Axon
Axon v0.3.1

View Source Axon.Activations (Axon v0.3.1)

Activation functions.

Activation functions are element-wise, (typically) non-linear functions called on the output of another layer, such as a dense layer:

x
|> dense(weight, bias)
|> relu()

Activation functions output the "activation" or how active a given layer's neurons are in learning a representation of the data-generating distribution.

Some activations are commonly used as output activations. For example softmax is often used as the output in multiclass classification problems because it returns a categorical probability distribution:

iex> Axon.Activations.softmax(Nx.tensor([[1, 2, 3]], type: {:f, 32}))
#Nx.Tensor<
  f32[1][3]
  [
    [0.09003057330846786, 0.2447284758090973, 0.6652409434318542]
  ]
>

Other activations such as tanh or sigmoid are used because they have desirable properties, such as keeping the output tensor constrained within a certain range.

Generally, the choice of activation function is arbitrary; although some activations work better than others in certain problem domains. For example ReLU (rectified linear unit) activation is a widely-accepted default. You can see a list of activation functions and implementations here.

All of the functions in this module are implemented as numerical functions and can be JIT or AOT compiled with any supported Nx compiler.

Link to this section Summary

Functions

celu(x, opts \\ [])

Continuously-differentiable exponential linear unit activation.

elu(x, opts \\ [])

Exponential linear unit activation.

exp(x)

Exponential activation.

gelu(x)

Gaussian error linear unit activation.

hard_sigmoid(x, opts \\ [])

Hard sigmoid activation.

hard_silu(x, opts \\ [])

Hard sigmoid weighted linear unit activation.

hard_tanh(x)

Hard hyperbolic tangent activation.

leaky_relu(x, opts \\ [])

Leaky rectified linear unit activation.

linear(x)

Linear activation.

log_sigmoid(x)

Log-sigmoid activation.

log_softmax(x, opts \\ [])

Log-softmax activation.

log_sumexp(x, opts \\ [])

Logsumexp activation.

mish(x)

Mish activation.

relu6(x)

Rectified linear unit 6 activation.

relu(x)

Rectified linear unit activation.

selu(x, opts \\ [])

Scaled exponential linear unit activation.

sigmoid(x)

Sigmoid activation.

silu(x)

Sigmoid weighted linear unit activation.

softmax(x, opts \\ [])

Softmax activation along an axis.

softplus(x)

Softplus activation.

softsign(x)

Softsign activation.

tanh(x)

Hyperbolic tangent activation.

Link to this section Functions

Link to this function

celu(x, opts \\ [])

View Source

Continuously-differentiable exponential linear unit activation.

$$f(x_i) = \max(0, x_i) + \min(0, \alpha * e^{\frac{x_i}{\alpha}} - 1)$$

options

 Options

  • alpha - $\alpha$ in CELU formulation. Must be non-zero. Defaults to 1.0

examples

 Examples 

iex> Axon.Activations.celu(Nx.tensor([-3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0]))
#Nx.Tensor<
  f32[7]
  [-0.9502129554748535, -0.8646647334098816, -0.6321205496788025, 0.0, 1.0, 2.0, 3.0]
>

iex> Axon.Activations.celu(Nx.tensor([[-1.0, -2.0, -3.0], [1.0, 2.0, 3.0]], type: {:bf, 16}))
#Nx.Tensor<
  bf16[2][3]
  [
    [-0.62890625, -0.86328125, -0.94921875],
    [1.0, 2.0, 3.0]
  ]
>

error-cases

 Error cases 

iex> Axon.Activations.celu(Nx.tensor([0.0, 1.0, 2.0], type: {:f, 32}), alpha: 0.0)
** (ArgumentError) :alpha must be non-zero in CELU activation

references

 References

Link to this function

elu(x, opts \\ [])

View Source

Exponential linear unit activation.

Equivalent to celu for $\alpha = 1$

$$f(x_i) = \begin{cases}x_i & x _i > 0 \newline \alpha * (e^{x_i} - 1) & x_i \leq 0 \ \end{cases}$$

options

 Options

  • alpha - $\alpha$ in ELU formulation. Defaults to 1.0

examples

 Examples 

iex> Axon.Activations.elu(Nx.tensor([-3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0]))
#Nx.Tensor<
  f32[7]
  [-0.9502129554748535, -0.8646647334098816, -0.6321205496788025, 0.0, 1.0, 2.0, 3.0]
>

iex> Axon.Activations.elu(Nx.tensor([[-1.0, -2.0, -3.0], [1.0, 2.0, 3.0]], type: {:bf, 16}))
#Nx.Tensor<
  bf16[2][3]
  [
    [-0.62890625, -0.86328125, -0.94921875],
    [1.0, 2.0, 3.0]
  ]
>

references

 References

Link to this function

exp(x)

