Text classification model in TensorFlow - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the TensorFlow library.

TensorFlow

import [1] as tf

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

Atensorflow

Btorch

Csklearn

Dnumpy

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'torch' which is for PyTorch, not TensorFlow.

Using 'sklearn' which is for traditional machine learning.

Using 'numpy' which is for numerical operations.

TensorFlow is imported using the name 'tensorflow' to use its functions with the alias 'tf'.

2fill in blank

medium

Complete the code to create a Sequential model for text classification.

TensorFlow

model = tf.keras.models.[1]()

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

AModel

BGraph

CFunctional

DSequential

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'Model' which requires a different API style.

Using 'Functional' which is not a class in tf.keras.models.

Using 'Graph' which is not a TensorFlow model class.

The Sequential class is used to build a simple linear stack of layers for the model.

3fill in blank

hard

Fix the error in the code to add an Embedding layer with input dimension 10000 and output dimension 16.

TensorFlow

model.add(tf.keras.layers.Embedding([1], 16))

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

A1000

B16

C10000

D5000

Attempts:

3 left

💡 Hint

Common Mistakes

Using 1000 or 5000 which are smaller than the vocabulary size.

Using 16 which is the output dimension, not input dimension.

The input dimension should match the vocabulary size, which is 10000 here.

4fill in blank

hard

Fill both blanks to compile the model with Adam optimizer and sparse categorical crossentropy loss.

TensorFlow

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

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

Aadam

Bsgd

Csparse_categorical_crossentropy

Dcategorical_crossentropy

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'sgd' optimizer which is less common here.

Using 'categorical_crossentropy' which expects one-hot labels.

Adam is a popular optimizer and sparse categorical crossentropy is used for integer labels in classification.

5fill in blank

hard

Fill all three blanks to train the model for 10 epochs with training data and validation data.

TensorFlow

history = model.fit([1], [2], epochs=[3], validation_data=val_data)

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

Atrain_data

Btrain_labels

C10

D5

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping data and labels.

Using too few epochs like 5 which may underfit.

Model.fit requires training inputs, training labels, and number of epochs to train.

Practice

1. What is the main purpose of a text classification model in TensorFlow?

easy

A. To sort text into different categories based on content

B. To generate new text from a given input

C. To translate text from one language to another

D. To detect objects in images

Solution

1. Step 1: Understand the task of text classification

   Text classification means assigning labels or categories to text data based on its content.

2. Step 2: Match the task to the options

   To sort text into different categories based on content describes sorting text into categories, which matches text classification.

3. Final Answer:

   To sort text into different categories based on content -> Option A

4. Quick Check:

   Text classification = sorting text [OK]

Hint: Text classification means sorting text into groups [OK]

Common Mistakes:

• Confusing classification with text generation
• Thinking classification is for images
• Mixing up translation with classification

2. Which of the following is the correct way to add an embedding layer in a TensorFlow Sequential model for text classification?

easy

A. model.add(tf.keras.layers.Dense(64, input_dim=1000))

B. model.add(tf.keras.layers.Conv2D(64, kernel_size=3))

C. model.add(tf.keras.layers.Embedding(input_dim=1000, output_dim=64))

D. model.add(tf.keras.layers.LSTM(64, input_shape=(1000,)))

Solution

1. Step 1: Identify the embedding layer syntax

   The embedding layer in TensorFlow is added with tf.keras.layers.Embedding and requires input_dim and output_dim parameters.

2. Step 2: Check each option

   model.add(tf.keras.layers.Embedding(input_dim=1000, output_dim=64)) correctly uses Embedding with input_dim=1000 and output_dim=64. Others use Dense, Conv2D, or LSTM which are not embedding layers.

3. Final Answer:

   model.add(tf.keras.layers.Embedding(input_dim=1000, output_dim=64)) -> Option C

4. Quick Check:

   Embedding layer uses tf.keras.layers.Embedding [OK]

Hint: Embedding layer uses tf.keras.layers.Embedding with input/output dims [OK]

Common Mistakes:

• Using Dense or Conv2D instead of Embedding
• Missing input_dim or output_dim parameters
• Confusing LSTM with embedding layer

3. Given the following TensorFlow model code for text classification, what will be the shape of the output layer's predictions for a batch of 32 samples if there are 5 classes?

model = tf.keras.Sequential([
  tf.keras.layers.Embedding(input_dim=1000, output_dim=16, input_length=10),
  tf.keras.layers.GlobalAveragePooling1D(),
  tf.keras.layers.Dense(5, activation='softmax')
])

predictions = model(tf.random.uniform((32, 10), maxval=1000, dtype=tf.int32))

medium

A. (10, 5)

B. (32, 5)

C. (32, 10)

D. (5, 32)

Solution

1. Step 1: Analyze model output layer

   The last Dense layer has 5 units with softmax, so output shape per sample is (5,).

2. Step 2: Consider batch size

   Input batch size is 32, so output shape is (32, 5).

3. Final Answer:

   (32, 5) -> Option B

4. Quick Check:

   Batch size 32 x 5 classes = (32, 5) [OK]

Hint: Output shape = (batch size, number of classes) [OK]

Common Mistakes:

• Confusing sequence length with batch size
• Mixing up input and output shapes
• Ignoring batch dimension in output

4. You wrote this TensorFlow model for text classification but get a shape error during training:

model = tf.keras.Sequential([
  tf.keras.layers.Embedding(input_dim=5000, output_dim=32),
  tf.keras.layers.Dense(10, activation='relu'),
  tf.keras.layers.Dense(3, activation='softmax')
])

What is the likely cause of the error?

medium

A. Dense layers cannot follow Embedding layers

B. Using wrong activation function in the last layer

C. input_dim in Embedding is too small

D. Missing a Flatten or GlobalAveragePooling1D layer after Embedding

Solution

1. Step 1: Understand Embedding output shape

   Embedding outputs 3D tensor (batch, sequence_length, embedding_dim). Dense expects 2D input.

2. Step 2: Identify missing layer

   Without Flatten or GlobalAveragePooling1D, Dense layer gets wrong input shape causing error.

3. Final Answer:

   Missing a Flatten or GlobalAveragePooling1D layer after Embedding -> Option D

4. Quick Check:

   Embedding output must be flattened before Dense [OK]

Hint: Add pooling or flatten after Embedding before Dense layers [OK]

Common Mistakes:

• Assuming Dense accepts 3D input
• Changing activation won't fix shape error
• Believing Dense can't follow Embedding at all

5. You want to build a text classification model in TensorFlow that handles variable-length input sentences and classifies them into 4 categories. Which model design is best?

hard

A. Use Embedding layer, GlobalAveragePooling1D, then Dense with 4 units and softmax activation

B. Use Embedding layer, Flatten, then Dense with 4 units and sigmoid activation

C. Use Embedding layer, Conv2D, then Dense with 1 unit and softmax activation

D. Use Dense layers only without Embedding

Solution

1. Step 1: Handle variable-length input

   GlobalAveragePooling1D can convert variable-length sequences to fixed-size vectors.

2. Step 2: Choose output layer for multi-class classification

   Dense with 4 units and softmax activation outputs probabilities for 4 classes.

3. Final Answer:

   Use Embedding layer, GlobalAveragePooling1D, then Dense with 4 units and softmax activation -> Option A

4. Quick Check:

   Pooling + softmax = best for variable-length multi-class [OK]

Hint: Use pooling + softmax for variable-length multi-class text [OK]

Common Mistakes:

• Using Flatten breaks variable-length input handling
• Using sigmoid for multi-class instead of softmax
• Using Conv2D for text sequences incorrectly