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Building CNN using Tensorflow and Pytorch
Building CNN using Tensorflow and Pytorch Quiz
Question 1
What is the role of the ReLU activation function in a CNN?
It introduces non-linearity into the network
It reduces the number of layers in the CNN
It normalizes the input data
It converts the image into grayscale
Question 2
In a CNN, what is the purpose of pooling layers?
To increase the number of learnable parameters
To add more convolutional layers
To reduce the spatial dimensions of feature maps
To directly classify the images
Question 3
Which of the following statements about fully connected layers in a CNN is true?
They are only used in the middle layers of the network
They help convert extracted features into final predictions
They apply convolution operations on input images
They are used only in the first layer of a CNN
Question 4
In PyTorch, which module is used to define a convolutional layer?
torch.nn.Linear
torch.nn.Conv2d
torch.nn.ReLU
torch.nn.MaxPool2d
Question 5
What is the role of the torch.nn.CrossEntropyLoss() function in PyTorch CNN models?
It is used for binary classification problems
It applies pooling operations to the output
It initializes the CNN model
It computes the loss for multi-class classification tasks
Question 6
In TensorFlow what is the purpose of the Flatten layer in a CNN?
To reduce the number of layers in the network
To perform max pooling on the feature maps
To convert the 2D feature maps into a 1D vector
To apply activation functions to the output
There are 6 questions to complete.
