14-TensorFlow入门和基本模型

一.什么是TensorFlow

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即为张量从图的一端流动到另一端;
支持CNN（卷积神经网络）、RNN（循环神经网络）和LSTM（长短期记忆网络）算法，是目前在 Image，NLP 最流行的深度神经网络模型.

二.TensorFlow优点

第一，基于Python，写的很快并且具有可读性。
第二，在CPU或GPU系统上的都可运行。
第三，代码编译效率较高。
第四，社区发展的非常迅速并且活跃。
第五，能够生成显示网络拓扑结构和性能的可视化图--tensorboard。

三.TensorFlow原理

TensorFlow是用数据流图(data flow graphs)技术来进行数值计算的。
数据流图是描述有向图中的数值计算过程。
有向图中，节点通常代表数学运算，边表示节点之间的某种联系，它负责传输多维数据(Tensors)。

原理图.png

四.TensorFlow使用

使用图(graph)来表示任务;
被称之为会话(Session)的上下文(context)中执行图;
使用tensor表示数据;
通过变量(Variable)维护状态f(x) = w*x + b;
使用feed和fetches可以为任意操作(arbitrary operation)赋值或者从其中获取数据.

五.TensorFlow：Helloworld

#导包
import tensorflow as tf

#声明常量
hello = tf.constant('Hello, TensorFlow!')

#创建会话
sess = tf.Session()

#执行
print(sess.run(hello))

sess.close()

六.TensorFlow：基本操作

• Basic constant operations

import tensorflow as tf

a = tf.constant(2)
b = tf.constant(3)

with tf.Session() as sess:
    print("a: %i" % sess.run(a), "b: %i" % sess.run(b))
    print("Addition with constants: %i" % sess.run(a+b))
    print("Multiplication with constants: %i" % sess.run(a*b))

• 赋值assign操作

v = tf.Variable(0,name = 'a')

add = tf.assign_add(v,10)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    print(sess.run(v))
    for i in range(5):
        print(sess.run(add))

• Basic Operations with variable as graph input

import tensorflow as tf

a = tf.placeholder(tf.int16)
b = tf.placeholder(tf.int16)

add = tf.add(a, b)
mul = tf.multiply(a, b)

with tf.Session() as sess:
    # Run every operation with variable input
    print("Addition with variables: %i" % sess.run(add, feed_dict={a: 2, b: 3}))
    print("Multiplication with variables: %i" % sess.run(mul, feed_dict={a: 2, b: 3}))

• 矩阵操作

matrix1 = tf.constant([[1., 6.]])

matrix2 = tf.constant([[3.],[2.]])

product = tf.matmul(matrix1, matrix2)

with tf.Session() as sess:
    result = sess.run(product)
    print(result)

• 根据Graph完成操作

  
  
  

  Graph.png

七.TensorFlow：基本模型

• 梯度下降

#梯度下降示例
import numpy as np
import matplotlib.pyplot as plt
import time
x=np.arange(-5, 5, 0.001)
y=x**4-3*x**3+2

plt.plot(x,y)
plt.show()

old = 0
# 范围就是-5 到5
new= 5

# 步幅
step = 0.001

# 精确度
precision = 0.001

# 导数
def derivative(x):
    return 4*x**3-9*x**2

# 进行梯度下降
while abs(new - old) > precision:
    print('------------------------------>',new)
    time.sleep(1)
    old = new
    new = new - step * derivative(new)

print (new)

• 线性回归

#导包
mport tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
rng = numpy.random

#定义常量
##定义训练次数learning_epochs，卷曲神经的学习率learning_rate显示打印数据的步幅display_step
learning_rate = 0.01
training_epochs = 1000
display_step = 50

#训练数据
train_X = np.linspace(0,10,num= 20)+np.random.randn(20)
train_Y = np.linspace(1,4,num = 20)+np.random.randn(20)
n_samples = train_X.shape[0]

