What are Arithmetic functions and Activation functions in chainer explain

This recipe explains what are Arithmetic functions and Activation functions in chainer explain

Recipe Objective - What are Arithmetic functions and Activation functions in chainer explain?

Basic arithmetic operations for Variables are implemented as operators.

chainer.functions.add - Element-wise addition.

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Example -

# Importing necessary libraries
import chainer
import numpy
from chainer import function_node
# Creating chainer variable
x = chainer.Variable(numpy.arange(5, dtype=numpy.int32))
# Sum function
chainer.functions.sum(x.data.astype(numpy.float))

Output - variable(10.)

In artificial neural networks, the activation function of a node defines the output of that node given an input or set of inputs.

Some of the activation functions are -

chainer.functions.clipped_relu - Clipped Rectifier Unit function.

chainer.functions.crelu - Concatenated Rectified Linear Unit function.

chainer.functions.hard_sigmoid - Element-wise hard-sigmoid function.

chainer.functions.softmax - Softmax function.

chainer.functions.tanh - Elementwise hyperbolic tangent function.

Example -

x = numpy.array([[-1, 0], [2, -3], [-2, 1]], numpy.float32)
# Applying relu function
y = chainer.functions.relu(x)
print(x)
print(y)

Output - 
[[-1.  0.]
 [ 2. -3.]
 [-2.  1.]]
variable([[0. 0.]
          [2. 0.]
          [0. 1.]])

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