Multi-class classification model in TensorFlow - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a multi-class classification model using TensorFlow Keras.

TensorFlow

model = tf.keras.Sequential([
    tf.keras.layers.Dense(64, activation='relu', input_shape=(input_shape,)),
    tf.keras.layers.Dense(10, activation=[1])
])

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

A'sigmoid'

B'linear'

C'softmax'

D'tanh'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'sigmoid' activation which is for binary classification.

Using 'linear' activation which does not output probabilities.

For multi-class classification, the output layer uses the softmax activation to output probabilities for each class.

2fill in blank

medium

Complete the code to compile the multi-class classification model with the correct loss function.

TensorFlow

model.compile(optimizer='adam', loss=[1], metrics=['accuracy'])

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

A'categorical_crossentropy'

B'binary_crossentropy'

C'mean_squared_error'

D'hinge'

Attempts:

3 left

💡 Hint

Common Mistakes

Using binary_crossentropy which is for binary classification.

Using mean_squared_error which is for regression tasks.

For multi-class classification with one-hot encoded labels, categorical_crossentropy is the correct loss function.

3fill in blank

hard

Fix the error in the code to correctly prepare labels for multi-class classification training.

TensorFlow

y_train_encoded = tf.keras.utils.to_categorical([1], num_classes=10)

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

AX_train

BX_test

Cy_test

Dy_train

Attempts:

3 left

💡 Hint

Common Mistakes

Encoding input features instead of labels.

Encoding test labels instead of training labels.

Labels for training (y_train) must be converted to one-hot encoding for multi-class classification.

4fill in blank

hard

Fill both blanks to train the model with the encoded labels and set the number of epochs.

TensorFlow

history = model.fit(X_train, [1], epochs=[2], batch_size=32, validation_split=0.2)

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

Ay_train_encoded

By_test_encoded

C10

D5

Attempts:

3 left

💡 Hint

Common Mistakes

Using test labels for training.

Setting epochs too low or too high without reason.

Use the encoded training labels y_train_encoded and set epochs to 10 for training.

5fill in blank

hard

Fill all three blanks to evaluate the model on test data and print the accuracy.

TensorFlow

loss, accuracy = model.evaluate([1], [2], batch_size=32)
print(f"Test accuracy: [3]")

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

AX_test

By_test_encoded

Caccuracy

Dloss

Attempts:

3 left

💡 Hint

Common Mistakes

Using training data for evaluation.

Printing loss instead of accuracy.

Evaluate the model on test features X_test and encoded test labels y_test_encoded, then print the accuracy.

Practice

1.

What activation function is commonly used in the last layer of a multi-class classification model in TensorFlow?

easy

A. Sigmoid

B. ReLU

C. Softmax

D. Tanh

Solution

1. Step 1: Understand the purpose of the last layer in multi-class classification

   The last layer outputs probabilities for each class, so the activation must convert raw scores to probabilities.

2. Step 2: Identify the activation function that outputs probabilities summing to 1

   Softmax converts logits into probabilities that sum to 1, suitable for multi-class classification.

3. Final Answer:

   Softmax -> Option C

4. Quick Check:

   Softmax = last layer activation [OK]

Hint: Use softmax for multi-class output probabilities [OK]

Common Mistakes:

• Using sigmoid which is for binary classification
• Using ReLU which does not output probabilities
• Using tanh which outputs values between -1 and 1

2.

Which loss function should you use in TensorFlow for a multi-class classification model with integer labels?

easy

A. binary_crossentropy

B. sparse_categorical_crossentropy

C. mean_squared_error

D. hinge

Solution

1. Step 1: Identify the label format

   Labels are integer class IDs, not one-hot encoded vectors.

2. Step 2: Choose loss function matching integer labels for multi-class

   Sparse categorical crossentropy works with integer labels directly, unlike categorical crossentropy which needs one-hot labels.