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Exponential activation.

$$f(x_i) = e^{x_i}$$

examples

 Examples 

iex> Axon.Activations.exp(Nx.tensor([-3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0], names: [:data]))
#Nx.Tensor<
  f32[data: 7]
  [0.049787066876888275, 0.1353352814912796, 0.3678794503211975, 1.0, 2.7182817459106445, 7.389056205749512, 20.08553695678711]
>

iex> Axon.Activations.exp(Nx.tensor([[-1.0, -2.0, -3.0], [1.0, 2.0, 3.0]], type: {:bf, 16}, names: [:batch, :data]))
#Nx.Tensor<
  bf16[batch: 2][data: 3]
  [
    [0.3671875, 0.134765625, 0.049560546875],
    [2.703125, 7.375, 20.0]
  ]
>
Link to this function

gelu(x)

View Source

Gaussian error linear unit activation.

$$f(x_i) = \frac{x_i}{2}(1 + {erf}(\frac{x_i}{\sqrt{2}}))$$

examples

 Examples 

iex> Axon.Activations.gelu(Nx.tensor([-3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0], names: [:data]))
#Nx.Tensor<
  f32[data: 7]
  [-0.0040496885776519775, -0.04550027847290039, -0.15865525603294373, 0.0, 0.8413447141647339, 1.9544997215270996, 2.995950222015381]
>

iex> Axon.Activations.gelu(Nx.tensor([[-1.0, -2.0, -3.0], [1.0, 2.0, 3.0]], type: {:bf, 16}, names: [:batch, :data]))
#Nx.Tensor<
  bf16[batch: 2][data: 3]
  [
    [-0.16015625, -0.046875, -0.005859375],
    [0.83984375, 1.953125, 2.984375]
  ]
>

references

 References

Link to this function

hard_sigmoid(x, opts \\ [])

View Source

Hard sigmoid activation.

examples

 Examples 

iex> Axon.Activations.hard_sigmoid(Nx.tensor([-3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0], names: [:data]))
#Nx.Tensor<
  f32[data: 7]
  [0.0, 0.0, 0.0, 0.20000000298023224, 0.4000000059604645, 0.6000000238418579, 0.800000011920929]
>

iex> Axon.Activations.hard_sigmoid(Nx.tensor([[-1.0, -2.0, -3.0], [1.0, 2.0, 3.0]], type: {:bf, 16}, names: [:batch, :data]))
#Nx.Tensor<
  bf16[batch: 2][data: 3]
  [
    [7.781982421875e-4, 0.0, 0.0],
    [0.3984375, 0.59765625, 0.796875]
  ]
>
Link to this function

hard_silu(x, opts \\ [])

View Source

Hard sigmoid weighted linear unit activation.

$$f(x_i) = \begin{cases} 0 & x_i \leq -3 \newline x & x_i \geq 3 \newline \frac{x_i^2}{6} + \frac{x_i}{2} & otherwise \end{cases}$$

examples

 Examples 

iex> Axon.Activations.hard_silu(Nx.tensor([-3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0], names: [:data]))
#Nx.Tensor<
  f32[data: 7]
  [-0.0, -0.0, -0.0, 0.0, 0.4000000059604645, 1.2000000476837158, 2.4000000953674316]
>

iex> Axon.Activations.hard_silu(Nx.tensor([[-1.0, -2.0, -3.0], [1.0, 2.0, 3.0]], type: {:bf, 16}, names: [:batch, :data]))
#Nx.Tensor<
  bf16[batch: 2][data: 3]
  [
    [-7.781982421875e-4, -0.0, -0.0],
    [0.3984375, 1.1953125, 2.390625]
  ]
>
Link to this function

hard_tanh(x)

View Source

Hard hyperbolic tangent activation.

$$f(x_i) = \begin{cases} 1 & x > 1 \newline -1 & x < -1 \newline x & otherwise \end{cases}$$

examples

 Examples 

iex> Axon.Activations.hard_tanh(Nx.tensor([-3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0], names: [:data]))
#Nx.Tensor<
  f32[data: 7]
  [-1.0, -1.0, -1.0, 0.0, 1.0, 1.0, 1.0]
>

iex> Axon.Activations.hard_tanh(Nx.tensor([[-1.0, -2.0, -3.0], [1.0, 2.0, 3.0]], type: {:bf, 16}, names: [:batch, :data]))
#Nx.Tensor<
  bf16[batch: 2][data: 3]
  [
    [-1.0, -1.0, -1.0],
    [1.0, 1.0, 1.0]
  ]
>
Link to this function

leaky_relu(x, opts \\ [])

