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doc/fluid/api_guides/low_level/layers/activations_en.rst

+.. _api_guide_activations_en:
+
+###################
+Activation Function
+###################
+
+The activation function incorporates non-linearity properties into the neural network.
+
+PaddlePaddle Fluid supports most of the activation functions, including:
+
+:ref:`api_fluid_layers_relu`,
+:ref:`api_fluid_layers_tanh`,
+:ref:`api_fluid_layers_sigmoid`,
+:ref:`api_fluid_layers_elu`,
+:ref:`api_fluid_layers_relu6`,
+:ref:`api_fluid_layers_pow`,
+:ref:`api_fluid_layers_stanh`,
+:ref:`api_fluid_layers_hard_sigmoid`,
+:ref:`api_fluid_layers_swish`,
+:ref:`api_fluid_layers_prelu`,
+:ref:`api_fluid_layers_brelu`,
+:ref:`api_fluid_layers_leaky_relu`,
+:ref:`api_fluid_layers_soft_relu`,
+:ref:`api_fluid_layers_thresholded_relu`,
+:ref:`api_fluid_layers_maxout`,
+:ref:`api_fluid_layers_logsigmoid`,
+:ref:`api_fluid_layers_hard_shrink`,
+:ref:`api_fluid_layers_softsign`,
+:ref:`api_fluid_layers_softplus`,
+:ref:`api_fluid_layers_tanh_shrink`,
+:ref:`api_fluid_layers_softshrink`,
+:ref:`api_fluid_layers_exp`.
+
+
+**Fluid provides two ways to use the activation function:**
+
+- If a layer interface provides :code:`act` variables (default None), we can specify the type of layer activation function through this parameter. This mode supports common activation functions :code:`relu`, :code:`tanh`, :code:`sigmoid`, :code:`identity`.
+
+.. code-block:: python
+
+	conv2d = fluid.layers.conv2d(input=data, num_filters=2, filter_size=3, act="relu")
+
+
+- Fluid provides an interface for each Activation, and we can explicitly call it.
+
+.. code-block:: python
+
+	conv2d = fluid.layers.conv2d(input=data, num_filters=2, filter_size=3)
+	relu1 = fluid.layers.relu(conv2d)