MLS-C01 · Question #49
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…
The correct answer is C. Softmax. The Softmax function is the standard output activation for multi-class classification. It takes a vector of raw scores (logits) from the final dense layer and normalizes them into a probability distribution: each output value is between 0 and 1, and all 10 values sum to exactly…
Question
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?
Options
- ADropout
- BSmooth L1 loss
- CSoftmax
- DRectified linear units (ReLU)
How the community answered
(28 responses)- A4% (1)
- B7% (2)
- C89% (25)
Explanation
The Softmax function is the standard output activation for multi-class classification. It takes a vector of raw scores (logits) from the final dense layer and normalizes them into a probability distribution: each output value is between 0 and 1, and all 10 values sum to exactly 1.0. This directly answers 'how likely is the image to belong to each of the 10 classes.' Dropout is a regularization technique that randomly zeros out neurons during training - it is not an output activation. Smooth L1 loss is a loss/cost function used during training (common in object detection), not an activation function. ReLU (Rectified Linear Unit) is used in hidden layers to introduce non-linearity; it does not produce a probability distribution and its outputs can be any non-negative value.
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