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Leaky rectified linear unit activation.

$$f(x_i) = \begin{cases} x & x \geq 0 \newline \alpha * x & otherwise \end{cases}$$

options

 Options

  • :alpha - $\alpha$ in Leaky ReLU formulation. Defaults to 1.0e-2

examples

 Examples 

iex> Axon.Activations.leaky_relu(Nx.tensor([-3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0], names: [:data]), alpha: 0.5)
#Nx.Tensor<
  f32[data: 7]
  [-1.5, -1.0, -0.5, 0.0, 1.0, 2.0, 3.0]
>

iex> Axon.Activations.leaky_relu(Nx.tensor([[-1.0, -2.0, -3.0], [1.0, 2.0, 3.0]], names: [:batch, :data]), alpha: 0.5)
#Nx.Tensor<
  f32[batch: 2][data: 3]
  [
    [-0.5, -1.0, -1.5],
    [1.0, 2.0, 3.0]
  ]
>
Link to this function

linear(x)

View Source

Linear activation.

$$f(x_i) = x_i$$

examples

 Examples 

iex> Axon.Activations.linear(Nx.tensor([-3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0], names: [:data]))
#Nx.Tensor<
  f32[data: 7]
  [-3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0]
>

iex> Axon.Activations.linear(Nx.tensor([[-1.0, -2.0, -3.0], [1.0, 2.0, 3.0]], type: {:bf, 16}, names: [:batch, :data]))
#Nx.Tensor<
  bf16[batch: 2][data: 3]
  [
    [-1.0, -2.0, -3.0],
    [1.0, 2.0, 3.0]
  ]
>
Link to this function

log_sigmoid(x)

View Source

Log-sigmoid activation.

$$f(x_i) = \log(\sigmoid(x))$$

examples

 Examples 

iex> Axon.Activations.log_sigmoid(Nx.tensor([-3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0], type: {:f, 32}, names: [:data]))
#Nx.Tensor<
  f32[data: 7]
  [-3.0485873222351074, -2.1269280910491943, -1.3132617473602295, -0.6931471824645996, -0.3132616877555847, -0.12692801654338837, -0.04858734831213951]
>

iex> Axon.Activations.log_sigmoid(Nx.tensor([[-1.0, -2.0, -3.0], [1.0, 2.0, 3.0]], type: {:bf, 16}, names: [:batch, :data]))
#Nx.Tensor<
  bf16[batch: 2][data: 3]
  [
    [-1.3125, -2.125, -3.046875],
    [-0.3125, -0.1259765625, -0.04833984375]
  ]
>
Link to this function

log_softmax(x, opts \\ [])

View Source

Log-softmax activation.

$$f(x_i) = -log( um{e^x_i})$$

examples

 Examples 

iex> Axon.Activations.log_softmax(Nx.tensor([-3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0], type: {:f, 32}, names: [:data]))
#Nx.Tensor<
  f32[data: 7]
  [-6.457762718200684, -5.457762718200684, -4.457762718200684, -3.4577627182006836, -2.4577627182006836, -1.4577628374099731, -0.45776283740997314]
>

iex> Axon.Activations.log_softmax(Nx.tensor([[-1.0, -2.0, -3.0], [1.0, 2.0, 3.0]], type: {:bf, 16}, names: [:batch, :data]))
#Nx.Tensor<
  bf16[batch: 2][data: 3]
  [
    [-0.404296875, -1.3984375, -2.390625],
    [-2.390625, -1.3984375, -0.404296875]
  ]
>
Link to this function

log_sumexp(x, opts \\ [])

View Source

Logsumexp activation.

$$\log(sum e^x_i)$$

examples

 Examples 

iex> Axon.Activations.log_sumexp(Nx.tensor([-3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0], names: [:data]))
#Nx.Tensor<
  f32[data: 1]
  [0.45776283740997314]
>

iex> Axon.Activations.log_sumexp(Nx.tensor([[-1.0, -2.0, -3.0], [1.0, 2.0, 3.0]], type: {:bf, 16}, names: [:batch, :data]))
#Nx.Tensor<
  bf16[batch: 2][data: 1]
  [
    [0.404296875],
    [0.404296875]
  ]
>
Link to this function

mish(x)

View Source

Mish activation.

