Simple GAN implementation in TensorFlow - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define the generator model input layer.

TensorFlow

generator = tf.keras.Sequential([
    tf.keras.layers.Dense(7 * 7 * 256, use_bias=False, input_shape=([1],)),
    tf.keras.layers.BatchNormalization(),
    tf.keras.layers.LeakyReLU(),
    tf.keras.layers.Reshape((7, 7, 256)),
    tf.keras.layers.Conv2DTranspose(128, (5, 5), strides=(1, 1), padding='same', use_bias=False),
    tf.keras.layers.BatchNormalization(),
    tf.keras.layers.LeakyReLU(),
    tf.keras.layers.Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same', use_bias=False),
    tf.keras.layers.BatchNormalization(),
    tf.keras.layers.LeakyReLU(),
    tf.keras.layers.Conv2DTranspose(1, (5, 5), strides=(2, 2), padding='same', use_bias=False, activation='tanh')
])

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A100

B28

C256

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using image dimensions instead of noise vector size.

Confusing number of filters with input shape.

The generator input shape is a vector of size 100 representing the noise input.

2fill in blank

medium

Complete the code to define the discriminator model output layer.

TensorFlow

discriminator = tf.keras.Sequential([
    tf.keras.layers.Conv2D(64, (5, 5), strides=(2, 2), padding='same', input_shape=(28, 28, 1)),
    tf.keras.layers.LeakyReLU(),
    tf.keras.layers.Dropout(0.3),
    tf.keras.layers.Conv2D(128, (5, 5), strides=(2, 2), padding='same'),
    tf.keras.layers.LeakyReLU(),
    tf.keras.layers.Dropout(0.3),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense([1], activation='sigmoid')
])

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A10

B256

C100

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using multiple output neurons instead of one.

Forgetting to use sigmoid activation.

The discriminator outputs a single probability value indicating real or fake.

3fill in blank

hard

Fix the error in the training step where the generator loss is computed.

TensorFlow

def generator_loss(fake_output):
    return cross_entropy(tf.ones_like(fake_output), [1])

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Anoise

Breal_output

Cfake_output

Ddiscriminator_output

Attempts:

3 left

💡 Hint

Common Mistakes

Using undefined variables like real_output or noise.

Using discriminator output on real images instead of fake.

Generator loss compares fake output to ones to fool the discriminator.

4fill in blank

hard

Fill both blanks to complete the training step function for GAN.

TensorFlow

def train_step(images):
    noise = tf.random.normal([[1], 100])
    with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
        generated_images = generator(noise, training=True)
        real_output = discriminator(images, training=True)
        fake_output = discriminator(generated_images, training=True)
        gen_loss = generator_loss(fake_output)
        disc_loss = discriminator_loss(real_output, fake_output)
    gradients_of_generator = gen_tape.gradient(gen_loss, [2].trainable_variables)
    gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)
    generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))
    discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))

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A32

B64

Cgenerator

Ddiscriminator

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong batch size for noise.

Computing gradients for discriminator variables in generator gradient step.

Batch size is 32 and gradients are computed for the generator model.

5fill in blank

hard

Fill all three blanks to complete the code that generates and saves images during training.

TensorFlow

def generate_and_save_images(model, epoch, test_input):
    predictions = model([1], training=False)
    fig = plt.figure(figsize=(4, 4))
    for i in range(predictions.shape[0]):
        plt.subplot(4, 4, i + 1)
        plt.imshow(predictions[i, :, :, 0] * 127.5 + 127.5, cmap='gray')
        plt.axis('[2]')
    plt.savefig('image_at_epoch_[3].png')
    plt.close(fig)

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Atest_input

Bnoise

Coff

Don

Attempts:

3 left

💡 Hint

Common Mistakes

Using undefined variable noise instead of test_input.

Forgetting to turn axis off.

Not saving images with epoch number.

Use test_input for predictions, turn axis off, and save with epoch number.

Practice

1. In a simple GAN, what is the main role of the generator?

easy

A. To classify data as real or fake

B. To create fake data that looks real

C. To optimize the loss function of the discriminator

D. To preprocess the input data

Solution

1. Step 1: Understand the generator's purpose

   The generator's job is to produce fake data samples that resemble real data.

