Activation Functions

The activation ops provide different types of onolinearities for use in neural networks. These include:

• smooth nonlinearities (sigmoid, tanh, elu, softplus, and softsign)
• continous but not everywhere differentiable functions (relu, relu6, and relu_x) and
• random regularization (dropout)

All activation ops apply componentwise, and produce a tensor of the same shape as the input tensor.

tf.nn.relu(features, name=None)

Computes rectified linear: max(features, 0).

tf.nn.relu6(features, name=None)

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

tf.nn.elu(features, name=None)

Compute exponential linear: exp(feature) - 1 if < 0, features otherwise.

See Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)



tf.nn.softplus(features, name=None)

Compute softplus: log(exp(features) + 1).

tf.nn.softsign(features, name=None)

Compute softsign: features / (abs(features) + 1).

tf.nn.dropout(x, keep_prob, noise_shape=None, seed=None, name=None)

Computes dropout.

With probability keep_prob, outputs the input element scaled up by 1 / keep_prob, otherwise outputs 0. The scaling is so that the expected sum is unchanged.

By default, each element is kept or dropped independently. If noise_shape is specified, it must be broadcastable to the shape of , and only dimensions with noise_shape[i] will make independent decisions.

tf.nn.bias_add(value, bias, data_format=None, name=None)

Add bias to value.
This is (mostly) a special case of tf.add where bias is restricted to 1-D. Broadcasting is supported, so value may have any number of dimensions.

tf.sigmoid(x, name=None)

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

tf.tanh(x, name=None)

Computes hyperbolic tangent of x element-wise.