Keras as TensorFlow's high-level API - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import Keras from TensorFlow.

TensorFlow

from tensorflow import [1]

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

Anumpy

Bkeras

Cpandas

Dmatplotlib

Attempts:

3 left

💡 Hint

Common Mistakes

Importing unrelated libraries like numpy or pandas instead of keras.

Trying to import keras directly without tensorflow.

We import keras from TensorFlow to use its high-level API for building models.

2fill in blank

medium

Complete the code to create a Sequential model in Keras.

TensorFlow

model = keras.models.[1]()

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

AModel

BGraph

CNetwork

DSequential

Attempts:

3 left

💡 Hint

Common Mistakes

Using Model instead of Sequential for a simple stack.

Using undefined classes like Graph or Network.

The Sequential class is used to create a simple linear stack of layers in Keras.

3fill in blank

hard

Fix the error in the code to add a Dense layer with 10 units and ReLU activation.

TensorFlow

model.add(keras.layers.Dense([1], activation='relu'))

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

A10

Brelu

Cunits=10

D'10'

Attempts:

3 left

💡 Hint

Common Mistakes

Passing '10' as a string instead of integer 10.

Using 'units=10' as a positional argument instead of keyword argument.

The number of units must be an integer, so 10 without quotes is correct.

4fill in blank

hard

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

TensorFlow

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

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

Aadam

Bcategorical_crossentropy

Cmean_squared_error

Dsgd

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'sgd' optimizer instead of 'adam'.

Using 'mean_squared_error' loss for classification.

Adam is a popular optimizer and categorical_crossentropy is the correct loss for multi-class classification.

5fill in blank

hard

Fill all three blanks to train the model for 5 epochs with batch size 32 on data X_train and y_train.

TensorFlow

model.fit([1], [2], epochs=[3], batch_size=32)

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

AX_train

By_train

C5

D10

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping X_train and y_train arguments.

Setting epochs to 10 instead of 5.

We train the model on input data X_train and labels y_train for 5 epochs.