# "Boolean"(Net Decoder) NetDecoder["Boolean"]

represents a decoder that converts a probability p to False if p<0.5, and True otherwise.

# Details • NetDecoder[][input] applies the decoder to an input to produce an output.
• NetDecoder[][{input1,input2,}] applies the decoder to a list of inputs to produce a list of outputs.
• The input to the decoder input is a scalar in the range 0input1.
• NetDecoder[{"Boolean","InputDepth"->n}] can be used to specify that the input array has depth n. The default depth is 0, indicating that the input is a single real number.
• A decoder can be attached to an output port of a net by specifying "port"->NetDecoder[] when constructing the net.
• ##### Properties
• NetDecoder["Boolean"][data,prop] can be used to calculate a specific property for the input data.
• When a "Boolean" decoder is attached to a net, net[data,prop] or net[data,"oport"->prop] can be used to calculate a specific property of the decoded output.
• The "Boolean" decoder supports the following properties prop:
•  "Decision" the Boolean class False or True with the highest probability (default) "Probability" the probability p of class True "Entropy" the entropy of the probability distribution "RandomSample" sample a Boolean proportionally to its probability {"RandomSample","Temperature"t} sample using a positive temperature t None bypass decoding and return the input

# Examples

open allclose all

## Basic Examples(1)

Create a Boolean decoder:

Decode a probability as either True or False:

The decoder maps over a batch of examples:

The decoder expects probabilities as inputs and acts as Identity with the property "Probability":

Compute the entropy:

## Scope(2)

Attach a "Boolean" decoder to the output port of an ElementwiseLayer:

Apply the layer to an input:

Create a "Boolean" decoder that converts a matrix of probabilities into a matrix of Boolean values:

Attach the decoder to a net and apply it to an input:

Obtain the probability of the positive class:

Compute the entropy of each Bernoulli distribution: