Tensorflow不仅仅是一个深度学习框架。它是一种通用计算框架，用于以并行和分布式方式执行通用数学运算。下面描述这样的示例。
常用的并且易于计算的基本统计示例是将一条线拟合到数据集。在tensorflow中执行此操作的方法在下面的代码和注释中进行了描述。
(TensorFlow)脚本的主要步骤是：
声明占位符（x_ph，y_ph）和变量（W，b）
定义初始化运算符（init）
在占位符和变量声明操作（y_pred，loss，train_op）
建立工作阶段（sess）
运行初始化运算符（）sess.run(init)
运行一些图形操作（例如sess.run([train_op, loss], feed_dict={x_ph: x, y_ph: y})）
图形构建使用Python TensorFlow API完成（也可以使用C ++ TensorFlow API完成）。运行该图将调用低级C ++例程。
'''
function: create a linear model which try to fit the line
y = x + 2 using SGD optimizer to minimize
root-mean-square(RMS) loss function
'''
import tensorflow as tf
import numpy as np
# number of epoch
num_epoch = 100
# training data x and label y
x = np.array([0., 1., 2., 3.], dtype=np.float32)
y = np.array([2., 3., 4., 5.], dtype=np.float32)
# convert x and y to 4x1 matrix
x = np.reshape(x, [4, 1])
y = np.reshape(y, [4, 1])
# test set(using a little trick)
x_test = x + 0.5
y_test = y + 0.5
# This part of the script builds the TensorFlow graph using the Python API
# First declare placeholders for input x and label y
# Placeholders are TensorFlow variables requiring to be explicitly fed by some
# input data
x_ph = tf.placeholder(tf.float32, shape=[None, 1])
y_ph = tf.placeholder(tf.float32, shape=[None, 1])
# Variables (if not specified) will be learnt as the GradientDescentOptimizer
# is run
# Declare weight variable initialized using a truncated_normal law
W = tf.Variable(tf.truncated_normal([1, 1], stddev=0.1))
# Declare bias variable initialized to a constant 0.1
b = tf.Variable(tf.constant(0.1, shape=[1]))
# Initialize variables just declared
init = tf.initialize_all_variables()
# In this part of the script, we build operators storing operations
# on the previous variables and placeholders.
# model: y = w * x + b
y_pred = x_ph * W + b
# loss function
loss = tf.mul(tf.reduce_mean(tf.square(tf.sub(y_pred, y_ph))), 1. / 2)
# create training graph
train_op = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
# This part of the script runs the TensorFlow graph (variables and operations
# operators) just built.
with tf.Session() as sess:
# initialize all the variables by running the initializer operator
sess.run(init)
for epoch in xrange(num_epoch):
# Run sequentially the train_op and loss operators with
# x_ph and y_ph placeholders fed by variables x and y
_, loss_val = sess.run([train_op, loss], feed_dict={x_ph: x, y_ph: y})
print('epoch %d: loss is %.4f' % (epoch, loss_val))
# see what model do in the test set
# by evaluating the y_pred operator using the x_test data
test_val = sess.run(y_pred, feed_dict={x_ph: x_test})
print('ground truth y is: %s' % y_test.flatten())
print('predict y is : %s' % test_val.flatten())