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Activations#

Collection of Ivy neural network activations as stateful classes.

class ivy.stateful.activations.ELU(*args, **kwargs)[source]#

Bases: Module

__init__(alpha=1.0)[source]#

Apply the ELU activation function.

class ivy.stateful.activations.GEGLU(*args, **kwargs)[source]#

Bases: Module

__init__()[source]#

Apply the GEGLU activation function.

class ivy.stateful.activations.GELU(*, approximate=False, complex_mode='jax')[source]#

Bases: Module

__init__(*, approximate=False, complex_mode='jax')[source]#

Apply the GELU activation function.

Parameters:

  • approximate (bool, default: False) – whether to use the gelu approximation algorithm or exact formulation.

  • complex_mode (Literal['split', 'magnitude', 'jax'], default: 'jax') – Specifies how to handle complex input. See ivy.func_wrapper.handle_complex_input for more detail.

class ivy.stateful.activations.Hardswish(complex_mode='jax')[source]#

Bases: Module

__init__(complex_mode='jax')[source]#

Apply the HARDSWISH activation function.

Parameters:

complex_mode (Literal['split', 'magnitude', 'jax'], default: 'jax') –

Specifies how to handle complex input. See

ivy.func_wrapper.handle_complex_input for more detail.

class ivy.stateful.activations.LeakyReLU(alpha=0.2, complex_mode='jax')[source]#

Bases: Module

__init__(alpha=0.2, complex_mode='jax')[source]#

Apply the LEAKY RELU activation function.

Parameters:

  • alpha (float, default: 0.2) – Negative slope for ReLU.

  • complex_mode (Literal['split', 'magnitude', 'jax'], default: 'jax') – Specifies how to handle complex input. See ivy.func_wrapper.handle_complex_input for more detail.

class ivy.stateful.activations.LogSigmoid(complex_mode='jax')[source]#

Bases: Module

__init__(complex_mode='jax')[source]#

Apply the LogSigmoid activation function.

Parameter#

complex_mode

Specifies how to handle complex input. See ivy.func_wrapper.handle_complex_input for more detail.

class ivy.stateful.activations.LogSoftmax(axis=-1, complex_mode='jax')[source]#

Bases: Module

__init__(axis=-1, complex_mode='jax')[source]#

Apply the LOG SOFTMAX activation function.

Parameters:

  • axis (Optional[int], default: -1) – The dimension log_softmax would be performed on. The default is None

  • complex_mode (Literal['split', 'magnitude', 'jax'], default: 'jax') – optional specifier for how to handle complex data types. See ivy.func_wrapper.handle_complex_input for more detail.

class ivy.stateful.activations.Logit(*args, **kwargs)[source]#

Bases: Module

__init__(eps=None, complex_mode='jax')[source]#

Apply the LOGIT activation function.

Parameters:

  • eps (default: None) – The epsilon value for the logit formation. Default: None.

  • complex_mode (default: 'jax') – optional specifier for how to handle complex data types. See ivy.func_wrapper.handle_complex_input for more detail.

class ivy.stateful.activations.Mish(*args, **kwargs)[source]#

Bases: Module

__init__()[source]#

Apply the MISH activation function.

class ivy.stateful.activations.PReLU(*args, **kwargs)[source]#

Bases: Module

__init__(slope)[source]#

Apply the PRELU activation function.

class ivy.stateful.activations.ReLU(complex_mode='jax')[source]#

Bases: Module

__init__(complex_mode='jax')[source]#

Apply the RELU activation function.

Parameters:

complex_mode (Literal['split', 'magnitude', 'jax'], default: 'jax') –

Specifies how to handle complex input. See

ivy.func_wrapper.handle_complex_input for more detail.

class ivy.stateful.activations.ReLU6(complex_mode='jax')[source]#

Bases: Module

__init__(complex_mode='jax')[source]#

Apply the TANH activation function.

Parameters:

complex_mode (Literal['split', 'magnitude', 'jax'], default: 'jax') –

Specifies how to handle complex input. See

ivy.func_wrapper.handle_complex_input for more detail.

class ivy.stateful.activations.SeLU(*args, **kwargs)[source]#

Bases: Module

__init__()[source]#

Apply the SELU activation function.

class ivy.stateful.activations.SiLU(*args, **kwargs)[source]#

Bases: Module

__init__()[source]#

Apply the SiLU activation function.

class ivy.stateful.activations.Sigmoid(complex_mode='jax')[source]#

Bases: Module

__init__(complex_mode='jax')[source]#

Apply the SIGMOID activation function.

Parameter#

complex_mode

Specifies how to handle complex input. See ivy.func_wrapper.handle_complex_input for more detail.

class ivy.stateful.activations.Softmax(axis=-1, complex_mode='jax')[source]#

Bases: Module

__init__(axis=-1, complex_mode='jax')[source]#

Apply the SOFTMAX activation function.

Parameters:

  • axis (int, default: -1) – The axis which we apply softmax op on.

  • complex_mode (Literal['split', 'magnitude', 'jax'], default: 'jax') – Specifies how to handle complex input. See ivy.func_wrapper.handle_complex_input for more detail.

class ivy.stateful.activations.Softplus(*args, **kwargs)[source]#

Bases: Module

__init__(beta=1.0, threshold=None)[source]#

Apply the SOFTPLUS activation function.

class ivy.stateful.activations.Tanh(complex_mode='jax')[source]#

Bases: Module

__init__(complex_mode='jax')[source]#

Apply the TANH activation function.

Parameters:

complex_mode (Literal['split', 'magnitude', 'jax'], default: 'jax') –

Specifies how to handle complex input. See

ivy.func_wrapper.handle_complex_input for more detail.

This should have hopefully given you an overview of the activations submodule, if you have any questions, please feel free to reach out on our discord!

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