If a neural network with a single hidden layer uses dropout with a rate of 0.5, what fraction of neurons are turned off during each training step?

Breakdown the Problem

1. Identify the key concept: The question involves understanding the dropout technique in neural networks.
2. Clarify the dropout rate: The given rate is 0.5.

Relevant Concepts

1. Dropout is a regularization technique used to prevent overfitting in neural networks.
2. The dropout rate signifies the proportion of neurons that will be randomly set to zero during training.

Analysis and Detail

1. When the dropout rate is 0.5, it means that during each training step, 50% of the neurons in the specified layer will be turned off (set to zero).
2. This is a stochastic process, and thus, the fractions may vary on every iteration, but on average, half the neurons are inactive during training at this rate.

Verify and Summarize

1. Since a dropout rate of 0.5 implies that for every neuron in the hidden layer, there is a 50% chance that it will not be used in that particular training step.
2. Therefore, the verification of this concept aligns with the definition of dropout.

Final Answer

In each training step, 50% (or 0.5) of the neurons in the hidden layer are turned off when using a dropout rate of 0.5. Thus, the fraction of neurons turned off is $\frac{1}{2}$.