Handwriting recognition basics in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load the MNIST dataset for handwriting recognition.

Computer Vision

from tensorflow.keras.datasets import [1]

(train_images, train_labels), (test_images, test_labels) = [1].load_data()

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

Amnist

Bcifar10

Cfashion_mnist

Dimdb

Attempts:

3 left

💡 Hint

Common Mistakes

Using CIFAR-10 which is for object images, not handwriting.

Using IMDB which is for text data.

Using Fashion MNIST which is for clothing images.

The MNIST dataset contains handwritten digit images commonly used for handwriting recognition tasks.

2fill in blank

medium

Complete the code to normalize the pixel values of the images between 0 and 1.

Computer Vision

train_images = train_images.astype('float32') / [1]
test_images = test_images.astype('float32') / [1]

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

A256

B100

C1

D255

Attempts:

3 left

💡 Hint

Common Mistakes

Dividing by 100 or 256 which does not scale correctly.

Dividing by 1 which does nothing.

Pixel values range from 0 to 255, so dividing by 255 scales them to 0-1.

3fill in blank

hard

Fix the error in the model definition by choosing the correct activation function for the output layer.

Computer Vision

from tensorflow.keras import models, layers

model = models.Sequential([
    layers.Flatten(input_shape=(28, 28)),
    layers.Dense(128, activation='relu'),
    layers.Dense(10, activation=[1])
])

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

A'sigmoid'

B'softmax'

C'relu'

D'tanh'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'relu' or 'tanh' which are not suitable for output probabilities.

Using 'sigmoid' which is for binary classification.

For multi-class classification like digits 0-9, 'softmax' activation outputs probabilities for each class.

4fill in blank

hard

Fill both blanks to compile the model with the correct loss function and optimizer for handwriting recognition.

Computer Vision

model.compile(optimizer=[1], loss=[2], metrics=['accuracy'])

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

A'adam'

B'sgd'

C'sparse_categorical_crossentropy'

D'binary_crossentropy'

Attempts:

3 left

💡 Hint

Common Mistakes

Using binary crossentropy which is for binary classification.

Using SGD optimizer without adaptive learning rate.

Adam optimizer is popular for training neural networks. Sparse categorical crossentropy is used for multi-class classification with integer labels.

5fill in blank

hard

Fill all three blanks to train the model for 5 epochs with a batch size of 64 and validate on test data.

Computer Vision

history = model.fit(train_images, train_labels, epochs=[1], batch_size=[2], validation_data=([3], test_labels))

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

A32

B64

Ctest_images

D5

Attempts:

3 left

💡 Hint

Common Mistakes

Using batch size 32 instead of 64 as specified.

Using train_images instead of test_images for validation.

Training for 5 epochs with batch size 64 is common. Validation data uses test images and labels.

Practice

1. What is the main goal of handwriting recognition in computer vision?

easy

A. To convert images of handwritten text into digital text

B. To create handwritten images from typed text

C. To detect faces in handwritten notes

D. To enhance the colors of handwritten images

Solution

1. Step 1: Understand handwriting recognition purpose

   Handwriting recognition aims to read and convert handwritten text images into machine-readable text.

2. Step 2: Compare options with this goal

   Only To convert images of handwritten text into digital text matches this goal; others describe unrelated tasks.

3. Final Answer:

   To convert images of handwritten text into digital text -> Option A

4. Quick Check:

   Handwriting recognition = convert handwriting to text [OK]

Hint: Think: handwriting recognition means reading handwriting [OK]

Common Mistakes:

• Confusing recognition with image enhancement
• Thinking it creates handwriting instead of reading it
• Mixing handwriting with face detection

2. Which Python library is commonly used to load the MNIST dataset for handwriting recognition?

easy

A. pandas

B. matplotlib

C. tensorflow.keras.datasets

D. scikit-learn.preprocessing

Solution

1. Step 1: Recall common MNIST loading methods

   The MNIST dataset is often loaded using tensorflow.keras.datasets for easy access.

2. Step 2: Check options for dataset loading

   Only tensorflow.keras.datasets provides direct MNIST loading; others do not.

3. Final Answer:

   tensorflow.keras.datasets -> Option C

4. Quick Check:

   MNIST load = tensorflow.keras.datasets [OK]

Hint: Remember: TensorFlow has built-in MNIST loader [OK]

Common Mistakes:

• Choosing matplotlib which is for plotting
• Selecting pandas which handles tables, not images
• Confusing preprocessing with dataset loading

3. What will be the output shape of the images array after loading MNIST dataset with (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()?

medium

A. (28, 28, 60000)

B. (60000, 28, 28)

C. (60000, 784)

D. (60000, 28, 28, 1)

Solution

1. Step 1: Understand MNIST image shape

   MNIST images are 28x28 pixels grayscale images, and training set has 60000 samples.

2. Step 2: Check output shape from load_data()

   Images are loaded as (60000, 28, 28) without channel dimension by default.

3. Final Answer:

   (60000, 28, 28) -> Option B

4. Quick Check:

   MNIST images shape = (60000, 28, 28) [OK]

Hint: MNIST images are 28x28 pixels, 60000 training samples [OK]

Common Mistakes:

• Assuming images are flattened to 784 by default
• Confusing channel dimension presence
• Mixing sample count with image dimensions

4. Identify the error in this simple neural network code for handwriting recognition:

model = tf.keras.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28, 1)),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dense(10)
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

medium

A. Optimizer name is invalid

B. Missing activation function in the last Dense layer

C. Wrong loss function for classification

D. Incorrect input_shape in Flatten layer

Solution

1. Step 1: Review model architecture

   MNIST images from load_data() have shape (60000, 28, 28).

2. Step 2: Check input_shape in Flatten

   input_shape=(28, 28, 1) expects input of shape (None, 28, 28, 1), but MNIST data is (None, 28, 28), causing shape mismatch.

3. Final Answer:

   Incorrect input_shape in Flatten layer -> Option D

4. Quick Check:

   MNIST x_train.shape = (60000, 28, 28), input_shape=(28, 28) [OK]

Hint: MNIST default shape is (60000, 28, 28), no channel dim [OK]

Common Mistakes:

• Focusing on missing output activation (optional with this loss)
• Thinking loss is wrong (correct for integer labels)
• Assuming optimizer string is invalid (strings work)

5. You want to improve handwriting recognition accuracy by adding dropout to the model. Which code snippet correctly adds dropout after the first Dense layer?

hard

A. tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dropout(0.2)

B. tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(128, activation='relu')

C. tf.keras.layers.Dense(128, activation='relu', dropout=0.2)

D. tf.keras.layers.Dense(128, activation='relu', rate=0.2)

Solution

1. Step 1: Understand dropout usage in Keras

   Dropout is a separate layer added after a Dense layer to randomly ignore neurons during training.

2. Step 2: Check each option for correct syntax

   tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dropout(0.2) correctly places Dropout after Dense with correct parameter 0.2; options C and D incorrectly add dropout as Dense parameters; tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(128, activation='relu') reverses order, which is not standard.

3. Final Answer:

   tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dropout(0.2) -> Option A

4. Quick Check:

   Dropout is a separate layer after Dense [OK]

Hint: Dropout is its own layer placed after Dense layer [OK]

Common Mistakes:

• Trying to add dropout as Dense layer argument
• Placing Dropout before Dense layer
• Using wrong parameter names for dropout