Activation Functions: Sigmoid, ReLU, Softmax Quiz Quiz

Explore key differences and practical roles of the sigmoid, ReLU, and softmax activation functions in neural networks. Perfect for learners aiming to deepen their understanding of how these functions impact model behavior, output ranges, and learning dynamics.

1. Sigmoid Output Range

   When applying the sigmoid activation function to an input value in a neural network, what is the possible range of its output?

   1. 0 to 1
   2. 0 to infinity
   3. -1 to 1
   4. -infinity to +infinity

   Explanation: The sigmoid activation function squashes input values to a range between 0 and 1, making it suitable for binary classification problems. The option '-1 to 1' refers to the tanh function, not sigmoid. '0 to infinity' and '-infinity to +infinity' are incorrect because sigmoid does not output unbounded values. Only the interval from 0 to 1 matches the actual output range.

2. ReLU and the Vanishing Gradient Problem

   Which advantage does the ReLU (Rectified Linear Unit) activation function have over the sigmoid when training deep neural networks?

   1. ReLU helps reduce the vanishing gradient problem
   2. ReLU is not differentiable at any point
   3. ReLU always produces values between 0 and 1
   4. ReLU outputs only negative values

   Explanation: ReLU's main advantage is mitigating the vanishing gradient problem, especially in deep networks, by allowing gradients to pass through unchanged for positive inputs. The option stating ReLU outputs only negative values is incorrect; it outputs zero or positive values. ReLU does not restrict outputs between 0 and 1—that's a property of sigmoid. Lastly, ReLU is differentiable everywhere except at zero, not nowhere.

3. Softmax Output Interpretation

   After applying the softmax activation to the output layer of a neural network for a three-class classification problem, what do the resulting outputs represent?

   1. Negative values for incorrect classes
   2. Random selection among classes
   3. Raw scores for each class
   4. Probabilities summing to one for each class

   Explanation: Softmax converts raw output scores into probabilities so that each value is between 0 and 1 and all values sum to one, making it useful for multi-class classification. Raw scores are provided before softmax, not after. Softmax does not deliver random selections nor assign negative values to classes—outputs are always non-negative probabilities.

4. Sigmoid vs. ReLU Saturation

   In which scenario is the sigmoid activation function more likely to suffer from saturation compared to the ReLU, particularly during backpropagation?

   1. When the output must be strictly greater than one
   2. When input values are always zero
   3. When input values are very high or very low
   4. When using it in shallow networks only

   Explanation: Sigmoid outputs values close to 0 or 1 for extreme inputs, causing very small gradients (saturation) and slowing learning during backpropagation. If inputs are always zero, sigmoid outputs 0.5, not causing saturation. The output cannot be strictly greater than one in sigmoid, and saturation is an issue in both shallow and deep networks, not exclusive to shallow ones.

5. Selecting Activation Functions

   Which activation function is generally most appropriate for the final layer in a neural network performing multi-class classification with mutually exclusive classes?

   1. ReLU
   2. Softmax
   3. Linear
   4. Sigmiod

   Explanation: Softmax is designed for multi-class classification, converting final layer outputs into class probabilities that sum to one. 'Sigmiod' is a misspelling and refers incorrectly to sigmoid, which is typically used for binary outputs. ReLU is commonly used in hidden layers, not output for classification. Linear activation does not produce probabilities and thus is less appropriate for this scenario.