TensorFlow入门

TensorFlow计算模型--计算图

计算图的概念

TensorFlow两个重要概念:Tensor和Flow,Tensor就是张量（可以理解为多维数组）,Flow就是计算相互转化的过程。TensorFlow的计算方式类似Spark的有向无环图(DAG),在创建Session之后才开始计算（类似Action算子）。

简单示例

import tensorflow as tf 
a = tf.constant([1.0,2.0],name="a")
b = tf.constant([3.0,4.0],name="b")
result = a + b
sess = tf.Session()
print(sess.run(result)) # [ 4.  6.]

TensorFlow数据模型--张量

张量的概念

张量可以简单理解为多维数组。 零阶张量表示标量(scalar)，也就是一个数。一阶张量表示为向量(vector),也就是一维数组。n阶张量表示为n维数组。但张量在TensorFlow中只是对结算结果的引用，它保存的是如何得到这些数字的计算过程。

import tensorflow as tf
a = tf.constant([1.0,2.0],name="a")
b = tf.constant([3.0,4.0],name="b")
result = a + b
print(result)
# Tensor("add_1:0", shape=(2,), dtype=float32)

上面输出了三个属性:名字(name)、维度(shape)、类型(type)
张量的第一个属性名字是张量的唯一标识符，也显示出这个张量是如何计算出来的
张量的第二个属性维度是张量的维度信息，上面输出结果shape(2,)表示是一个一维数组，长度为2
张量的第三个属性类型是每个张量都会有的唯一类型，TensorFlow会对所有参与运算的张量进行类型检查，如果类型不匹配会报错。

TensorFlow运行模型--会话

创建会话的两种方式

# 创建一个会话
sess = tf.Session()
sess.run()
sess.cloes()
# 这种创建会话的方式需要显示关闭会话，释放资源

# 使用python 上下位管理器来管理这个会话
with tf.Session() as sess:
    sess.run()
# 不需要显示调用"sess.close()"函数来关闭会话
# 当上下文退出时会话关闭和资源释放也自动完成了

TensorFlow会生成一个默认的计算图，可以通过tf.Tensor.eval函数来计算一个张量的取值

import tensorflow as tf
a = tf.constant([1.0,2.0],name="a")
b = tf.constant([3.0,4.0],name="b")
result = a + b
with tf.Session() as sess:
    # 两种方式计算张量的取值
    print(sess.run(result))
    print(result.eval(session=sess))

神经网络参数与TenworFlow变量

变量(tf.Variable)的作用就是保存和更新神经网络中的参数

# 声明一个2 * 3 的矩阵变量，矩阵均值为0，标准差为2的随机数
import tensorflow as tf
weights = tf.Variable(tf.random_normal([2,3],stddev=2))
# 初始化变量
init = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init)
    print(sess.run(weights))
    # [[-0.69297457  1.13187325  2.36984086]
    #  [ 1.20076609  0.77468276  2.01622796]]

TensorFlow随机数生成函数

函数名称	随机数分布	主要参数
tf.random_normal	正太分布	平均值、标准差、取值类型
tf.truncated_normal	正太分布,但如果随机出来的值离平均值超过2个标准差，那么这个数将会被重新随机	平均值、标准差、取值类型
tf.random_uniform	平均分布	最小、最大取值、取值类型
tf.random_gramma	Gramma分布	形状参数alpha、尺度参数beta、取值类型

TensorFlow常数生成函数

函数名称	功能	样例
tf.zeros	产生全0的数组	tf.zeros([2,3],int32)->[[0,0,0],[0,0,0]]
tf.ones	产生全1的数组	tf.ones([2,3],int32)->[[1,1,1],[1,1,1]]
tf.fill	产生一个全部为给定数字的数组	tf.fill([2,3],9)->[[9,9,9],[9,9,9]]
tf.constant	产生一个给定值的常量	tf.constant([1,2,3])->[1,2,3]

神经网络程序

import tensorflow as tf
from numpy.random import RandomState

# 定义训练数据batch的大小
batch_size = 8

# 定义神经网络的参数
w1 = tf.Variable(tf.random_normal([2,3],stddev=1,seed=1))
w2 = tf.Variable(tf.random_normal([3,1],stddev=1,seed=1))

# 在shape的一个维度上使用None可以方便使用不同的batch大小
x = tf.placeholder(tf.float32,shape=(None,2),name='x-input')
y_ = tf.placeholder(tf.float32,shape=(None,1),name='y-input')

