Sequential model API in TensorFlow - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a simple sequential model with one dense layer.

TensorFlow

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

model = Sequential()
model.add(Dense([1], input_shape=(10,), activation='relu'))

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

ASequential

B32

C10

D'relu'

Attempts:

3 left

💡 Hint

Common Mistakes

Using the activation function name instead of number of units.

Passing the input shape as the first argument instead of units.

The first Dense layer needs the number of units as an integer. Here, 32 units is a common choice.

2fill in blank

medium

Complete the code to compile the model with mean squared error loss.

TensorFlow

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

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

Amean_squared_error

Bcategorical_crossentropy

Caccuracy

Dadam

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'accuracy' as loss instead of a loss function.

Using 'adam' as loss instead of optimizer.

For regression tasks, mean squared error is a common loss function.

3fill in blank

hard

Fix the error in the code to add a dropout layer after the first dense layer.

TensorFlow

from tensorflow.keras.layers import Dropout

model = Sequential()
model.add(Dense(64, input_shape=(20,), activation='relu'))
model.add([1](0.5))

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

ADropout

BDense

CActivation

DFlatten

Attempts:

3 left

💡 Hint

Common Mistakes

Using Dense instead of Dropout for dropout layer.

Forgetting to import Dropout layer.

Dropout layer is added by calling Dropout with the dropout rate.

4fill in blank

hard

Fill both blanks to create a sequential model with two dense layers and compile it with Adam optimizer and categorical crossentropy loss.

TensorFlow

model = Sequential()
model.add(Dense([1], activation='relu', input_shape=(15,)))
model.add(Dense([2], activation='softmax'))
model.compile(optimizer='[3]', loss='categorical_crossentropy')

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

A64

B10

C32

Dadam

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong number of units in output layer.

Using wrong optimizer name.

The first dense layer has 64 units with relu activation. The second dense layer has 10 units for 10 classes with softmax activation. Adam optimizer is used for compiling.

5fill in blank

hard

Fill all three blanks to create a sequential model with input shape, two dense layers, and compile it with RMSprop optimizer and binary crossentropy loss.

TensorFlow

model = Sequential()
model.add(Dense([1], activation='relu', input_shape=([2],)))
model.add(Dense([3], activation='sigmoid'))
model.compile(optimizer='rmsprop', loss='binary_crossentropy')

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

A128

B20

C1

D64

Attempts:

3 left

💡 Hint

Common Mistakes

Using more than 1 unit in output layer for binary classification.

Wrong input shape format.

The first dense layer has 128 units with relu activation and input shape of 20 features. The output layer has 1 unit with sigmoid activation for binary classification.

Practice

1. What is the main purpose of the Sequential model API in TensorFlow?

easy

A. To visualize the training process of a model

B. To create complex models with multiple inputs and outputs

C. To perform data preprocessing before training

D. To build a model by stacking layers in a linear order

Solution

1. Step 1: Understand the Sequential API purpose

   The Sequential API is designed to build models by stacking layers one after another in a simple linear fashion.

2. Step 2: Compare options with the API's function

   Options B, C, and D describe other functionalities not related to the Sequential API's main purpose.

3. Final Answer:

   To build a model by stacking layers in a linear order -> Option D

4. Quick Check:

   Sequential API = linear stacking of layers [OK]

Hint: Sequential means layers stacked one after another [OK]

Common Mistakes:

• Confusing Sequential with Functional API for complex models
• Thinking Sequential handles data preprocessing
• Assuming Sequential is for visualization

2. Which of the following is the correct way to create a Sequential model with one dense layer of 10 units in TensorFlow?

easy

A. model = Sequential(Dense(10))

B. model = Sequential([Dense(10)])

C. model = Sequential().add(Dense(10))

D. model = Sequential.add(Dense(10))

Solution

1. Step 1: Recall correct Sequential model creation syntax

   The Sequential model can be created by passing a list of layers inside the constructor, e.g., Sequential([Dense(10)]).

2. Step 2: Check each option's syntax validity

   model = Sequential([Dense(10)]) uses the correct list syntax. model = Sequential(Dense(10)) misses the list brackets. model = Sequential().add(Dense(10)) is valid usage but requires assignment to a variable to keep the model reference. model = Sequential.add(Dense(10)) incorrectly calls add() on the class, not an instance.

3. Final Answer:

   model = Sequential([Dense(10)]) -> Option B

4. Quick Check:

   Sequential needs list of layers in constructor [OK]

Hint: Pass layers as a list inside Sequential() [OK]

Common Mistakes:

• Omitting brackets around layers list
• Calling add() on class instead of instance
• Chaining add() without assignment

3. What will be the output shape of the model after running this code?

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

model = Sequential([
    Dense(5, input_shape=(10,)),
    Dense(3)
])
print(model.output_shape)

medium

A. (None, 5)

B. (10, 3)

C. (None, 3)

D. (5, 3)

Solution

1. Step 1: Understand input and output shapes in Sequential

   The input shape is (10,), so the first Dense layer outputs (None, 5). The second Dense layer outputs (None, 3) because it has 3 units.

2. Step 2: Identify final output shape

   The model's output shape is the output of the last layer, which is (None, 3). None means batch size is flexible.

3. Final Answer:

   (None, 3) -> Option C

4. Quick Check:

   Last Dense units = output shape [OK]

Hint: Output shape matches last layer units with batch None [OK]

Common Mistakes:

• Confusing input shape with output shape
• Using batch size instead of None
• Mixing layer output shapes

4. Identify the error in this Sequential model code:

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

model = Sequential()
model.add(Dense(10, input_shape=(5,)))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')
model.fit(x_train, y_train, epochs=5)

medium

A. x_train and y_train are not defined

B. Sequential model cannot use add() method

C. Loss function 'mse' is invalid

D. Missing import for optimizer

Solution

1. Step 1: Check code for missing definitions

   The code uses x_train and y_train in model.fit() but they are not defined anywhere, causing a runtime error.

2. Step 2: Verify other parts

   Optimizer 'adam' and loss 'mse' are valid strings. add() method is valid for Sequential instances. Imports are sufficient.

3. Final Answer:

   x_train and y_train are not defined -> Option A

4. Quick Check:

   Undefined training data causes error [OK]

Hint: Check if training data variables are defined before fit() [OK]

Common Mistakes:

• Assuming loss 'mse' is invalid
• Thinking add() method is not allowed
• Ignoring missing data variables

5. You want to build a Sequential model for a classification task with 3 classes. Which of the following is the best final layer and loss combination?

hard

A. Dense(3, activation='softmax') with loss='categorical_crossentropy'

B. Dense(1, activation='sigmoid') with loss='mean_squared_error'

C. Dense(3, activation='relu') with loss='binary_crossentropy'

D. Dense(3) with loss='sparse_categorical_crossentropy'

Solution

1. Step 1: Understand classification output requirements

   For 3 classes, the final layer should have 3 units with softmax activation to output class probabilities.

2. Step 2: Match appropriate loss function

   For one-hot encoded labels, 'categorical_crossentropy' is the correct loss function to use with softmax output.

3. Final Answer:

   Dense(3, activation='softmax') with loss='categorical_crossentropy' -> Option A

4. Quick Check:

   Softmax + categorical_crossentropy = multi-class classification [OK]

Hint: Use softmax + categorical_crossentropy for multi-class tasks [OK]

Common Mistakes:

• Using sigmoid for multi-class output
• Using mean squared error for classification
• Missing activation in final layer