Image generation basics in TensorFlow - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a random noise vector for image generation.

TensorFlow

noise = tf.random.normal(shape=([1], 100))

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

A128

B32

C64

D256

Attempts:

3 left

💡 Hint

Common Mistakes

Using a batch size too small like 32 may slow training.

Using a batch size too large like 256 may cause memory issues.

The batch size for generating images is commonly set to 64 for training stability.

2fill in blank

medium

Complete the code to define the output layer of a generator model for 28x28 grayscale images.

TensorFlow

model.add(tf.keras.layers.Dense(28 * 28 * 1, activation=[1]))

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

A'sigmoid'

B'relu'

C'softmax'

D'tanh'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'relu' can produce values outside the pixel range.

Using 'softmax' is for classification, not pixel output.

The 'sigmoid' activation outputs values between 0 and 1, suitable for grayscale pixel intensities.

3fill in blank

hard

Fix the error in the code to reshape the generator output to image dimensions.

TensorFlow

output_image = tf.reshape(generated_vector, ([1], 28, 28, 1))

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

A32

B100

C128

D64

Attempts:

3 left

💡 Hint

Common Mistakes

Using 100 confuses noise vector size with batch size.

Using 32 or 128 may cause shape mismatch errors.

The batch size must match the number of generated samples, here 64.

4fill in blank

hard

Fill both blanks to complete the discriminator model's output layer for binary classification.

TensorFlow

model.add(tf.keras.layers.Dense(1, activation=[1]))
model.compile(optimizer='adam', loss=[2], metrics=['accuracy'])

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

A'sigmoid'

B'binary_crossentropy'

C'categorical_crossentropy'

D'softmax'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'softmax' activation for binary output is unnecessary.

Using 'categorical_crossentropy' loss is for multi-class problems.

The discriminator outputs a probability with 'sigmoid' activation and uses 'binary_crossentropy' loss for two classes: real or fake.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps generated images to labels 1 (real) or 0 (fake) based on a condition.

TensorFlow

labels = {img: [1] for img in images if img.shape[0] [2] 28 and img.shape[1] [3] 28}

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

A1

B==

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using 1 as label for fake images is incorrect here.

Using '<' or '>' instead of '==' causes wrong filtering.

Images with shape 28x28 are labeled 0 (fake). The conditions check if height and width equal 28.

Practice

1. What is the main role of a generator model in image generation using TensorFlow?

easy

A. To classify images into categories

B. To label images with tags

C. To reduce the size of images

D. To create new images from random noise inputs

Solution

1. Step 1: Understand the generator's purpose

   The generator model takes random noise as input and learns to produce new images that look like real data.

2. Step 2: Differentiate from other models

   Unlike classifiers or reducers, the generator's job is to create images, not to label or compress them.

3. Final Answer:

   To create new images from random noise inputs -> Option D

4. Quick Check:

   Generator creates images = B [OK]

Hint: Generator turns noise into images step-by-step [OK]

Common Mistakes:

• Confusing generator with classifier
• Thinking generator compresses images
• Assuming generator labels images

2. Which of the following TensorFlow code snippets correctly defines a simple generator model layer that upsamples noise to a 7x7x128 feature map?

easy

A. tf.keras.layers.MaxPooling2D((2,2))

B. tf.keras.layers.Dense(7*7*128, use_bias=False, input_shape=(100,))

C. tf.keras.layers.Conv2D(128, (3,3), activation='relu')

D. tf.keras.layers.Flatten()

Solution

1. Step 1: Identify the layer that creates a 7x7x128 feature map from noise

   The Dense layer with units 7*7*128 reshapes noise vector into a flat feature map for upsampling.

2. Step 2: Check other options

   Conv2D applies convolution but does not reshape noise directly; MaxPooling2D reduces size; Flatten converts to 1D.