#线性模型
pred = tf.add(tf.multiply(X, W), b)

#创建TensorFlow均方误差cost以及梯度下降优化器optimizer
# 均方误差，平均误差
cost = tf.reduce_sum(tf.pow(pred-Y, 2))/n_samples
# 实现梯度下降算法的优化器
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

线性回归.png

#TensorFlow进行初始化
init = tf.global_variables_initializer()

#进行训练
# 训练开始
with tf.Session() as sess:
    sess.run(init)

    # 训练所有数据
    for epoch in range(training_epochs):
        for (x, y) in zip(train_X, train_Y):
            sess.run(optimizer, feed_dict={X: x, Y: y})

        #每执行50次显示运算结果
        if (epoch+1) % display_step == 0:
            c = sess.run(cost, feed_dict={X: train_X, Y:train_Y})
            print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c),
                  "W=", sess.run(W), "b=", sess.run(b))

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

    #数据可视化
    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()

• 类逻辑斯蒂

softmax:

softmax.png

信息熵:

信息熵.png

交叉熵:

交叉熵.png

steps:

#导包
import tensorflow as tf

# Import MINST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("./", one_hot=True)

#声明算法
# Parameters
learning_rate = 0.01
training_epochs = 25
batch_size = 100
display_step = 1

# tf Graph Input
x = tf.placeholder(tf.float32, shape = [None,784]) # mnist data image of shape 28*28=784
y = tf.placeholder(tf.float32, shape = [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))
# Gradient Descent
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

# Initialize the variables (i.e. assign their default value)
init = tf.global_variables_initializer()

#训练
# Start training
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)
            # Fit training using batch data
            _, 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 for 3000 examples
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    print("Accuracy:", accuracy.eval({x: mnist.test.images[:3000], y: mnist.test.labels[:3000]}))

• 了解KNN

距离

• L1 Distance：曼哈顿距离

distance = tf.reduce_sum(tf.abs(tf.add(xtr, tf.negative(xte))), axis=1)

曼哈顿距离.png

• L2 Distance：欧氏距离

distance = tf.sqrt(tf.reduce_sum(tf.pow((xtr-xte),2),axis = -1))

欧氏距离.png

#导包
import numpy as np
import tensorflow as tf

# Import MINST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("./", one_hot = True)

#算法
Xtr, Ytr = mnist.train.next_batch(5000) #5000 for training (nn candidates)
Xte, Yte = mnist.test.next_batch(200) #200 for testing

# tf Graph Input
xtr = tf.placeholder("float", [None, 784])
xte = tf.placeholder("float", [784])

# 使用 L1 Distance 算法计算距离
# Calculate L1 Distance
distance = tf.reduce_sum(tf.abs(tf.add(xtr, tf.negative(xte))), axis=1)
# distance = tf.sqrt(tf.reduce_sum(tf.pow((xtr-xte),2),axis = -1))
# Prediction: Get min distance index (Nearest neighbor)
pred = tf.argmin(distance, 0)

accuracy = 0.

# Initialize the variables (i.e. assign their default value)
init = tf.global_variables_initializer()

#训练
# Start training
with tf.Session() as sess:
    sess.run(init)

    # loop over test data
    for i in range(len(Xte)):
        # Get nearest neighbor
        nn_index = sess.run(pred, feed_dict={xtr: Xtr, xte: Xte[i, :]})
        # Get nearest neighbor class label and compare it to its true label
        print("Test", i, "Prediction:", np.argmax(Ytr[nn_index]), \
            "True Class:", np.argmax(Yte[i]))
        # Calculate accuracy
        if np.argmax(Ytr[nn_index]) == np.argmax(Yte[i]):
            accuracy += 1./len(Xte)
    print("Done!")
    print("Accuracy:", accuracy)

pip安装指定源
conda install -i https://pypi.tuna.tsinghua.edu.cn/simple tensorflow