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clj-synapses.fun
The activation functions a neuron can have.
They can be used in the arguments of neural network's creation.
(require '[clj-synapses.net :as net]
'[clj-synapses.fun :as fun])
(net/->net
[2 3 1]
(fn [_] fun/sigmoid)
(fn [_] (rand)))
(net/->net
[4 6 8 5 3]
(fn [_] fun/identity)
(fn [_] (rand)))
(net/->net
[4 8 3]
(fn [_] fun/leaky-re-lu)
(fn [_] (rand)))
(net/->net
[2 1]
(fn [_] fun/tanh)
(fn [_] (rand)))
The activation functions a neuron can have.
They can be used in the arguments of neural network's creation.
```clojure
(require '[clj-synapses.net :as net]
'[clj-synapses.fun :as fun])
```
```clojure
(net/->net
[2 3 1]
(fn [_] fun/sigmoid)
(fn [_] (rand)))
```
```clojure
(net/->net
[4 6 8 5 3]
(fn [_] fun/identity)
(fn [_] (rand)))
```
```clojure
(net/->net
[4 8 3]
(fn [_] fun/leaky-re-lu)
(fn [_] (rand)))
```
```clojure
(net/->net
[2 1]
(fn [_] fun/tanh)
(fn [_] (rand)))
```identityclj
Identity is a linear function where the output is equal to the input.
(fn [x] x)
Identity is a linear function where the output is equal to the input. ```clojure (fn [x] x) ```
leaky-re-luclj
Leaky-re-lu gives a small proportion of x if x is negative and x otherwise.
(fn [x]
(if (< x 0.0)
(* x 0.01)
x))
Leaky-re-lu gives a small proportion of x if x is negative and x otherwise.
```clojure
(fn [x]
(if (< x 0.0)
(* x 0.01)
x))
```sigmoidclj
Sigmoid takes any value as input and returns values in the range of 0.0 to 1.0.
(fn [x] (/ (inc (Math/exp (- x)))))
Sigmoid takes any value as input and returns values in the range of 0.0 to 1.0. ```clojure (fn [x] (/ (inc (Math/exp (- x))))) ```
tanhclj
Tanh is similar to Sigmoid, but outputs values in the range of -1.0 and 1.0.
(fn [x] (Math/tanh x))
Tanh is similar to Sigmoid, but outputs values in the range of -1.0 and 1.0. ```clojure (fn [x] (Math/tanh x)) ```
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