Inception modules in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the InceptionV3 model from Keras applications.

Computer Vision

from tensorflow.keras.applications import [1]

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AResNet50

BMobileNet

CInceptionV3

DVGG16

Attempts:

3 left

💡 Hint

Common Mistakes

Importing a different model like ResNet50 or VGG16 instead of InceptionV3.

The InceptionV3 model is imported from tensorflow.keras.applications using 'InceptionV3'.

2fill in blank

medium

Complete the code to create an InceptionV3 model without the top classification layer.

Computer Vision

model = InceptionV3(weights='imagenet', include_top=[1])

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ATrue

BNone

C'imagenet'

DFalse

Attempts:

3 left

💡 Hint

Common Mistakes

Setting include_top=True which keeps the original classification layers.

Setting include_top=False removes the final classification layers, allowing customization.

3fill in blank

hard

Fix the error in the code to add a global average pooling layer after the Inception base.

Computer Vision

from tensorflow.keras.layers import GlobalAveragePooling2D
x = model.output
x = [1](x)

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AGlobalAveragePooling2D

BDense

CGlobalMaxPooling2D

DFlatten

Attempts:

3 left

💡 Hint

Common Mistakes

Using Dense or Flatten which are not pooling layers.

Using GlobalMaxPooling2D which uses max instead of average.

GlobalAveragePooling2D reduces each feature map to a single value by averaging, suitable after convolutional layers.

4fill in blank

hard

Fill both blanks to add a dense output layer with 10 classes and softmax activation.

Computer Vision

from tensorflow.keras.layers import Dense
outputs = Dense([1], activation=[2])(x)

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A10

B'softmax'

C'relu'

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using relu activation in the output layer.

Setting units to 1 instead of 10.

The output layer has 10 units for 10 classes and uses softmax activation for probabilities.

5fill in blank

hard

Fill all three blanks to compile the model with Adam optimizer, categorical crossentropy loss, and accuracy metric.

Computer Vision

model.compile(optimizer=[1], loss=[2], metrics=[[3]])

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A'adam'

B'categorical_crossentropy'

C'accuracy'

D'mse'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'mse' loss which is for regression.

Using wrong metric like 'mse' instead of 'accuracy'.

Adam optimizer is popular; categorical crossentropy is used for multi-class classification; accuracy measures correct predictions.