$$f(x_i) = x_i* \tanh(\log(1 + e^x_i))$$

examples

 Examples 

iex> Axon.Activations.mish(Nx.tensor([-3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0], type: {:f, 32}, names: [:data]))
#Nx.Tensor<
  f32[data: 7]
  [-0.14564745128154755, -0.2525014877319336, -0.30340147018432617, 0.0, 0.8650984168052673, 1.9439589977264404, 2.98653507232666]
>

iex> Axon.Activations.mish(Nx.tensor([[-1.0, -2.0, -3.0], [1.0, 2.0, 3.0]], type: {:bf, 16}, names: [:batch, :data]))
#Nx.Tensor<
  bf16[batch: 2][data: 3]
  [
    [-0.30078125, -0.25, -0.1435546875],
    [0.86328125, 1.9375, 2.96875]
  ]
>
Link to this function

relu6(x)

View Source

Rectified linear unit 6 activation.

$$f(x_i) = \min_i(\max_i(x, 0), 6)$$

examples

 Examples 

iex> Axon.Activations.relu6(Nx.tensor([-3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0]))
#Nx.Tensor<
  f32[7]
  [0.0, 0.0, 0.0, 0.0, 1.0, 2.0, 3.0]
>

iex> Axon.Activations.relu6(Nx.tensor([[-1.0, -2.0, -3.0], [1.0, 2.0, 3.0]], type: {:bf, 16}, names: [:batch, :data]))
#Nx.Tensor<
  bf16[batch: 2][data: 3]
  [
    [0.0, 0.0, 0.0],
    [1.0, 2.0, 3.0]
  ]
>

references

 References

Link to this function

relu(x)

View Source

Rectified linear unit activation.

$$f(x_i) = \max_i(x, 0)$$

examples

 Examples 

iex> Axon.Activations.relu(Nx.tensor([-3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0], names: [:data]))
#Nx.Tensor<
  f32[data: 7]
  [0.0, 0.0, 0.0, 0.0, 1.0, 2.0, 3.0]
>

iex> Axon.Activations.relu(Nx.tensor([[-1.0, -2.0, -3.0], [1.0, 2.0, 3.0]], type: {:bf, 16}, names: [:batch, :data]))
#Nx.Tensor<
  bf16[batch: 2][data: 3]
  [
    [0.0, 0.0, 0.0],
    [1.0, 2.0, 3.0]
  ]
>
Link to this function

selu(x, opts \\ [])

View Source

Scaled exponential linear unit activation.

$$f(x_i) = \begin{cases} \lambda x & x \geq 0 \newline \lambda \alpha(e^{x} - 1) & x < 0 \end{cases}$$

$$\alpha \approx 1.6733$$ $$\lambda \approx 1.0507$$

examples

 Examples 

iex> Axon.Activations.selu(Nx.tensor([-3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0], names: [:data]))
#Nx.Tensor<
  f32[data: 7]
  [-1.670568823814392, -1.5201665163040161, -1.1113307476043701, 0.0, 1.0507010221481323, 2.1014020442962646, 3.1521029472351074]
>

iex> Axon.Activations.selu(Nx.tensor([[-1.0, -2.0, -3.0], [1.0, 2.0, 3.0]], type: {:bf, 16}, names: [:batch, :data]))
#Nx.Tensor<
  bf16[batch: 2][data: 3]
  [
    [-1.09375, -1.5078125, -1.6640625],
    [1.046875, 2.09375, 3.140625]
  ]
>

references

 References

Link to this function

sigmoid(x)

View Source

Sigmoid activation.

$$f(x_i) = \frac{1}{1 + e^{-x_i}}$$

Implementation Note: Sigmoid logits are cached as metadata in the expression and can be used in calculations later on. For example, they are used in cross-entropy calculations for better stability.

examples

 Examples 

iex> Axon.Activations.sigmoid(Nx.tensor([-3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0], names: [:data]))
#Nx.Tensor<
  f32[data: 7]
  [0.04742587357759476, 0.11920291930437088, 0.2689414322376251, 0.5, 0.7310585975646973, 0.8807970881462097, 0.9525741338729858]
>

iex> Axon.Activations.sigmoid(Nx.tensor([[-1.0, -2.0, -3.0], [1.0, 2.0, 3.0]], type: {:bf, 16}, names: [:batch, :data]))
#Nx.Tensor<
  bf16[batch: 2][data: 3]
  [
    [0.267578125, 0.119140625, 0.04736328125],
    [0.73046875, 0.87890625, 0.94921875]
  ]
>
Link to this function

silu(x)

View Source

Sigmoid weighted linear unit activation.

