error: OSError: Unable to open file (file signature not found). What is the most likely cause?

medium

A. The model file is too large to load

B. You forgot to import load_model from keras

C. The file 'model.h5' is corrupted or not a valid model file

D. The model was saved with a different Python version

Solution

1. Step 1: Analyze the error message

   OSError about file signature means the file is not recognized as a valid HDF5 model file.

2. Step 2: Identify common causes

   This usually happens if the file is corrupted or not a saved model file.

3. Final Answer:

   The file 'model.h5' is corrupted or not a valid model file -> Option C

4. Quick Check:

   File signature error = corrupted or invalid file [OK]

Hint: File signature errors mean corrupted or wrong file type [OK]

Common Mistakes:

• Assuming Python version mismatch causes this error
• Forgetting to import but error message differs
• Thinking file size causes signature errors

5. You have a trained model saved as model_v1.h5. You want to load it, continue training on new data, then save the updated model as model_v2.h5. Which code snippet correctly does this?

hard

A. from tensorflow.keras.models import load_model
model = load_model('model_v1.h5')
model.fit(new_data, new_labels, epochs=5)
model.save('model_v2.h5')

B. model = load('model_v1.h5')
model.train(new_data, new_labels)
save_model(model, 'model_v2.h5')

C. load_model('model_v1.h5')
train_model(new_data, new_labels)
save('model_v2.h5')

D. from keras import load
model = load('model_v1.h5')
model.fit(new_data, new_labels)
model.save('model_v2.h5')

Solution

1. Step 1: Load the saved model correctly

   Use load_model('model_v1.h5') from tensorflow.keras.models to load the model.

2. Step 2: Continue training and save updated model

   Call model.fit() with new data, then save with model.save('model_v2.h5').

3. Final Answer:

   from tensorflow.keras.models import load_model model = load_model('model_v1.h5') model.fit(new_data, new_labels, epochs=5) model.save('model_v2.h5') -> Option A

4. Quick Check:

   Load, fit, save = from tensorflow.keras.models import load_model model = load_model('model_v1.h5') model.fit(new_data, new_labels, epochs=5) model.save('model_v2.h5') [OK]

Hint: Load with load_model, fit new data, then save again [OK]

Common Mistakes:

• Using undefined functions like load or save_model
• Forgetting to import load_model
• Not calling fit before saving updated model