Training an image classifier in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the necessary library for building a neural network model.

Computer Vision

from tensorflow import [1]

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

Akeras

Bpandas

Cnumpy

Dmatplotlib

Attempts:

3 left

💡 Hint

Common Mistakes

Importing unrelated libraries like pandas or matplotlib.

Trying to import tensorflow.keras as a separate package.

We use keras from TensorFlow to build and train neural network models.

2fill in blank

medium

Complete the code to load the CIFAR-10 dataset for training and testing.

Computer Vision

(train_images, train_labels), (test_images, test_labels) = keras.datasets.cifar10.[1]()

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

Aget_dataset

Bfetch_data

Cload_data

Ddownload

Attempts:

3 left

💡 Hint

Common Mistakes

Using non-existent functions like fetch_data or get_dataset.

Trying to call download() which is not a valid method here.

The load_data() function loads the CIFAR-10 dataset as training and testing sets.

3fill in blank

hard

Fix the error in the model compilation step by completing the optimizer argument.

Computer Vision

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

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

Aadam

Brmsprop

Csgd

Drandom

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'random' which is not a valid optimizer.

Choosing 'sgd' without adjusting learning rate may slow training.

The 'adam' optimizer is commonly used for training image classifiers and works well here.

4fill in blank

hard

Fill both blanks to normalize the image pixel values between 0 and 1.

Computer Vision

train_images = train_images [1] 255.0
 test_images = test_images [2] 255.0

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

A/

B*

C-

D+

Attempts:

3 left

💡 Hint

Common Mistakes

Using multiplication or subtraction instead of division.

Normalizing only train or only test images.

Dividing pixel values by 255.0 scales them from 0-255 to 0-1, which helps model training.

5fill in blank

hard

Fill all three blanks to define a simple CNN model with Conv2D, MaxPooling2D, and Dense layers.

Computer Vision

model = keras.Sequential([
    keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=[1]),
    keras.layers.MaxPooling2D([2]),
    keras.layers.Flatten(),
    keras.layers.Dense([3], activation='softmax')
])

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

A(32, 32, 3)

B(2, 2)

C10

D(3, 3)

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong input shape like (28, 28, 1).

Using incorrect pool size or missing it.

Setting output units to a wrong number.

The input shape for CIFAR-10 images is (32, 32, 3). MaxPooling2D commonly uses a pool size of (2, 2). The Dense output layer has 10 units for the 10 classes.

Practice

1. What is the main goal when training an image classifier?

easy

A. To convert images into text

B. To teach the model to recognize different categories of images

C. To increase the size of the images

D. To remove colors from images

Solution

1. Step 1: Understand the purpose of image classification

   Image classification means teaching a model to identify what category an image belongs to, like cats or dogs.

2. Step 2: Identify the correct goal

   The goal is to train the model to recognize image categories, not to change image size or color.

3. Final Answer:

   To teach the model to recognize different categories of images -> Option B

4. Quick Check:

   Image classification = recognize categories [OK]

Hint: Remember: Classifier means sorting images into groups [OK]

Common Mistakes:

• Confusing image classification with image editing
• Thinking the goal is to change image colors
• Assuming the model outputs text instead of categories

2. Which code snippet correctly adds a convolutional layer in a TensorFlow Keras model?

easy

A. model.add(MaxPooling2D(32, (3, 3)))

B. model.add(Dense(32, (3, 3), activation='relu'))

C. model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 3)))

D. model.add(Flatten(32, (3, 3)))

Solution

1. Step 1: Identify the correct layer type for convolution

   Conv2D is the correct layer to extract image features using filters.

2. Step 2: Check the syntax for Conv2D

   The correct syntax includes number of filters, kernel size, activation, and input shape for the first layer.

3. Final Answer:

   model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 3))) -> Option C

4. Quick Check:

   Conv2D with filters and kernel size = model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 3))) [OK]

Hint: Conv2D needs filters, kernel size, and activation [OK]

Common Mistakes:

• Using Dense instead of Conv2D for images
• Passing wrong arguments to Flatten or MaxPooling2D
• Missing input_shape in first Conv2D layer

3. Given this code, what will be the printed accuracy after training?

import tensorflow as tf
from tensorflow.keras import layers, models

model = models.Sequential([
  layers.Conv2D(16, (3,3), activation='relu', input_shape=(28,28,1)),
  layers.Flatten(),
  layers.Dense(10, activation='softmax')
])

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

import numpy as np
x_train = np.random.random((100, 28, 28, 1))
y_train = np.random.randint(0, 10, 100)

history = model.fit(x_train, y_train, epochs=1, verbose=0)
print(f"Accuracy: {history.history['accuracy'][0]:.2f}")

medium

A. Accuracy will be around 0.10 (random guessing)

B. Accuracy will be close to 1.00 (perfect)

C. Code will raise a syntax error

D. Accuracy will be exactly 0.50

Solution

1. Step 1: Understand the data and labels

   The training data is random noise and labels are random integers from 0 to 9, so no real pattern exists.

2. Step 2: Predict model accuracy on random data

   Since the model cannot learn meaningful features, accuracy will be close to random guessing, about 10% for 10 classes.

3. Final Answer:

   Accuracy will be around 0.10 (random guessing) -> Option A

4. Quick Check:

   Random data accuracy ≈ 1/number_of_classes = 0.10 [OK]

Hint: Random labels mean accuracy near chance level [OK]

Common Mistakes:

• Expecting high accuracy on random data
• Thinking code has syntax errors
• Assuming accuracy is always 0.5

4. This code tries to train an image classifier but throws an error. What is the problem?

model = tf.keras.Sequential()
model.add(tf.keras.layers.Conv2D(32, 3, activation='relu'))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(10, activation='softmax'))
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, epochs=5)

Assume x_train shape is (100, 28, 28, 1).

medium

A. Missing input_shape in first Conv2D layer

B. Dense layer should come before Conv2D

C. Loss function is incorrect for classification

D. Optimizer 'adam' is not supported

Solution

1. Step 1: Check Conv2D layer input requirements

   The first Conv2D layer must specify input_shape to know the input image size.

2. Step 2: Identify missing input_shape

   Since input_shape is missing, TensorFlow cannot infer input dimensions, causing an error.

3. Final Answer:

   Missing input_shape in first Conv2D layer -> Option A

4. Quick Check:

   First Conv2D needs input_shape [OK]

Hint: First Conv2D layer always needs input_shape [OK]

Common Mistakes:

• Thinking Dense must come before Conv2D
• Confusing loss function for classification
• Believing 'adam' optimizer is invalid

5. You want to improve your image classifier's accuracy on a small dataset. Which approach is best?

hard

A. Remove the activation functions from all layers

B. Reduce the number of convolutional layers to one

C. Train for only one epoch to avoid overfitting

D. Add data augmentation like rotations and flips during training

Solution

1. Step 1: Understand challenges with small datasets

   Small datasets can cause overfitting, where the model memorizes instead of generalizing.

2. Step 2: Identify best method to improve generalization

   Data augmentation creates new image variations, helping the model learn better and improve accuracy.

3. Final Answer:

   Add data augmentation like rotations and flips during training -> Option D

4. Quick Check:

   Data augmentation improves small dataset accuracy [OK]

Hint: Use data augmentation to expand small datasets [OK]

Common Mistakes:

• Reducing layers too much loses learning power
• Training only one epoch usually underfits
• Removing activations breaks model learning