Training history and visualization in TensorFlow - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to train the model and save the training history.

TensorFlow

history = model.fit(X_train, y_train, epochs=[1], validation_split=0.2)

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

A100

B5

C50

D10

Attempts:

3 left

💡 Hint

Common Mistakes

Using too many epochs can cause long training times.

Forgetting to set epochs causes an error.

The epochs parameter sets how many times the model will see the entire training data. Here, 10 epochs is a common choice for a simple training.

2fill in blank

medium

Complete the code to plot training accuracy from the history object.

TensorFlow

plt.plot(history.history['[1]'])

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

Aaccuracy

Bval_accuracy

Cval_loss

Dloss

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'loss' instead of 'accuracy' to plot accuracy.

Using validation keys when plotting training metrics.

The history.history dictionary stores metrics by their names. To plot training accuracy, use the key 'accuracy'.

3fill in blank

hard

Fix the error in the code to plot validation loss correctly.

TensorFlow

plt.plot(history.history['[1]'])

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

Aval_loss

Bval_accuracy

Closs_val

Dval_acc

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'val_acc' or 'loss_val' which are invalid keys.

Mixing validation accuracy key with loss key.

The correct key for validation loss in history.history is 'val_loss'. Other keys will cause errors or wrong plots.

4fill in blank

hard

Fill both blanks to plot training and validation accuracy.

TensorFlow

plt.plot(history.history['[1]'], label='train')
plt.plot(history.history['[2]'], label='validation')
plt.legend()

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

Aaccuracy

Bval_accuracy

Closs

Dval_loss

Attempts:

3 left

💡 Hint

Common Mistakes

Plotting loss keys instead of accuracy keys.

Mixing training loss with validation accuracy.

To compare training and validation accuracy, plot 'accuracy' and 'val_accuracy' from the history.

5fill in blank

hard

Fill all three blanks to create a dictionary of training loss, validation loss, and epochs.

TensorFlow

results = {
  '[1]': history.history['loss'],
  '[2]': history.history['val_loss'],
  '[3]': list(range(1, len(history.history['loss']) + 1))
}

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

Atrain_loss

Bvalidation_loss

Cepochs

Dloss

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect or inconsistent dictionary keys.

Not creating the epochs list correctly.

This dictionary stores training loss as 'train_loss', validation loss as 'validation_loss', and the epoch numbers as 'epochs'.

Practice

1. What does the history.history object store after training a TensorFlow model?

easy

A. The dataset used for training

B. The model's architecture details

C. Loss and accuracy values for each epoch during training

D. The optimizer's internal state

Solution

1. Step 1: Understand what history.history contains

   After training, TensorFlow's model.fit() returns a history object that stores metrics like loss and accuracy for each epoch.

2. Step 2: Identify the correct stored data

   The history.history dictionary holds lists of loss and accuracy values recorded at each epoch for training and validation.

3. Final Answer:

   Loss and accuracy values for each epoch during training -> Option C

4. Quick Check:

   Training metrics stored in history.history = Loss and accuracy values for each epoch during training [OK]

Hint: Remember: history stores metrics per epoch, not model or data [OK]

Common Mistakes:

• Confusing history with model architecture
• Thinking history stores the dataset
• Assuming history holds optimizer state

2. Which of the following is the correct way to plot training and validation accuracy from a TensorFlow history object using matplotlib?

easy

A. plt.plot(history.history['accuracy']); plt.plot(history.history['val_accuracy'])

B. plt.plot(history['accuracy']); plt.plot(history['val_accuracy'])

C. plt.plot(history.accuracy); plt.plot(history.val_accuracy)

D. plt.plot(history.accuracy()); plt.plot(history.val_accuracy())

Solution

1. Step 1: Recall how to access metrics in history object

   The history object stores metrics in a dictionary under history.history. Access keys like 'accuracy' and 'val_accuracy' as dictionary keys.

2. Step 2: Use matplotlib to plot lists from the dictionary

   Use plt.plot() with history.history['accuracy'] and history.history['val_accuracy'] to plot training and validation accuracy.

3. Final Answer:

   plt.plot(history.history['accuracy']); plt.plot(history.history['val_accuracy']) -> Option A

4. Quick Check:

   Access metrics via history.history['key'] for plotting [OK]

Hint: Always access metrics with history.history['metric_name'] [OK]

Common Mistakes:

• Using dot notation instead of dictionary keys
• Calling metrics as functions
• Accessing history directly without .history

3. Given the following code snippet, what will be the output of print(history.history['loss']) after training for 3 epochs?

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
history = model.fit(x_train, y_train, epochs=3, validation_data=(x_val, y_val))
print(history.history['loss'])

medium

A. A list of 3 loss values, one per epoch

B. An error because 'loss' key does not exist

C. A single float value of final loss

D. [0.8, 0.6, 0.4]

Solution

1. Step 1: Understand what history.history['loss'] contains

   It stores the loss values recorded at the end of each epoch during training as a list.

2. Step 2: Predict the output after 3 epochs

   Since training runs for 3 epochs, the list will have 3 float values representing loss per epoch, not just one or a fixed list.

3. Final Answer:

   A list of 3 loss values, one per epoch -> Option A

4. Quick Check:

   Loss per epoch stored as list = A list of 3 loss values, one per epoch [OK]

Hint: Loss history is a list with one value per epoch [OK]

Common Mistakes:

• Expecting a single float instead of a list
• Assuming fixed loss values without training
• Thinking 'loss' key is missing

4. Identify the error in this code snippet that tries to plot training and validation loss:

import matplotlib.pyplot as plt
plt.plot(history['loss'])
plt.plot(history['val_loss'])
plt.show()

medium

A. plt.plot() cannot plot lists

B. history should be accessed as history.history, not directly

C. Missing plt.title() causes error

D. No error, code runs fine

Solution

1. Step 1: Check how history metrics are accessed

   The history object stores metrics inside the history attribute, so direct access like history['loss'] is incorrect.

2. Step 2: Correct the access to history.history['loss']

   To fix, use history.history['loss'] and history.history['val_loss'] for plotting.

3. Final Answer:

   history should be accessed as history.history, not directly -> Option B

4. Quick Check:

   Access metrics via history.history, not history [OK]

Hint: Use history.history to access metrics, not history alone [OK]

Common Mistakes:

• Accessing history metrics directly
• Assuming plt.plot can't plot lists
• Thinking missing title causes error

5. You trained a model for 10 epochs but notice the validation loss increases after epoch 5 while training loss decreases. How can visualizing the training history help you decide the next step?

hard

A. It suggests increasing the learning rate to fix validation loss

B. It confirms the model is perfect, so no changes needed

C. It means the training data is incorrect and should be discarded

D. It shows overfitting, so you might stop training early or add regularization

Solution

1. Step 1: Interpret the training and validation loss curves

   When training loss decreases but validation loss increases, it indicates the model is overfitting the training data.

2. Step 2: Decide actions based on visualization

   Visualizing history helps identify overfitting, suggesting to stop early, add dropout, or use regularization to improve generalization.

3. Final Answer:

   It shows overfitting, so you might stop training early or add regularization -> Option D

4. Quick Check:

   Increasing validation loss with decreasing training loss = overfitting [OK]

Hint: Watch for validation loss rising while training loss falls [OK]

Common Mistakes:

• Ignoring validation loss trends
• Increasing learning rate without reason
• Assuming data is wrong without checking