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Generative Adversarial Networks (GANs) in PyTorch
Generative Adversarial Networks (GANs) in PyTorch Quiz
Question 1
Which library in Python is commonly used for implementing GANs in PyTorch?
Keras
TensorFlow
NumPy
PyTorch
Question 2
What is the primary difference between a Conditional GAN (CGAN) and a regular GAN?
CGAN uses random noise as input while GAN uses real data
CGAN conditions the generator and discriminator on additional information
CGAN does not have a discriminator
CGAN uses convolutional layers only
Question 3
In a GAN implemented with PyTorch, what is the function of the forward() method?
To define the layers of the neural network
To specify how data should be passed through the network
To initialize the network weights
To calculate the loss function
Question 4
When training a GAN in PyTorch, which loss function is typically used for the discriminator?
Mean Squared Error (MSE)
Cross-Entropy Loss
Hinge Loss
Kullback-Leibler (KL) Divergence
Question 5
Which layer is typically used in the generator of a GAN when generating image data?
nn.Linear layer
nn.Conv2d layer
nn.LSTM layer
nn.Embedding layer
Question 6
In PyTorch, which function is used to update the model’s parameters during training?
model.optimizer.step()
optimizer.zero_grad()
optimizer.step()
model.update()
Question 7
How is the loss for the generator typically calculated during GAN training in PyTorch?
By calculating the mean squared error between the real and fake data
By using binary cross-entropy loss where the target for the fake data is real
By comparing the output of the generator with the output of the discriminator
By using a least-squares loss
Question 8
What is the primary goal of training a GAN?
To make the generator produce more varied data
To make the discriminator better at distinguishing real from fake data
To have the generator produce data that the discriminator can no longer distinguish from real data
To minimize the loss function
There are 8 questions to complete.
