Defining a Model in TensorFlow QUIZ (MCQ QUESTIONS AND ANSWERS)

Sigmoid

Linear

ReLU (Rectified Linear Unit)

Tanh (Hyperbolic Tangent)

L1 regularization

L2 regularization

Dropout

Batch normalization

Stopping the training process when the model achieves 100% accuracy on the training data

Stopping the training process when the model starts overfitting on the validation data

Stopping the training process after a fixed number of epochs

None of the above

Learning rate scheduling

Early stopping

Dropout

Batch normalization

They require less computational resources for training

They offer better generalization on new tasks with limited data

They eliminate the need for fine-tuning

None of the above

Convolutional layers

Recurrent layers

Dense (fully connected) layers

Pooling layers

To reduce the dimensionality of the feature maps

To introduce non-linearity

To prevent overfitting

None of the above

Dense (fully connected)

Convolutional

Recurrent

Pooling

By defining the objective the network aims to minimize

By determining the architecture of the network

By initializing the model parameters

None of the above

A single pass through the entire training dataset

The number of layers in the neural network

The size of the mini-batch used during training

None of the above

L1 regularization

L2 regularization

Dropout

Batch normalization

The rate at which the model learns from the training data

The step size used for updating model parameters during optimization

The number of epochs required for convergence

None of the above

SGD (Stochastic Gradient Descent)

Adam

RMSprop

All of the above

To normalize the input data before feeding it to the network

To speed up convergence during training

To reduce internal covariate shift and improve stability

None of the above

By increasing the model complexity

By adding more layers to the network

By using regularization techniques

None of the above

Simplicity

Flexibility

Speed

None of the above

To introduce non-linearity

To increase model complexity

To speed up convergence during training

None of the above

Model parameters are fixed throughout the training process.

Model parameters are randomly initialized before training.

Model parameters are learned through the optimization process.

None of the above

By providing real-time visualization of metrics during training

By offering tools for debugging model architectures

Both A and B

None of the above

Sequential

Functional API

Both A and B

None of the above

Individual units in a neural network

Functional blocks performing specific computations

Both A and B

None of the above

Using the model.layers attribute

By calling model.get_weights() method

Both A and B

None of the above

Matplotlib

Seaborn

TensorBoard

Plotly

Information about the model architecture

Number of parameters in the model

Output shape of each layer

All of the above

Automatically by TensorFlow

Manually by the user

Both A and B

None of the above

Sequential

Functional API

Both A and B

None of the above

Sequential

Functional API

Both A and B

None of the above

Sequential

Functional API

Both A and B

None of the above

Sequential

Functional API

Both A and B

None of the above

Yes

No

Only in TensorFlow 2.x

Only in TensorFlow 1.x