$$f(x_i) = x\sigmoid(x)$$

examples

 Examples 

iex> Axon.Activations.silu(Nx.tensor([-3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0], names: [:data]))
#Nx.Tensor<
  f32[data: 7]
  [-0.14227762818336487, -0.23840583860874176, -0.2689414322376251, 0.0, 0.7310585975646973, 1.7615941762924194, 2.857722282409668]
>

iex> Axon.Activations.silu(Nx.tensor([[-1.0, -2.0, -3.0], [1.0, 2.0, 3.0]], type: {:bf, 16}, names: [:batch, :data]))
#Nx.Tensor<
  bf16[batch: 2][data: 3]
  [
    [-0.267578125, -0.23828125, -0.1416015625],
    [0.73046875, 1.7578125, 2.84375]
  ]
>

references

 References

Link to this function

softmax(x, opts \\ [])

View Source

Softmax activation along an axis.

$$\frac{e^{x_i}}{\sum_i e^{x_i}}$$

Implementation Note: Softmax logits are cached as metadata in the expression and can be used in calculations later on. For example, they are used in cross-entropy calculations for better stability.

options

 Options

  • :axis - softmax axis along which to calculate distribution. Defaults to 1.

examples

 Examples 

iex> Axon.Activations.softmax(Nx.tensor([[-3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0]], names: [:batch, :data]))
#Nx.Tensor<
  f32[batch: 1][data: 7]
  [
    [0.0015683004166930914, 0.004263082519173622, 0.011588259600102901, 0.03150015324354172, 0.08562629669904709, 0.23275642096996307, 0.6326975226402283]
  ]
>

iex> Axon.Activations.softmax(Nx.tensor([[-1.0, -2.0, -3.0], [1.0, 2.0, 3.0]], type: {:bf, 16}, names: [:batch, :data]))
#Nx.Tensor<
  bf16[batch: 2][data: 3]
  [
    [0.6640625, 0.2431640625, 0.08935546875],
    [0.08935546875, 0.2431640625, 0.6640625]
  ]
>
Link to this function

softplus(x)

View Source

Softplus activation.

$$\log(1 + e^x_i)$$

examples

 Examples 

iex> Axon.Activations.softplus(Nx.tensor([-3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0], names: [:data]))
#Nx.Tensor<
  f32[data: 7]
  [0.04858734831213951, 0.12692801654338837, 0.3132616877555847, 0.6931471824645996, 1.3132617473602295, 2.1269280910491943, 3.0485873222351074]
>

iex> Axon.Activations.softplus(Nx.tensor([[-1.0, -2.0, -3.0], [1.0, 2.0, 3.0]], type: {:bf, 16}, names: [:batch, :data]))
#Nx.Tensor<
  bf16[batch: 2][data: 3]
  [
    [0.3125, 0.1259765625, 0.04833984375],
    [1.3125, 2.125, 3.046875]
  ]
>
Link to this function

softsign(x)

View Source

Softsign activation.

$$f(x_i) = \frac{x_i}{|x_i| + 1}$$

examples

 Examples 

iex> Axon.Activations.softsign(Nx.tensor([-3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0], names: [:data]))
#Nx.Tensor<
  f32[data: 7]
  [-0.75, -0.6666666865348816, -0.5, 0.0, 0.5, 0.6666666865348816, 0.75]
>

iex> Axon.Activations.softsign(Nx.tensor([[-1.0, -2.0, -3.0], [1.0, 2.0, 3.0]], type: {:bf, 16}, names: [:batch, :data]))
#Nx.Tensor<
  bf16[batch: 2][data: 3]
  [
    [-0.5, -0.6640625, -0.75],
    [0.5, 0.6640625, 0.75]
  ]
>
Link to this function

tanh(x)

View Source

Hyperbolic tangent activation.

$$f(x_i) = \tanh(x_i)$$

examples

 Examples 

iex> Axon.Activations.tanh(Nx.tensor([-3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0], names: [:data]))
#Nx.Tensor<
  f32[data: 7]
  [-0.9950547814369202, -0.9640275835990906, -0.7615941762924194, 0.0, 0.7615941762924194, 0.9640275835990906, 0.9950547814369202]
>

iex> Axon.Activations.tanh(Nx.tensor([[-1.0, -2.0, -3.0], [1.0, 2.0, 3.0]], type: {:bf, 16}, names: [:batch, :data]))
#Nx.Tensor<
  bf16[batch: 2][data: 3]
  [
    [-0.7578125, -0.9609375, -0.9921875],
    [0.7578125, 0.9609375, 0.9921875]
  ]
>