Accuracy and loss monitoring in TensorFlow - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to print the training accuracy after each epoch.

TensorFlow

model.fit(x_train, y_train, epochs=5, callbacks=[tf.keras.callbacks.LambdaCallback(on_epoch_end=lambda epoch, logs: print('Accuracy:', logs.get('[1]')))])

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

Aaccuracy

Bloss

Cval_loss

Dval_accuracy

Attempts:

3 left

💡 Hint

Common Mistakes

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

Using validation keys like 'val_accuracy' when training accuracy is needed.

The 'accuracy' key in logs contains the training accuracy for the current epoch.

2fill in blank

medium

Complete the code to include validation loss monitoring during training.

TensorFlow

model.fit(x_train, y_train, epochs=10, validation_data=(x_val, y_val), callbacks=[tf.keras.callbacks.LambdaCallback(on_epoch_end=lambda epoch, logs: print('Validation Loss:', logs.get('[1]')))])

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

Aval_accuracy

Baccuracy

Closs

Dval_loss

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'loss' instead of 'val_loss' to get validation loss.

Using 'val_accuracy' when loss is needed.

The 'val_loss' key in logs contains the validation loss after each epoch.

3fill in blank

hard

Fix the error in the callback to correctly print validation accuracy.

TensorFlow

model.fit(x_train, y_train, epochs=3, validation_data=(x_val, y_val), callbacks=[tf.keras.callbacks.LambdaCallback(on_epoch_end=lambda epoch, logs: print('Val Accuracy:', logs.get('[1]')))])

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

Aval_accuracy

Bloss

Caccuracy

Dval_loss

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'accuracy' instead of 'val_accuracy' for validation accuracy.

Using 'val_loss' when accuracy is needed.

Validation accuracy is stored under the key 'val_accuracy' in logs.

4fill in blank

hard

Fill both blanks to create a dictionary that stores training loss and validation accuracy after each epoch.

TensorFlow

history_dict = { 'train_loss': logs.get('[1]'), 'val_acc': logs.get('[2]') }

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

Aloss

Baccuracy

Cval_accuracy

Dval_loss

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up training and validation keys.

Using 'accuracy' instead of 'loss' for training loss.

'loss' is the training loss key and 'val_accuracy' is the validation accuracy key in logs.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that stores metric values greater than 0.5 from logs.

TensorFlow

filtered_metrics = {k: v for k, v in logs.items() if v [1] 0.5 and 'acc' [2] k and 'val' [3] k}

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

A>

Bin

Cnot in

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' for value comparison.

Confusing 'in' and 'not in' for substring checks.

We want values greater than 0.5, keys containing 'acc', and keys not containing 'val'.

Practice

1. What is the main purpose of monitoring accuracy and loss during TensorFlow model training?

easy

A. To change the model architecture automatically

B. To track how well the model is learning and improving

C. To increase the size of the training dataset

D. To speed up the training process by skipping epochs

Solution

1. Step 1: Understand accuracy and loss roles

   Accuracy shows how many predictions are correct, loss shows error size.

2. Step 2: Purpose of monitoring during training

   Tracking these helps see if the model is learning or needs adjustment.

3. Final Answer:

   To track how well the model is learning and improving -> Option B

4. Quick Check:

   Accuracy and loss track learning progress = C [OK]

Hint: Accuracy and loss show model learning quality [OK]

Common Mistakes:

• Thinking accuracy changes dataset size
• Believing monitoring changes model structure
• Assuming monitoring speeds training automatically

2. Which is the correct way to include accuracy monitoring when compiling a TensorFlow model?

easy

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

B. model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')

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

D. model.compile(loss='sparse_categorical_crossentropy', metrics='accuracy')

Solution

1. Step 1: Check required compile parameters

   Optimizer and loss are required; metrics is optional for monitoring.

2. Step 2: Correct syntax for metrics

   metrics must be a list like ['accuracy'], not a string alone.

3. Final Answer:

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

4. Quick Check:

   metrics=['accuracy'] in compile = B [OK]

Hint: Use metrics=['accuracy'] inside model.compile [OK]

Common Mistakes:

• Omitting metrics parameter
• Passing metrics as a string instead of list
• Leaving out loss or optimizer

3. Given this code snippet, what will print(history.history['accuracy']) output?

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
history = model.fit(x_train, y_train, epochs=2)
print(history.history['accuracy'])

medium

A. A list of loss values for each epoch

B. A single float value of final accuracy, e.g. 0.90

C. An error because 'accuracy' is not in history

D. A list of accuracy values for each epoch, e.g. [0.85, 0.90]

Solution

1. Step 1: Understand history.history content

   It stores lists of metric values per epoch, including accuracy if monitored.

2. Step 2: What history.history['accuracy'] returns

   It returns a list of accuracy values, one per epoch, not a single value or error.

3. Final Answer:

   A list of accuracy values for each epoch, e.g. [0.85, 0.90] -> Option D

4. Quick Check:

   history.history['accuracy'] = list per epoch [OK]

Hint: history.history['accuracy'] holds accuracy per epoch list [OK]

Common Mistakes:

• Expecting a single float instead of list
• Confusing accuracy with loss values
• Assuming key 'accuracy' is missing

4. You run this code but get a KeyError when accessing history.history['accuracy']. What is the likely cause?

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

medium

A. Accuracy was not included in metrics during model.compile

B. The model.fit call is missing the epochs parameter

C. The loss function is incorrect for accuracy monitoring

D. history.history only stores loss, not accuracy

Solution

1. Step 1: Check model.compile parameters

   Accuracy monitoring requires metrics=['accuracy'] in compile, missing here.

2. Step 2: Effect on history.history keys

   Without metrics=['accuracy'], history.history has no 'accuracy' key, causing KeyError.

3. Final Answer:

   Accuracy was not included in metrics during model.compile -> Option A

4. Quick Check:

   Missing metrics=['accuracy'] causes KeyError [OK]

Hint: Always add metrics=['accuracy'] to compile to track accuracy [OK]

Common Mistakes:

• Forgetting to add metrics=['accuracy']
• Assuming loss function controls accuracy keys
• Thinking epochs parameter affects history keys

5. You want to monitor both accuracy and loss during training and plot their progress after training. Which code snippet correctly compiles the model and accesses the data for plotting?

hard

A. model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics='accuracy')
history = model.fit(x_train, y_train, epochs=5)
plt.plot(history['accuracy'])
plt.plot(history['loss'])

B. model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')
history = model.fit(x_train, y_train, epochs=5)
plt.plot(history.history['accuracy'])
plt.plot(history.history['loss'])

C. model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
history = model.fit(x_train, y_train, epochs=5)
plt.plot(history.history['accuracy'])
plt.plot(history.history['loss'])

D. model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
history = model.fit(x_train, y_train, epochs=5)
plt.plot(history['accuracy'])
plt.plot(history['loss'])

Solution

1. Step 1: Check model.compile metrics syntax

   metrics must be a list like ['accuracy']. B omits it, C uses string 'accuracy'.

2. Step 2: Check history access for plotting

   history.history['accuracy'] and history.history['loss'] are correct; history['accuracy'] fails as history object lacks these attributes.

3. Final Answer:

   model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) history = model.fit(x_train, y_train, epochs=5) plt.plot(history.history['accuracy']) plt.plot(history.history['loss']) -> Option C

4. Quick Check:

   metrics list + history.history keys = A [OK]

Hint: Use metrics=['accuracy'] and history.history for plotting [OK]

Common Mistakes:

• Passing metrics as string instead of list
• Accessing history keys directly on history object
• Omitting metrics parameter