2. Step 2: Differentiate from discriminator's role

   The discriminator's role is to distinguish real from fake data, not the generator's.

3. Final Answer:

   To create fake data that looks real -> Option B

4. Quick Check:

   Generator = create fake data [OK]

Hint: Generator makes fake data to fool discriminator [OK]

Common Mistakes:

• Confusing generator with discriminator
• Thinking generator classifies data
• Assuming generator preprocesses data

2. Which of the following is the correct way to define the input noise vector for the generator in TensorFlow?

easy

A. noise = tf.random.normal([batch_size, noise_dim])

B. noise = tf.zeros([batch_size, noise_dim])

C. noise = tf.ones([batch_size, noise_dim])

D. noise = tf.random.uniform([noise_dim])

Solution

1. Step 1: Identify typical noise input shape

   The noise vector usually has shape [batch_size, noise_dim] to generate a batch of samples.

2. Step 2: Check correct TensorFlow function

   tf.random.normal generates random normal noise, commonly used for GAN input.

3. Final Answer:

   noise = tf.random.normal([batch_size, noise_dim]) -> Option A

4. Quick Check:

   Use tf.random.normal with batch size and noise dim [OK]

Hint: Use tf.random.normal with batch and noise dims [OK]

Common Mistakes:

• Using tf.zeros or tf.ones which produce constant noise
• Missing batch dimension in noise shape
• Using tf.random.uniform without batch dimension

3. Given this TensorFlow code snippet for a GAN training step, what will be the shape of fake_output?

noise = tf.random.normal([32, 100])
fake_images = generator(noise)
fake_output = discriminator(fake_images)

medium

A. [32, 1]

B. [100, 32]

C. [32, 100]

D. [1, 32]

Solution

1. Step 1: Analyze input batch size

   The noise input has batch size 32, so generator outputs 32 fake images.

2. Step 2: Understand discriminator output shape

   The discriminator outputs a prediction per image, usually shape [batch_size, 1].

3. Final Answer:

   [32, 1] -> Option A

4. Quick Check:

   Discriminator output shape = [batch_size, 1] [OK]

Hint: Discriminator outputs one score per input image [OK]

Common Mistakes:

• Confusing noise dimension with output shape
• Swapping batch size and feature dimensions
• Assuming discriminator outputs full image shape

4. You have this error during GAN training: ValueError: Shapes (32, 1) and (64, 1) are incompatible. What is the most likely cause?

medium

A. Discriminator model is missing an output layer

B. Generator output shape is incorrect

C. Mismatch between batch sizes of real and fake data during training

D. Noise vector dimension is too large

Solution

1. Step 1: Understand the error message

   The error shows two tensors with batch sizes 32 and 64 being compared or combined.

2. Step 2: Identify cause in GAN training

   Real and fake batches must have the same batch size when computing loss or concatenating.

3. Final Answer:

   Mismatch between batch sizes of real and fake data during training -> Option C

4. Quick Check:

   Batch size mismatch causes shape errors [OK]

Hint: Ensure real and fake batches have same size [OK]

Common Mistakes:

• Ignoring batch size mismatch
• Blaming generator output shape without checking batch
• Assuming model layers missing cause shape error

5. You want to improve your simple GAN's training stability. Which change is best to try first?

hard

A. Train discriminator twice as often as generator

B. Increase noise vector dimension to 1000

C. Remove batch normalization layers from generator

D. Use label smoothing on discriminator labels

Solution

1. Step 1: Understand training stability issues

   GANs often suffer from unstable training causing oscillations or mode collapse.

2. Step 2: Evaluate common techniques

   Label smoothing (e.g., using 0.9 instead of 1 for real labels) helps discriminator not be too confident, improving stability.

3. Final Answer:

   Use label smoothing on discriminator labels -> Option D

4. Quick Check:

   Label smoothing improves GAN training stability [OK]

Hint: Try label smoothing first to stabilize GAN training [OK]

Common Mistakes:

• Increasing noise dimension too much adds complexity
• Removing batch norm can hurt generator learning
• Training discriminator more often can cause imbalance