# Linear

tflearn.activations.linear (x)

f(x) = x

### Arguments

• x : A `Tensor` with type `float`, `double`, `int32`, `int64`, `uint8`, `int16`, or `int8`.

### Returns

The incoming Tensor (without changes).

# Tanh

tflearn.activations.tanh (x)

Computes hyperbolic tangent of `x` element-wise.

### Arguments

• x: A Tensor with type `float`, `double`, `int32`, `complex64`, `int64`, or `qint32`.

### Returns

A Tensor with the same type as `x` if `x.dtype != qint32` otherwise the return type is `quint8`.

# Sigmoid

tflearn.activations.sigmoid (x)

Computes sigmoid of `x` element-wise. Specifically, `y = 1 / (1 + exp(-x))`.

### Arguments

• x: A Tensor with type `float`, `double`, `int32`, `complex64`, `int64`, or `qint32`.

### Returns

A Tensor with the same type as `x` if `x.dtype != qint32` otherwise the return type is `quint8`.

# Softmax

tflearn.activations.softmax (x)

Computes softmax activations.

For each batch `i` and class `j` we have

softmax[i, j] = exp(logits[i, j]) / sum(exp(logits[i]))

### Arguments

• x: A `Tensor`. Must be one of the following types: `float32`, `float64`. 2-D with shape `[batch_size, num_classes]`.

### Returns

A `Tensor`. Has the same type as `x`. Same shape as `x`.

# Softplus

tflearn.activations.softplus (x)

Computes softplus: `log(exp(features) + 1)`.

### Arguments

• x: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `uint8`, `int16`, `int8`, `uint16`.

### Returns

A `Tensor`. Has the same type as `x`.

# Softsign

tflearn.activations.softsign (x)

Computes softsign: `features / (abs(features) + 1)`.

### Arguments

• x: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `uint8`, `int16`, `int8`, `uint16`.

### Returns

A `Tensor`. Has the same type as `x`.

# ReLU

tflearn.activations.relu (x)

Computes rectified linear: `max(features, 0)`.

### Arguments

• x: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `uint8`, `int16`, `int8`, `uint16`.

### Returns

A `Tensor`. Has the same type as `x`.

# ReLU6

tflearn.activations.relu6 (x)

Computes Rectified Linear 6: `min(max(features, 0), 6)`.

### Arguments

• x: A `Tensor` with type `float`, `double`, `int32`, `int64`, `uint8`, `int16`, or `int8`.

### Returns

A `Tensor` with the same type as `x`.

# LeakyReLU

tflearn.activations.leaky_relu (x, alpha=0.1, name='LeakyReLU')

Modified version of ReLU, introducing a nonzero gradient for negative input.

### Arguments

• x: A `Tensor` with type `float`, `double`, `int32`, `int64`, `uint8`, `int16`, or `int8`.
• alpha: `float`. slope.
• name: A name for this activation op (optional).

### Returns

A `Tensor` with the same type as `x`.

### References

Rectifier Nonlinearities Improve Neural Network Acoustic Models, Maas et al. (2013).

http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf

# PReLU

tflearn.activations.prelu (x, channel_shared=False, weights_init='zeros', trainable=True, restore=True, reuse=False, scope=None, name='PReLU')

Parametric Rectified Linear Unit.

### Arguments

• x: A `Tensor` with type `float`, `double`, `int32`, `int64`, `uint8`, `int16`, or `int8`.
• channel_shared: `bool`. Single weight is shared by all channels
• weights_init: `str`. Weights initialization. Default: zeros.
• trainable: `bool`. If True, weights will be trainable.
• restore: `bool`. Restore or not alphas.
• reuse: `bool`. If True and 'scope' is provided, this layer variables will be reused (shared).
• name: A name for this activation op (optional).

### Attributes

• scope: `str`. This op scope.
• alphas: `Variable`. PReLU alphas.

### Returns

A `Tensor` with the same type as `x`.

### References

Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. He et al., 2014.

http://arxiv.org/pdf/1502.01852v1.pdf

# ELU

tflearn.activations.elu (x)

Exponential Linear Unit.

### Arguments

• x : A `Tensor` with type `float`, `double`, `int32`, `int64`, `uint8`, `int16`, or `int8`

### Returns

A `tuple` of `tf.Tensor`. This layer inference, i.e. output Tensors at training and testing time.

### References

Fast and Accurate Deep Network Learning by Exponential Linear Units, Djork-Arné Clevert, Thomas Unterthiner, Sepp Hochreiter. 2015.

http://arxiv.org/abs/1511.07289

# CReLU

tflearn.activations.crelu (x)

Computes Concatenated ReLU.

Concatenates a ReLU which selects only the positive part of the activation with a ReLU which selects only the negative part of the activation. Note that as a result this non-linearity doubles the depth of the activations.

### Arguments

• x : A `Tensor` with type `float`, `double`, `int32`, `int64`, `uint8`, `int16`, or `int8`.

### Returns

A `Tensor` with the same type as `x`.

https://arxiv.org/abs/1603.05201

# SELU

tflearn.activations.selu (x)

Scaled Exponential Linear Unit.

Arguments x : A `Tensor` with type `float`, `double`, `int32`, `int64`, `uint8`, `int16`, or `int8`

### References

Self-Normalizing Neural Networks, Klambauer et al., 2017.