tensorflow进阶

分类学习

分类和回归的区别在于输出变量的类型上。通俗理解定量输出是回归,或者说是连续变量预测;定性输出是分类,或者说是离散变量预测。如预测房价这是一个回归任务;把东西分成几类, 比如猫狗猪牛,就是一个分类任务。

MNIST

MNIST是一个入门级的计算机视觉数据集,它包含各种手写数字图片,它也包含每一张图片对应的标签,告诉我们这个是数字几。接下来,我们将训练一个机器学习模型用于预测图片里面的数字。

下载数据集

访问THE MNIST DATABASE,下载:

  • train-images-idx3-ubyte.gz
  • train-labels-idx1-ubyte.gz
  • t10k-images-idx3-ubyte.gz
  • t10k-labels-idx1-ubyte.gz

把这四个文件放到Tensorflow-Tutorial/tutorial-contents/mnist目录下。

数据中包含55000张训练图片,每张图片的分辨率是28×28,所以我们的训练网络输入应该是28×28=784个像素数据。

编码

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from __future__ import print_function
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
# number 1 to 10 data
mnist = input_data.read_data_sets('./mnist', one_hot=True)

def add_layer(inputs, in_size, out_size, activation_function=None,):
# add one more layer and return the output of this layer
Weights = tf.Variable(tf.random_normal([in_size, out_size]))
biases = tf.Variable(tf.zeros([1, out_size]) + 0.1,)
Wx_plus_b = tf.matmul(inputs, Weights) + biases
if activation_function is None:
outputs = Wx_plus_b
else:
outputs = activation_function(Wx_plus_b,)
return outputs

def compute_accuracy(v_xs, v_ys):
global prediction
y_pre = sess.run(prediction, feed_dict={xs: v_xs})
correct_prediction = tf.equal(tf.argmax(y_pre,1), tf.argmax(v_ys,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
result = sess.run(accuracy, feed_dict={xs: v_xs, ys: v_ys})
return result

# define placeholder for inputs to network
xs = tf.placeholder(tf.float32, [None, 784]) # 28x28
ys = tf.placeholder(tf.float32, [None, 10])

# add output layer
prediction = add_layer(xs, 784, 10, activation_function=tf.nn.softmax)

# the error between prediction and real data
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction),
reduction_indices=[1])) # loss
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

sess = tf.Session()
# important step
# tf.initialize_all_variables() no long valid from
# 2017-03-02 if using tensorflow >= 0.12
if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1:
init = tf.initialize_all_variables()
else:
init = tf.global_variables_initializer()
sess.run(init)

for i in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={xs: batch_xs, ys: batch_ys})
if i % 50 == 0:
print(compute_accuracy(
mnist.test.images, mnist.test.labels))

过拟合

过拟合的模型,泛化能力差,不能成功的表达除了训练数据以外的其他数据。

数据量

增加数据量,大部分过拟合产生的原因是因为数据量太少了。

正规化

运用正规化,L1、l2 regularization等等,这些方法适用于大多数的机器学习,包括神经网络。主要思想是把W(权重)加入到cost,W变得太大,就让cost随之变大,成为一种惩罚机制。

dropout

还有一种专门用在神经网络的正规化的方法,叫作 dropout。在训练的时候,我们随机忽略掉一些神经元和神经联结,使这个神经网络变得“不完整”。用一个不完整的神经网络训练一次。
到第二次再随机忽略另一些,变成另一个不完整的神经网络。有了这些随机 drop 掉的规则,我们可以想象其实每次训练的时候,我们都让每一次预测结果都不会依赖于其中某部分特定的神经元。

conda install scikit-learn

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# View more python learning tutorial on my Youtube and Youku channel!!!

# Youtube video tutorial: https://www.youtube.com/channel/UCdyjiB5H8Pu7aDTNVXTTpcg
# Youku video tutorial: http://i.youku.com/pythontutorial

"""
Please note, this code is only for python 3+. If you are using python 2+, please modify the code accordingly.
"""
from __future__ import print_function
import tensorflow as tf
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer

# load data
digits = load_digits()
X = digits.data
y = digits.target
y = LabelBinarizer().fit_transform(y)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3)


def add_layer(inputs, in_size, out_size, layer_name, activation_function=None, ):
# add one more layer and return the output of this layer
Weights = tf.Variable(tf.random_normal([in_size, out_size]))
biases = tf.Variable(tf.zeros([1, out_size]) + 0.1, )
Wx_plus_b = tf.matmul(inputs, Weights) + biases
# here to dropout
Wx_plus_b = tf.nn.dropout(Wx_plus_b, keep_prob)
if activation_function is None:
outputs = Wx_plus_b
else:
outputs = activation_function(Wx_plus_b, )
tf.summary.histogram(layer_name + '/outputs', outputs)
return outputs


# define placeholder for inputs to network
keep_prob = tf.placeholder(tf.float32)
xs = tf.placeholder(tf.float32, [None, 64]) # 8x8
ys = tf.placeholder(tf.float32, [None, 10])

# add output layer
l1 = add_layer(xs, 64, 50, 'l1', activation_function=tf.nn.tanh)
prediction = add_layer(l1, 50, 10, 'l2', activation_function=tf.nn.softmax)

# the loss between prediction and real data
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction),
reduction_indices=[1])) # loss
tf.summary.scalar('loss', cross_entropy)
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

sess = tf.Session()
merged = tf.summary.merge_all()
# summary writer goes in here
train_writer = tf.summary.FileWriter("log/train", sess.graph)
test_writer = tf.summary.FileWriter("log/test", sess.graph)

# tf.initialize_all_variables() no long valid from
# 2017-03-02 if using tensorflow >= 0.12
if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1:
init = tf.initialize_all_variables()
else:
init = tf.global_variables_initializer()
sess.run(init)
for i in range(500):
# here to determine the keeping probability
sess.run(train_step, feed_dict={xs: X_train, ys: y_train, keep_prob: 0.5})
if i % 50 == 0:
# record loss
train_result = sess.run(merged, feed_dict={xs: X_train, ys: y_train, keep_prob: 1})
test_result = sess.run(merged, feed_dict={xs: X_test, ys: y_test, keep_prob: 1})
train_writer.add_summary(train_result, i)
test_writer.add_summary(test_result, i)

参考资料

源码分享

https://github.com/voidking/Tensorflow-Tutorial.git

书签

TensorFlow官网

Tensorflow游乐场

莫烦Tensorflow教程系列

TensorFlow 官方文档中文版

TensorFlow中文社区

youtube CS 20SI: Tensorflow for Deep Learning Research

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