Sequential model API in TensorFlow - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of a simple Sequential model prediction

What is the output shape of the prediction from this Sequential model when given input shape (None, 10)?

TensorFlow

import tensorflow as tf
model = tf.keras.Sequential([
    tf.keras.layers.Dense(5, activation='relu', input_shape=(10,)),
    tf.keras.layers.Dense(3, activation='softmax')
])
import numpy as np
sample_input = np.random.random((1, 10))
prediction = model(sample_input)
prediction_shape = prediction.shape
print(prediction_shape)

A (1, 3)

B (10, 3)

C (1, 5)

D (3,)

💡 Hint

Remember the output shape depends on the last layer's units and batch size.

The model input shape is (None, 10), batch size 1 means input is (1,10). The last Dense layer has 3 units, so output shape is (1,3).

❓ Model Choice

intermediate

Choosing the correct Sequential model for binary classification

Which Sequential model architecture is best suited for a binary classification problem with input features of size 20?

A Sequential([Dense(1, activation='relu', input_shape=(20,)), Dense(1, activation='sigmoid')])

B Sequential([Dense(10, activation='relu', input_shape=(20,)), Dense(3, activation='softmax')])

C Sequential([Dense(10, activation='relu', input_shape=(20,)), Dense(1, activation='sigmoid')])

D Sequential([Dense(20, activation='relu'), Dense(1, activation='softmax')])

💡 Hint

Binary classification needs one output neuron with sigmoid activation.

For binary classification, the output layer should have 1 unit with sigmoid activation to output probabilities between 0 and 1.

❓ Hyperparameter

advanced

Effect of changing batch size in Sequential model training

What is the most likely effect of increasing the batch size from 16 to 256 during training of a Sequential model?

A Training will use less memory and always converge slower.

B Training will use less memory and produce more accurate models.

C Training will use more memory and always overfit the data.

D Training will use more memory and may converge faster but with less noisy gradient updates.

💡 Hint

Think about how batch size affects memory and gradient noise.

Larger batch sizes require more memory but produce smoother gradient estimates, which can speed up convergence but may reduce generalization.

❓ Metrics

advanced

Correct metric for multi-class classification with Sequential model

Which metric should be used to evaluate a Sequential model trained for 4-class classification?

A mean_absolute_error

B accuracy

C mean_squared_error

D binary_crossentropy

💡 Hint

Think about the type of problem and metric suitability.

Accuracy is the standard metric for multi-class classification to measure correct predictions ratio.

🔧 Debug

expert

Identify the error in this Sequential model code

What error will this code raise when run?

TensorFlow

import tensorflow as tf
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(10, activation='relu'))
model.add(tf.keras.layers.Dense(1, activation='sigmoid'))
model.compile(optimizer='adam', loss='binary_crossentropy')
import numpy as np
x = np.random.random((5, 20))
y = np.random.randint(0, 2, size=(5, 1))
model.fit(x, y, epochs=1)

A No error, code runs successfully

B TypeError: optimizer argument must be a string

C RuntimeError: model.fit requires validation data

D ValueError: Input shape is not defined for the first layer

💡 Hint

Keras Sequential models can infer input shape from training data.

Keras Sequential models infer the input shape automatically from the input data during the first forward pass in fit(), so no error is raised.

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