3. Final Answer:

   sparse_categorical_crossentropy -> Option B

4. Quick Check:

   Integer labels = sparse_categorical_crossentropy [OK]

Hint: Use sparse_categorical_crossentropy for integer class labels [OK]

Common Mistakes:

• Using binary_crossentropy which is for two classes
• Using mean_squared_error which is for regression
• Using hinge loss which is for SVMs

3.

What will be the shape of the output tensor from the last layer of this TensorFlow model for multi-class classification with 4 classes?

model = tf.keras.Sequential([
  tf.keras.layers.Dense(10, activation='relu'),
  tf.keras.layers.Dense(4, activation='softmax')
])
inputs = tf.random.uniform((5, 8))
outputs = model(inputs)
print(outputs.shape)

medium

A. (4, 5)

B. (8, 4)

C. (5, 10)

D. (5, 4)

Solution

1. Step 1: Understand input and output shapes

   Input batch size is 5, each input has 8 features. The last Dense layer outputs 4 units (classes).

2. Step 2: Determine output shape from last layer

   Output shape is (batch_size, number_of_classes) = (5, 4).

3. Final Answer:

   (5, 4) -> Option D

4. Quick Check:

   Batch size 5, classes 4 = (5, 4) [OK]

Hint: Output shape = (batch_size, number_of_classes) [OK]

Common Mistakes:

• Confusing batch size and feature dimensions
• Swapping rows and columns in output shape
• Assuming output shape matches input feature size

4.

Identify the error in this TensorFlow multi-class classification model code:

model = tf.keras.Sequential([
  tf.keras.layers.Dense(16, activation='relu'),
  tf.keras.layers.Dense(3, activation='sigmoid')
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

medium

A. Last layer activation should be softmax, not sigmoid

B. Loss function should be binary_crossentropy

C. Optimizer 'adam' is invalid

D. Dense layer units must be 1 for multi-class

Solution

1. Step 1: Check last layer activation for multi-class

   Sigmoid outputs independent probabilities, not suitable for multi-class where classes are exclusive.

2. Step 2: Correct activation for multi-class classification

   Softmax outputs probabilities summing to 1, appropriate for multi-class classification.

3. Final Answer:

   Last layer activation should be softmax, not sigmoid -> Option A

4. Quick Check:

   Multi-class needs softmax activation [OK]

Hint: Use softmax activation for multi-class last layer [OK]

Common Mistakes:

• Using sigmoid activation for multi-class output
• Confusing loss functions for classification types
• Thinking optimizer name 'adam' is invalid

5.

You want to build a multi-class classification model with 5 classes. Your labels are integers from 0 to 4. Which of the following code snippets correctly defines and compiles the model?

Option A:
model = tf.keras.Sequential([
  tf.keras.layers.Dense(32, activation='relu'),
  tf.keras.layers.Dense(5, activation='softmax')
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

Option B:
model = tf.keras.Sequential([
  tf.keras.layers.Dense(32, activation='relu'),
  tf.keras.layers.Dense(5, activation='sigmoid')
])
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

Option C:
model = tf.keras.Sequential([
  tf.keras.layers.Dense(32, activation='relu'),
  tf.keras.layers.Dense(1, activation='softmax')
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

Option D:
model = tf.keras.Sequential([
  tf.keras.layers.Dense(32, activation='relu'),
  tf.keras.layers.Dense(5, activation='softmax')
])
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

hard

A. Option A

B. Option B

C. Option C

D. Option D

Solution

1. Step 1: Check output layer units and activation

   For 5 classes, output units must be 5 with softmax activation to get class probabilities.

2. Step 2: Check loss function matches label format

   Labels are integers, so sparse_categorical_crossentropy is correct loss.

3. Step 3: Verify optimizer and metrics

   Adam optimizer and accuracy metric are appropriate choices.

4. Final Answer:

   Option A -> Option A

5. Quick Check:

   Correct output units, activation, and loss for integer labels [OK]

Hint: Match output units and loss to label format [OK]

Common Mistakes:

• Using sigmoid activation for multi-class output
• Using binary_crossentropy loss for multi-class
• Setting output units to 1 instead of number of classes