tf.contrib.nn.scaled_softplus

tf.contrib.nn.scaled_softplus(
    x,
    alpha,
    clip=None,
    name=None
)

Defined in tensorflow/contrib/nn/python/ops/scaled_softplus.py.

Returns y = alpha * ln(1 + exp(x / alpha)) or min(y, clip).

This can be seen as a softplus applied to the scaled input, with the output appropriately scaled. As alpha tends to 0, scaled_softplus(x, alpha) tends to relu(x). The clipping is optional. As alpha->0, scaled_softplus(x, alpha) tends to relu(x), and scaled_softplus(x, alpha, clip=6) tends to relu6(x).

Note: the gradient for this operation is defined to depend on the backprop inputs as well as the outputs of this operation.

Args:

• x: A Tensor of inputs.
• alpha: A Tensor, indicating the amount of smoothness. The caller must ensure that alpha > 0.
• clip: (optional) A Tensor, the upper bound to clip the values.
• name: A name for the scope of the operations (optional).

Returns:

A tensor of the size and type determined by broadcasting of the inputs.