Saving and loading models in ML Python

Introduction

Saving a model lets you keep it after training so you can use it later without retraining. Loading a model lets you bring it back to use or improve it.

You trained a model on your computer and want to use it later without retraining.

You want to share your trained model with a friend or colleague.

You want to deploy your model in an app or website.

You want to pause training and continue later from where you left off.

You want to test your model on new data without retraining.

Syntax

ML Python

model.save('filename.h5')

from tensorflow.keras.models import load_model
model = load_model('filename.h5')

Use save to save the whole model including architecture and weights.

Use load_model to load the saved model back into your program.

Examples

Saves the model to a file named my_model.h5.

ML Python

model.save('my_model.h5')

Loads the saved model from the file my_model.h5.

ML Python

from tensorflow.keras.models import load_model
model = load_model('my_model.h5')

Saves and loads only the model weights, not the architecture.

ML Python

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

Sample Model

This code trains a simple model on dummy data, saves it, loads it back, and makes predictions to show it works the same.

ML Python

import numpy as np
from tensorflow.keras.models import Sequential, load_model
from tensorflow.keras.layers import Dense

# Create simple model
model = Sequential([Dense(5, activation='relu', input_shape=(3,)), Dense(1, activation='sigmoid')])
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# Dummy data
X = np.array([[0,0,0],[1,1,1],[0,1,0],[1,0,1]])
y = np.array([0,1,0,1])

# Train model
model.fit(X, y, epochs=3, verbose=0)

# Save model
model.save('test_model.h5')

# Load model
loaded_model = load_model('test_model.h5')

# Predict with loaded model
predictions = loaded_model.predict(X)

# Print predictions rounded
print([round(float(p[0]), 2) for p in predictions])

Important Notes

Always save your model after training to avoid losing work.

Loading a model restores its architecture, weights, and optimizer state.

File extensions like .h5 are common for saved models but can vary.

Summary

Saving models lets you keep and reuse trained models without retraining.

Loading models brings saved models back into your program for predictions or further training.

Use model.save() and load_model() for easy saving and loading.

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