3. Final Answer:

   tf.keras.layers.Dense(7*7*128, use_bias=False, input_shape=(100,)) -> Option B

4. Quick Check:

   Dense layer reshapes noise = C [OK]

Hint: Dense layer with units = 7*7*128 reshapes noise [OK]

Common Mistakes:

• Using Conv2D before reshaping noise
• Choosing pooling layers for upsampling
• Flattening noise vector instead of reshaping

3. Given the following TensorFlow code snippet for generating an image, what will be the shape of the output image tensor?

noise = tf.random.normal([1, 100])
generated_image = generator(noise, training=False)

Assume the generator outputs images of shape (28, 28, 1).

medium

A. (1, 28, 28, 1)

B. (28, 28, 1)

C. (100, 28, 28, 1)

D. (1, 100)

Solution

1. Step 1: Understand input noise shape

   The noise tensor has shape (1, 100), where 1 is batch size and 100 is noise dimension.

2. Step 2: Check generator output shape

   The generator outputs images shaped (28, 28, 1) per input sample, so output shape includes batch size: (1, 28, 28, 1).

3. Final Answer:

   (1, 28, 28, 1) -> Option A

4. Quick Check:

   Batch size + image shape = (1, 28, 28, 1) [OK]

Hint: Output shape includes batch size from input noise [OK]

Common Mistakes:

• Ignoring batch size dimension
• Confusing noise dimension with image size
• Assuming output shape matches noise shape

4. You wrote this TensorFlow code to generate images but get an error:

noise = tf.random.normal([100])
generated_image = generator(noise, training=False)

What is the likely cause of the error?

medium

A. Noise input is missing batch dimension

B. Generator model is not compiled

C. Noise tensor has wrong data type

D. Training flag must be True for generation

Solution

1. Step 1: Check noise input shape

   The noise tensor shape is (100,), missing batch dimension which should be (1, 100) or more.

2. Step 2: Understand generator input requirements

   Generator expects batch dimension to process inputs; missing it causes shape mismatch error.

3. Final Answer:

   Noise input is missing batch dimension -> Option A

4. Quick Check:

   Batch dimension required in noise input = A [OK]

Hint: Add batch size dimension to noise input shape [OK]

Common Mistakes:

• Passing 1D noise without batch size
• Assuming training flag affects input shape
• Ignoring model compilation errors

5. You want to generate 5 new images using a trained TensorFlow generator model. Which code snippet correctly generates and displays these images using matplotlib?

hard

A. noise = tf.random.normal([5, 100])
generated_images = generator(noise)
plt.imshow(generated_images, cmap='gray')
plt.show()

B. noise = tf.random.normal([1, 100])
generated_images = generator(noise, training=False)
plt.imshow(generated_images, cmap='gray')
plt.show()

C. noise = tf.random.normal([5, 100])
generated_images = generator(noise, training=False)
for i in range(5):
plt.subplot(1, 5, i+1)
plt.imshow(generated_images[i, :, :, 0], cmap='gray')
plt.axis('off')
plt.show()

D. noise = tf.random.normal([5])
generated_images = generator(noise, training=True)
plt.imshow(generated_images[0], cmap='gray')
plt.show()

Solution

1. Step 1: Generate noise for 5 images

   Noise shape [5, 100] creates batch of 5 inputs for generator.

2. Step 2: Generate images and plot each

   Loop over generated_images to plot each image separately with plt.subplot and plt.imshow.

3. Step 3: Check other options

   noise = tf.random.normal([1, 100]) generated_images = generator(noise, training=False) plt.imshow(generated_images, cmap='gray') plt.show() generates only 1 image; another misses training=False and plots batch incorrectly; another has wrong noise shape and training mode.

4. Final Answer:

   noise = tf.random.normal([5, 100]) generated_images = generator(noise, training=False) for i in range(5): plt.subplot(1, 5, i+1) plt.imshow(generated_images[i, :, :, 0], cmap='gray') plt.axis('off') plt.show() -> Option C

5. Quick Check:

   Batch noise + loop display = A [OK]

Hint: Use batch noise and loop to display images with matplotlib [OK]

Common Mistakes:

• Using wrong noise shape for batch
• Not looping to display each image
• Forgetting training=False during generation