# 机器学习进阶笔记之一 | TensorFlow安装与入门

TensorFlow 是 Google 基于 DistBelief 进行研发的第二代人工智能学习系统，被广泛用于语音识别或图像识别等多项机器深度学习领域。其命名来源于本身的运行原理。Tensor（张量）意味着 N 维数组，Flow（流）意味着基于数据流图的计算，TensorFlow 代表着张量从图象的一端流动到另一端计算过程，是将复杂的数据结构传输至人工智能神经网中进行分析和处理的过程。 —— 由 UCloud云计算 分享

## 引言

TensorFlow完全开源，任何人都可以使用。可在小到一部智能手机、大到数千台数据中心服务器的各种设备上运行。

『机器学习进阶笔记』系列是将深入解析TensorFlow系统的技术实践，从零开始，由浅入深，与大家一起走上机器学习的进阶之路。

## Hello World

`````` import tensorflow as tf
hello = tf.constant(‘Hello, TensorFlow!‘)
sess = tf.Session()
print sess.run(hello)
``````

`````` import tensorflow as tf
a = tf.constant(2)
b = tf.constant(3)
with tf.Session() as sess:
print "a=2, b=3"
print "Addition with constants: %i" % sess.run(a+b)
print "Multiplication with constants: %i" % sess.run(a*b)
# output
a=2, b=3
Multiplication with constants: 6
``````

placeholder的使用见 https://www.tensorflow.org/versions/r0.8/api_docs/python/io_ops.html#placeholder

`````` import tensorflow as tf
a = tf.placeholder(tf.int16)
b = tf.placeholder(tf.int16)
mul = tf.mul(a, b)
with tf.Session() as sess:
# Run every operation with variable input
print "Multiplication with variables: %i" % sess.run(mul, feed_dict={a: 2, b: 3})
# output:
Multiplication with variables: 6
matrix1 = tf.constant([[3., 3.]])
matrix2 = tf.constant([[2.],[2.]])
with tf.Session() as sess:
result = sess.run(product)
print result
``````

## 线性回归

`````` import tensorflow as tf
import numpy
import matplotlib.pyplot as plt
rng = numpy.random

# Parameters
learning_rate = 0.01
training_epochs = 2000
display_step = 50

# Training Data
train_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,7.042,10.791,5.313,7.997,5.654,9.27,3.1])
train_Y = numpy.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,2.827,3.465,1.65,2.904,2.42,2.94,1.3])
n_samples = train_X.shape[0]

# tf Graph Input
X = tf.placeholder("float")
Y = tf.placeholder("float")

# Create Model

# Set model weights
W = tf.Variable(rng.randn(), name="weight")
b = tf.Variable(rng.randn(), name="bias")

# Construct a linear model

# Minimize the squared errors
cost = tf.reduce_sum(tf.pow(activation-Y, 2))/(2*n_samples) #L2 loss

# Initializing the variables
init = tf.initialize_all_variables()

# Launch the graph
with tf.Session() as sess:
sess.run(init)

# Fit all training data
for epoch in range(training_epochs):
for (x, y) in zip(train_X, train_Y):
sess.run(optimizer, feed_dict={X: x, Y: y})

#Display logs per epoch step
if epoch % display_step == 0:
print "Epoch:", ‘%04d‘ % (epoch+1), "cost=",                  "{:.9f}".format(sess.run(cost, feed_dict={X: train_X, Y:train_Y})),                  "W=", sess.run(W), "b=", sess.run(b)

print "Optimization Finished!"
print "cost=", sess.run(cost, feed_dict={X: train_X, Y: train_Y}),            "W=", sess.run(W), "b=", sess.run(b)

#Graphic display
plt.plot(train_X, train_Y, ‘ro‘, label=‘Original data‘)
plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label=‘Fitted line‘)
plt.legend()
plt.show()
``````

## 逻辑回归

`````` import tensorflow as tf
# Import MINST data
from tensorflow.examples.tutorials.mnist import input_data

# Parameters
learning_rate = 0.01
training_epochs = 25
batch_size = 100
display_step = 1

# tf Graph Input
x = tf.placeholder(tf.float32, [None, 784]) # mnist data image of shape 28*28=784
y = tf.placeholder(tf.float32, [None, 10]) # 0-9 digits recognition => 10 classes

# Set model weights
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))

# Construct model
pred = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax

# Minimize error using cross entropy
cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1))

# Initializing the variables
init = tf.initialize_all_variables()

# Launch the graph
with tf.Session() as sess:
sess.run(init)

# Training cycle
for epoch in range(training_epochs):
avg_cost = 0.
total_batch = int(mnist.train.num_examples/batch_size)
# Loop over all batches
for i in range(total_batch):
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
# Run optimization op (backprop) and cost op (to get loss value)
_, c = sess.run([optimizer, cost], feed_dict={x: batch_xs,
y: batch_ys})
# Compute average loss
avg_cost += c / total_batch
# Display logs per epoch step
if (epoch+1) % display_step == 0:
print "Epoch:", ‘%04d‘ % (epoch+1), "cost=", "{:.9f}".format(avg_cost)

print "Optimization Finished!"

# Test model
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
# Calculate accuracy
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print "Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})

# result :
Epoch: 0001 cost= 29.860467369
Epoch: 0002 cost= 22.001451784
Epoch: 0003 cost= 21.019925554
Epoch: 0004 cost= 20.561320320
Epoch: 0005 cost= 20.109135756
Epoch: 0006 cost= 19.927862290
Epoch: 0007 cost= 19.548687116
Epoch: 0008 cost= 19.429119071
Epoch: 0009 cost= 19.397068211
Epoch: 0010 cost= 19.180813479
Epoch: 0011 cost= 19.026808132
Epoch: 0012 cost= 19.057875510
Epoch: 0013 cost= 19.009575057
Epoch: 0014 cost= 18.873240641
Epoch: 0015 cost= 18.718575359
Epoch: 0016 cost= 18.718761925
Epoch: 0017 cost= 18.673640560
Epoch: 0018 cost= 18.562128253
Epoch: 0019 cost= 18.458205289
Epoch: 0020 cost= 18.538211225
Epoch: 0021 cost= 18.443384213
Epoch: 0022 cost= 18.428727668
Epoch: 0023 cost= 18.304270616
Epoch: 0024 cost= 18.323529782
Epoch: 0025 cost= 18.247192113
Optimization Finished!
(10000, 784)
Accuracy 0.9206
``````

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Burness（@段石石 ）， UCloud平台研发中心深度学习研发工程师，tflearn Contributor，做过电商推荐、精准化营销相关算法工作，专注于分布式深度学习框架、计算机视觉算法研究，平时喜欢玩玩算法，研究研究开源的项目，偶尔也会去一些数据比赛打打酱油，生活中是个极客，对新技术、新技能痴迷。

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