Why TensorFlow handles text effectively - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import TensorFlow's text processing module.

TensorFlow

import tensorflow as tf
from tensorflow import [1]

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

Atext

Bimage

Caudio

Ddata

Attempts:

3 left

💡 Hint

Common Mistakes

Importing unrelated modules like image or audio instead of text.

TensorFlow has a dedicated text module for handling text data efficiently.

2fill in blank

medium

Complete the code to create a tokenizer that splits sentences into words.

TensorFlow

tokenizer = tf.keras.preprocessing.text.[1]()

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

ATokenizerizer

BTokenizer

CTextSplitter

DWordTokenizer

Attempts:

3 left

💡 Hint

Common Mistakes

Using made-up class names like Tokenizerizer or WordTokenizer.

The Tokenizer class in TensorFlow helps split text into tokens (words).

3fill in blank

hard

Fix the error in the code to convert text to sequences of integers.

TensorFlow

tokenizer = tf.keras.preprocessing.text.Tokenizer()
tokenizer.fit_on_texts(texts)
sequences = tokenizer.[1](texts)

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

Atexts_to_sequences

Btext_to_seq

Ctext_to_sequences

Dtexts_to_seq

Attempts:

3 left

💡 Hint

Common Mistakes

Using singular forms or incorrect method names causing errors.

The correct method to convert texts to sequences is texts_to_sequences.

4fill in blank

hard

Fill both blanks to create a dictionary of word counts for words longer than 3 letters.

TensorFlow

word_counts = {word: [1] for word in words if len(word) [2] 3}

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

Awords.count(word)

Blen(word)

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using length as value instead of count.

Using wrong comparison operators.

We count how many times each word appears using words.count(word) and filter words longer than 3 letters with len(word) > 3.

5fill in blank

hard

Fill all three blanks to create a dictionary of uppercase words and their counts for words longer than 4 letters.

TensorFlow

result = { [1]: [2] for w in words if len(w) [3] 4 }

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

Aw.upper()

Bwords.count(w)

C>

Dw.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase keys instead of uppercase.

Using wrong comparison operators.

The dictionary keys are uppercase words w.upper(), values are counts words.count(w), and we filter words longer than 4 letters with len(w) > 4.

Practice

1. Why does TensorFlow convert text into numbers before processing?

easy

A. Because numbers are easier to print

B. Because text files are too large to handle

C. Because computers understand numbers better than text

D. Because text cannot be stored in memory

Solution

1. Step 1: Understand computer data processing

   Computers work with numbers internally, not raw text characters.

2. Step 2: Reason why text is converted

   Converting text to numbers allows TensorFlow to perform math operations needed for learning.

3. Final Answer:

   Because computers understand numbers better than text -> Option C

4. Quick Check:

   Text to numbers = computers understand numbers [OK]

Hint: Remember: computers only understand numbers, not words [OK]

Common Mistakes:

• Thinking text files are too large to process
• Believing numbers are easier to print than text
• Assuming text cannot be stored in memory

2. Which TensorFlow layer is commonly used to turn words into vectors?

easy

A. Dense layer

B. Embedding layer

C. Dropout layer

D. Conv2D layer

Solution

1. Step 1: Identify layers for text processing

   Embedding layers convert words into numeric vectors capturing meaning.

2. Step 2: Compare with other layers

   Dense layers are for general neural nets, Dropout prevents overfitting, Conv2D is for images.

3. Final Answer:

   Embedding layer -> Option B

4. Quick Check:

   Word to vector = Embedding layer [OK]

Hint: Embedding layers map words to numbers smartly [OK]

Common Mistakes:

• Choosing Dense layer for word vectors
• Confusing Conv2D with text layers
• Thinking Dropout creates vectors

3. What will be the output shape of this TensorFlow code?

import tensorflow as tf
layer = tf.keras.layers.Embedding(input_dim=1000, output_dim=64)
input_data = tf.constant([[1, 2, 3], [4, 5, 6]])
output = layer(input_data)
print(output.shape)

medium

A. (2, 3, 64)

B. (3, 2, 64)

C. (2, 64)

D. (3, 64)

Solution

1. Step 1: Understand input shape

   Input is a 2x3 tensor (2 rows, 3 words each).

2. Step 2: Embedding output shape

   Embedding converts each word index to a 64-length vector, so output shape is (2, 3, 64).

3. Final Answer:

   (2, 3, 64) -> Option A

4. Quick Check:

   Input shape (2,3) + vector size 64 = (2,3,64) [OK]

Hint: Embedding output shape = input shape + vector size [OK]

Common Mistakes:

• Swapping batch and sequence dimensions
• Forgetting embedding vector size
• Assuming output is 2D instead of 3D

4. Identify the error in this TensorFlow text processing code:

import tensorflow as tf
layer = tf.keras.layers.Embedding(input_dim=5000, output_dim=32)
input_data = tf.constant([1, 2, 3, 4])
output = layer(input_data)
print(output.shape)

medium

A. input_data should be 2D, not 1D

B. output_dim must be larger than input_dim

C. Embedding layer requires float input

D. Missing activation function in Embedding

Solution

1. Step 1: Check input shape requirements

   Embedding expects 2D input: (batch_size, sequence_length), but input_data is 1D.

2. Step 2: Confirm other parameters

   output_dim can be smaller than input_dim; input can be integer indices; activation not required.

3. Final Answer:

   input_data should be 2D, not 1D -> Option A

4. Quick Check:

   Embedding input must be 2D tensor [OK]

Hint: Embedding input must be 2D (batch, sequence) [OK]

Common Mistakes:

• Using 1D input instead of 2D
• Thinking output_dim must exceed input_dim
• Adding activation to Embedding unnecessarily

5. You want to build a chatbot using TensorFlow that understands word meaning and context. Which approach best helps TensorFlow handle text effectively?

hard

A. Convert text to uppercase and feed directly to a Conv2D layer

B. Use only a Dense layer on raw text input

C. Use Dropout layers only to prevent overfitting

D. Use an Embedding layer followed by an LSTM layer to capture word vectors and sequence context

Solution

1. Step 1: Understand text representation needs

   Embedding layers convert words to vectors capturing meaning; LSTM layers capture sequence context.

2. Step 2: Evaluate other options

   Dense layers alone can't handle sequences; Conv2D is for images; Dropout only prevents overfitting, not understanding.

3. Final Answer:

   Use an Embedding layer followed by an LSTM layer to capture word vectors and sequence context -> Option D

4. Quick Check:

   Embedding + LSTM = best for text understanding [OK]

Hint: Combine Embedding and LSTM for smart text apps [OK]

Common Mistakes:

• Using Dense layers on raw text
• Applying Conv2D to text data
• Relying only on Dropout layers