Binary classification model in TensorFlow - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a binary classification model using TensorFlow Keras.

TensorFlow

model = tf.keras.Sequential([
    tf.keras.layers.Dense(16, activation='relu', input_shape=(10,)),
    tf.keras.layers.Dense(1, activation=[1])
])

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

A'softmax'

B'sigmoid'

C'relu'

D'tanh'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'softmax' activation for a single output neuron.

Using 'relu' activation in the output layer.

For binary classification, the output layer uses a sigmoid activation to output probabilities between 0 and 1.

2fill in blank

medium

Complete the code to compile the binary 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'binary_crossentropy'

B'hinge'

C'mean_squared_error'

D'categorical_crossentropy'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'categorical_crossentropy' for binary classification.

Using regression loss functions like 'mean_squared_error'.

Binary classification models use 'binary_crossentropy' as the loss function.

3fill in blank

hard

Fix the error in the code to correctly train the binary classification model.

TensorFlow

history = model.fit(X_train, y_train, epochs=[1], batch_size=32)

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

ANone

B'10'

C5

D'twenty'

Attempts:

3 left

💡 Hint

Common Mistakes

Passing epochs as a string instead of an integer.

Passing None as epochs.

The number of epochs must be an integer, not a string or None.

4fill in blank

hard

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

TensorFlow

loss, [1] = model.evaluate(X_test, y_test)
print('Test [2]:', [1])

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

Aaccuracy

Bloss

Dscore

Attempts:

3 left

💡 Hint

Common Mistakes

Confusing loss and accuracy variables.

Printing the wrong variable.

The evaluate method returns loss and accuracy. We assign accuracy to a variable and print it.

5fill in blank

hard

Fill all three blanks to predict classes for new data and convert probabilities to binary labels.

TensorFlow

predictions = model.predict([1])
binary_predictions = (predictions [2] 0.5).astype([3])

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

AX_new

B>

Cint

DX_test

Attempts:

3 left

💡 Hint

Common Mistakes

Using test data instead of new data for prediction.

Using wrong comparison operator.

Not converting boolean array to integers.

We predict on new data X_new, compare probabilities to 0.5 to get True/False, then convert to integers 0 or 1.

Practice

1. What activation function is commonly used in the output layer of a binary classification model in TensorFlow?

easy

A. Tanh

B. ReLU

C. Softmax

D. Sigmoid

Solution

1. Step 1: Understand output layer role in binary classification

   The output layer must produce a probability between 0 and 1 to represent two classes.

2. Step 2: Identify suitable activation function

   Sigmoid activation compresses output to range [0, 1], perfect for binary decisions.

3. Final Answer:

   Sigmoid -> Option D

4. Quick Check:

   Binary output needs sigmoid = Sigmoid [OK]

Hint: Binary output needs sigmoid activation [OK]

Common Mistakes:

• Using softmax for binary output
• Using ReLU which outputs unbounded values
• Using tanh which outputs between -1 and 1

2. Which of the following is the correct way to compile a binary classification model in TensorFlow?

easy

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

B. model.compile(optimizer='rmsprop', loss='hinge', metrics=['accuracy'])

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

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

Solution

1. Step 1: Identify appropriate loss for binary classification

   Binary classification requires 'binary_crossentropy' loss to measure error correctly.

2. Step 2: Check optimizer and metrics

   'adam' optimizer and 'accuracy' metric are standard choices for training and evaluation.

3. Final Answer:

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

4. Quick Check:

   Binary loss = binary_crossentropy [OK]

Hint: Use binary_crossentropy loss for binary classification [OK]

Common Mistakes:

• Using categorical_crossentropy for binary tasks
• Using mean_squared_error which is for regression
• Choosing hinge loss which is for SVMs

3. Given the following TensorFlow model code, what will be the shape of the output layer?

model = tf.keras.Sequential([
  tf.keras.layers.Dense(10, activation='relu', input_shape=(5,)),
  tf.keras.layers.Dense(1, activation='sigmoid')
])

medium

A. (None, 1)

B. (None, 10)

C. (5, 1)

D. (1,)

Solution

1. Step 1: Analyze the last layer configuration

   The last Dense layer has 1 unit and sigmoid activation, so output shape is (batch_size, 1).

2. Step 2: Understand batch dimension placeholder

   TensorFlow uses None for batch size, so output shape is (None, 1).

3. Final Answer:

   (None, 1) -> Option A

4. Quick Check:

   Output units = 1 means shape = (None, 1) [OK]

Hint: Output shape matches last layer units with batch size None [OK]

Common Mistakes:

• Confusing input shape with output shape
• Ignoring batch size dimension
• Assuming output shape is (1,) without batch

4. You trained a binary classification model but the accuracy stays around 50% after many epochs. Which fix is most likely to improve the model?

medium

A. Change the output activation to softmax

B. Use binary_crossentropy loss instead of categorical_crossentropy

C. Increase the batch size to 1024

D. Remove the activation function from the output layer

Solution

1. Step 1: Identify the cause of poor accuracy

   Using categorical_crossentropy loss with a single sigmoid output causes wrong loss calculation.

2. Step 2: Apply correct loss function

   Switching to binary_crossentropy aligns loss with sigmoid output for binary classification.

3. Final Answer:

   Use binary_crossentropy loss instead of categorical_crossentropy -> Option B

4. Quick Check:

   Loss must match output activation [OK]

Hint: Match loss to output activation for correct training [OK]

Common Mistakes:

• Using softmax for binary output
• Removing output activation causing invalid probabilities
• Assuming batch size alone fixes accuracy

5. You want to build a binary classification model to predict if an email is spam or not. Your dataset has 1000 samples with 20 features each. Which model architecture and compile settings are best?

hard

A. Sequential model with one Dense layer (1 unit, sigmoid), compile with binary_crossentropy and adam

B. Sequential model with one Dense layer (20 units, softmax), compile with categorical_crossentropy and sgd

C. Sequential model with two Dense layers (10 units relu, then 1 unit sigmoid), compile with binary_crossentropy and adam

D. Sequential model with three Dense layers (64 relu, 32 relu, 1 tanh), compile with mean_squared_error and rmsprop

Solution

1. Step 1: Choose model complexity for dataset size

   Two layers with relu then sigmoid balance learning capacity and binary output.

2. Step 2: Select correct loss and optimizer

   Binary_crossentropy fits binary tasks; adam optimizer adapts well for small datasets.

3. Final Answer:

   Sequential model with two Dense layers (10 units relu, then 1 unit sigmoid), compile with binary_crossentropy and adam -> Option C

4. Quick Check:

   Two layers + sigmoid + binary_crossentropy = Best practice [OK]

Hint: Use relu hidden layers + sigmoid output + binary_crossentropy [OK]

Common Mistakes:

• Using softmax for binary classification
• Using tanh output activation
• Using mean_squared_error loss for classification