Saving and loading models in ML Python - ML Experiment: Train & Evaluate

Experiment - Saving and loading models

Problem:You trained a simple neural network on the MNIST dataset. The model achieves good accuracy, but you want to save it to disk and load it later to make predictions without retraining.

Current Metrics:Training accuracy: 98%, Validation accuracy: 97%

Issue:Currently, the model is trained every time you run the code, which wastes time and resources.

Your Task

Save the trained model to a file and load it back to make predictions. Verify that the loaded model gives the same accuracy as the original.

Use TensorFlow/Keras for model saving and loading.

Do not retrain the model after loading.

Use the MNIST dataset provided by Keras.

Hint 1

Hint 2

Hint 3

Solution

ML Python

import tensorflow as tf
from tensorflow import keras

# Load MNIST data
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()

# Normalize data
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255

# Flatten images
x_train = x_train.reshape(-1, 28*28)
x_test = x_test.reshape(-1, 28*28)

# Build a simple model
model = keras.Sequential([
    keras.layers.Dense(128, activation='relu', input_shape=(28*28,)),
    keras.layers.Dense(10, activation='softmax')
])

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

# Train the model
model.fit(x_train, y_train, epochs=5, batch_size=32, validation_split=0.1)

# Save the model
model.save('mnist_model.h5')

# Load the model
loaded_model = keras.models.load_model('mnist_model.h5')

# Evaluate loaded model
loss, accuracy = loaded_model.evaluate(x_test, y_test, verbose=0)
print(f'Loaded model accuracy: {accuracy * 100:.2f}%')

Added model.save('mnist_model.h5') to save the trained model to disk.

Used keras.models.load_model('mnist_model.h5') to load the saved model.

Evaluated the loaded model on the test set to verify accuracy without retraining.

Results Interpretation

Before saving, the model had a validation accuracy of 97%. After saving and loading, the model's accuracy on the test set remains about 97%, confirming the model was saved and loaded correctly.

Saving and loading models allows you to reuse trained models without retraining, saving time and resources while keeping performance intact.

Bonus Experiment

Try saving the model in TensorFlow's SavedModel format instead of HDF5 and load it back.

💡 Hint

Use model.save('folder_name') without the .h5 extension to save in SavedModel format, and load it with keras.models.load_model('folder_name').

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