In AlfonsoRReyes/rDeepThought:

https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/basic_operations.ipynb

library(tensorflow)

# Basic constant operations
# The value returned by the constructor represents the output
a <- tf$constant(2)
b <- tf$constant(3)
# sess$run(hello)

sess = tf$Session()

print(sess$run(a))
print(sess$run(b))

# Basic Operations with variable as graph input
# The value returned by the constructor represents the output
# of the Variable op. (define as input when running session)
# tf Graph input
a = tf$placeholder(tf$int16)
b = tf$placeholder(tf$int16)

# Define some operations
add = tf$add(a, b)
mul = tf$multiply(a, b)

sess$run(add, feed_dict = dict(a=2, b=4))

sess$run(mul, feed_dict = dict(a = 2, b = 3))

# ----------------
# More in details:
# Matrix Multiplication from TensorFlow official tutorial

# Create a Constant op that produces a 1x2 matrix.  The op is
# added as a node to the default graph.
#
# The value returned by the constructor represents the output
# of the Constant op.
matrix1 = tf$constant(matrix(c(3,3), 1, 2))
matrix1

# Create another Constant that produces a 2x1 matrix.
matrix2 = tf$constant(matrix(c(2,2), 2, 1))
matrix2

# Create a Matmul op that takes 'matrix1' and 'matrix2' as inputs.
# The returned value, 'product', represents the result of the matrix
# multiplication.
product = tf$matmul(matrix1, matrix2)
product

sess = tf$Session()
result <- sess$run(product)
print(result)

AlfonsoRReyes/rDeepThought documentation built on May 3, 2019, 6:42 p.m.