# 定义神经网络前向传播的过程
a = tf.matmul(x,w1)
y = tf.matmul(a,w2)

# 定义损失函数和反响传播算法
cross_entropy = -tf.reduce_mean(y_ * tf.log(tf.clip_by_value(y,1e-10,1.0)))
train_step = tf.train.AdamOptimizer(0.001).minimize(cross_entropy)

# 通过随机数生成一个模拟数据集
rdm = RandomState(1)
dataset_size = 128
X = rdm.rand(dataset_size,2)

# 定义规则来给出样本的标签，x1+x2<1的样例都被认为是正样本，其他为负样本，0：负样本，1：正样本
Y = [[int(x1+x2<1)] for (x1,x2) in X]
# 创建一个会话来运行TensorFlow程序
with tf.Session() as sess:
    # 初始化变量
    init_op = tf.global_variables_initializer()
    sess.run(init_op)
    
    print(sess.run(w1))
    print(sess.run(w2))
    
    # 设定训练的轮数
    STEPS = 5000
    
    for i in range(STEPS):
        # 每次选取batch_size 个样本进行训练
        start = (i * batch_size)% dataset_size
        end = min(start+batch_size,dataset_size)
        
        # 通过选取的样本训练神经网络并更新参数
        sess.run(train_step,feed_dict={x:X[start:end],y_:Y[start:end]})
        if i % 1000 == 0:
            total_cross_entropy = sess.run(cross_entropy,feed_dict={x:X,y_:Y})
            print("After %d trainint step(s),cross entropy on all data is %g" % (i,total_cross_entropy))
            
    print(sess.run(w1))
    print(sess.run(w2))

训练神经网络的过程可以分为3个步骤:

1. 定义神经网络的结构和前向传播的输出结果
2. 定义损失函数以及选择反向传播优化的算法
3. 生成会话(tf.Session)并在训练数据上仿佛运行反向传播优化算法

tensorflow实现线性回归

'''
A linear regression learning algorithm example using TensorFlow library.

Author: Aymeric Damien
Project: https://github.com/aymericdamien/TensorFlow-Examples/
'''

from __future__ import print_function

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

# Parameters
learning_rate = 0.01
training_epochs = 1000
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")

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

# Construct a linear model
pred = tf.add(tf.multiply(X, W), b)

# Mean squared error
cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples)
# Gradient descent
#  Note, minimize() knows to modify W and b because Variable objects are trainable=True by default
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

# Initializing the variables
init = tf.global_variables_initializer()

# 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+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')

    # 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()

    # Testing example, as requested (Issue #2)
    test_X = numpy.asarray([6.83, 4.668, 8.9, 7.91, 5.7, 8.7, 3.1, 2.1])
    test_Y = numpy.asarray([1.84, 2.273, 3.2, 2.831, 2.92, 3.24, 1.35, 1.03])

    print("Testing... (Mean square loss Comparison)")
    testing_cost = sess.run(
        tf.reduce_sum(tf.pow(pred - Y, 2)) / (2 * test_X.shape[0]),
        feed_dict={X: test_X, Y: test_Y})  # same function as cost above
    print("Testing cost=", testing_cost)
    print("Absolute mean square loss difference:", abs(
        training_cost - testing_cost))

    plt.plot(test_X, test_Y, 'bo', label='Testing data')
    plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
    plt.legend()
    plt.show()

tensorflow实现逻辑回归

'''
A logistic regression learning algorithm example using TensorFlow library.
This example is using the MNIST database of handwritten digits
(http://yann.lecun.com/exdb/mnist/)

Author: Aymeric Damien
Project: https://github.com/aymericdamien/TensorFlow-Examples/
'''

from __future__ import print_function

import tensorflow as tf

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

# 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))
# Gradient Descent
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

# Initializing the variables
init = tf.global_variables_initializer()

# 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}))

tensorflow实现K-近邻

'''
A nearest neighbor learning algorithm example using TensorFlow library.
This example is using the MNIST database of handwritten digits
(http://yann.lecun.com/exdb/mnist/)

Author: Aymeric Damien
Project: https://github.com/aymericdamien/TensorFlow-Examples/
'''

from __future__ import print_function

import numpy as np
import tensorflow as tf

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

# In this example, we limit mnist data
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])

# Nearest Neighbor calculation using L1 Distance
# Calculate L1 Distance
distance = tf.reduce_sum(tf.abs(tf.add(xtr, tf.negative(xte))), reduction_indices=1)
# Prediction: Get min distance index (Nearest neighbor)
pred = tf.arg_min(distance, 0)

accuracy = 0.

# Initializing the variables
init = tf.global_variables_initializer()

# Launch the graph
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)

笔记来自<< TensorFlow:实战Google深度学习框架 >>

来源：https://www.cnblogs.com/Elag/p/7009476.html