Activation

Simple activation function to be applied to the output.

Scala:

Activation(activation, inputShape = null)

Python:

Activation(activation, input_shape=None, name=None)

Parameters:

activation: Name of the activation function as string. See here for available activation strings.
inputShape: Only need to specify this argument when you use this layer as the first layer of a model. For Scala API, it should be a Shape object. For Python API, it should be a shape tuple. Batch dimension should be excluded.

Scala example:

import com.intel.analytics.bigdl.nn.keras.{Sequential, Activation}
import com.intel.analytics.bigdl.utils.Shape
import com.intel.analytics.bigdl.tensor.Tensor

val model = Sequential[Float]()
model.add(Activation("tanh", inputShape = Shape(3)))
val input = Tensor[Float](2, 3).randn()
val output = model.forward(input)

Input is:

input: com.intel.analytics.bigdl.tensor.Tensor[Float] =
2.1659365   0.28006053  -0.20148286
0.9146865    3.4301455    1.0930616
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x3]

Output is:

output: com.intel.analytics.bigdl.nn.abstractnn.Activity =
0.9740552    0.2729611    -0.1988
 0.723374   0.99790496  0.7979928
[com.intel.analytics.bigdl.tensor.DenseTensor of size 2x3]

Python example:

import numpy as np
from bigdl.nn.keras.topology import Sequential
from bigdl.nn.keras.layer import Activation

model = Sequential()
model.add(Activation("tanh", input_shape=(3, )))
input = np.random.random([2, 3])
output = model.forward(input)

Input is:

[[ 0.26202468  0.15868397  0.27812652]
 [ 0.45931689  0.32100054  0.51839282]]

Output is

[[ 0.2561883   0.15736534  0.27117023]
 [ 0.42952728  0.31041133  0.47645861]]

Note that the following two pieces of code will be equivalent:

model.add(Dense(32))
model.add(Activation('relu'))

model.add(Dense(32, activation="relu"))

Activation

Activation

Available Activations