Which function will produce the desired output?

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A Machine Learning Specialist is building a convolutional neural network (CNN) that will classify 10 types of animals.The Specialist has built a series of layers in a neural network that will take an input image of an animal, pass it through a series of convolutional and pooling layers, and then finally pass it through a dense and fully connected layer with 10 nodes.The Specialist would like to get an output from the neural network that is a probability distribution of how likely it is that the input image belongs to each of the 10 classes.

Which function will produce the desired output?

Dropout

Smooth L1 loss

Softmax

Rectified linear units (ReLU)

Explanations:

Dropout is a regularization technique used to prevent overfitting by randomly setting a fraction of input units to zero during training. It does not affect the output probabilities.

Smooth L1 loss is a loss function used in regression problems to reduce the impact of outliers. It is not used for generating probabilities in classification tasks.

Softmax is the appropriate activation function for multi-class classification. It converts the raw output values (logits) from the fully connected layer into a probability distribution over the classes.

ReLU (Rectified Linear Units) is an activation function used to introduce non-linearity in the hidden layers of a neural network. It is not used to produce probability distributions for classification.

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