Accuracy and loss monitoring in TensorFlow

Introduction

Accuracy and loss monitoring helps you see how well your machine learning model is learning. It shows if the model is improving or needs changes.

When training a model to check if it is learning correctly.

When comparing different models to pick the best one.

When tuning model settings to improve performance.

When spotting if the model is overfitting or underfitting.

When sharing training progress with your team or stakeholders.

Syntax

TensorFlow

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

history = model.fit(x_train, y_train, epochs=5, validation_data=(x_val, y_val))

model.compile sets how the model learns and what to watch.

metrics=['accuracy'] tells TensorFlow to track accuracy during training.

Examples

Using SGD optimizer and categorical crossentropy loss with accuracy metric.

TensorFlow

model.compile(optimizer='sgd', loss='categorical_crossentropy', metrics=['accuracy'])

Train for 10 epochs and use 20% of data for validation to monitor accuracy and loss.

TensorFlow

history = model.fit(x_train, y_train, epochs=10, validation_split=0.2)

Access and print accuracy and loss values recorded during training.

TensorFlow

print(history.history['accuracy'])
print(history.history['loss'])

Sample Model

This example creates a simple model, trains it on random data for 2 epochs, and prints the accuracy and loss values recorded during training.

TensorFlow

import tensorflow as tf

# Prepare dummy data
x_train = tf.random.normal([100, 28, 28])
y_train = tf.random.uniform([100], maxval=10, dtype=tf.int32)

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

# Compile model with accuracy metric
model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

# Train model
history = model.fit(x_train, y_train, epochs=2)

# Print accuracy and loss from history
print('Accuracy:', history.history['accuracy'])
print('Loss:', history.history['loss'])

Important Notes

Accuracy shows the percentage of correct predictions.

Loss shows how far the model's predictions are from the true answers; lower is better.

Validation data helps check if the model works well on new data.

Summary

Accuracy and loss monitoring helps track model learning progress.

Use metrics=['accuracy'] in model.compile to monitor accuracy.

Access training results from history.history after model.fit.

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