USTC-ZCC

IRIS数据集介绍

  IRIS数据集(鸢尾花数据集),是一个经典的机器学习数据集,适合作为多分类问题的测试数据,它的下载地址为:http://archive.ics.uci.edu/ml/machine-learning-databases/iris/
  IRIS数据集是用来给鸢尾花做分类的数据集,一共150个样本,每个样本包含了花萼长度(sepal length in cm)、花萼宽度(sepal width in cm)、花瓣长度(petal length in cm)、花瓣宽度(petal width in cm)四个特征,将鸢尾花分为三类,分别为Iris Setosa,Iris Versicolour,Iris Virginica,每一类都有50个样本。
  IRIS数据集具体如下(只展示部分数据,顺序已打乱):

读取数据集

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer

# 读取CSV数据集,并拆分为训练集和测试集
# 该函数的传入参数为CSV_FILE_PATH: csv文件路径
def load_data(CSV_FILE_PATH):
    IRIS = pd.read_csv(CSV_FILE_PATH)
    target_var = \'class\'  # 目标变量
    # 数据集的特征
    features = list(IRIS.columns)
    features.remove(target_var)
    # 目标变量的类别
    Class = IRIS[target_var].unique()
    # 目标变量的类别字典
    Class_dict = dict(zip(Class, range(len(Class))))
    # 增加一列target, 将目标变量进行编码
    IRIS[\'target\'] = IRIS[target_var].apply(lambda x: Class_dict[x])
    # 对目标变量进行0-1编码(One-hot Encoding)
    lb = LabelBinarizer()
    lb.fit(list(Class_dict.values()))
    transformed_labels = lb.transform(IRIS[\'target\'])
    y_bin_labels = []  # 对多分类进行0-1编码的变量
    for i in range(transformed_labels.shape[1]):
        y_bin_labels.append(\'y\' + str(i))
        IRIS[\'y\' + str(i)] = transformed_labels[:, i]
    # 将数据集分为训练集和测试集
    train_x, test_x, train_y, test_y = train_test_split(IRIS[features], IRIS[y_bin_labels], \
                                                        train_size=0.7, test_size=0.3, random_state=0)
    return train_x, test_x, train_y, test_y, Class_dict

搭建DNN

  接下来,笔者将展示如何利用Keras来搭建一个简单的深度神经网络(DNN)来解决这个多分类问题。我们要搭建的DNN的结构如下图所示:

                                                                                                                                             DNN模型的结构示意图

我们搭建的DNN由输入层、隐藏层、输出层和softmax函数组成,其中输入层由4个神经元组成,对应IRIS数据集中的4个特征,作为输入向量,隐藏层有两层,每层分别有5和6个神经元,之后就是输出层,由3个神经元组成,对应IRIS数据集的目标变量的类别个数,最后,就是一个softmax函数,用于解决多分类问题而创建。
  对应以上的DNN结构,用Keras来搭建的话,其Python代码如下:

 import keras as K
    # 2. 定义模型
    init = K.initializers.glorot_uniform(seed=1)
    simple_adam = K.optimizers.Adam()
    model = K.models.Sequential()
    model.add(K.layers.Dense(units=5, input_dim=4, kernel_initializer=init, activation=\'relu\'))
    model.add(K.layers.Dense(units=6, kernel_initializer=init, activation=\'relu\'))
    model.add(K.layers.Dense(units=3, kernel_initializer=init, activation=\'softmax\'))
    model.compile(loss=\'categorical_crossentropy\', optimizer=simple_adam, metrics=[\'accuracy\'])

在这个模型中,我们选择的神经元激活函数为ReLU函数,损失函数为交叉熵(cross entropy),迭代的优化器(optimizer)选择Adam,最初各个层的连接权重(weights)和偏重(biases)是随机生成的。这样我们就讲这个DNN的模型定义完毕了。

训练及预测

  OK,定义完模型后,我们需要对模型进行训练、评估及预测。对于模型训练,我们每次训练的批数为1,共迭代100次,代码如下(接以上代码):

   # 3. 训练模型
    b_size = 1
    max_epochs = 100
    print("Starting training ")
    h = model.fit(train_x, train_y, batch_size=b_size, epochs=max_epochs, shuffle=True, verbose=1)
    print("Training finished \n")

为了对模型有个评估,感知模型的表现,需要输出该DNN模型的损失函数的值以及在测试集上的准确率,其Python代码如下(接以上代码):

  # 4. 评估模型
    eval = model.evaluate(test_x, test_y, verbose=0)
    print("Evaluation on test data: loss = %0.6f accuracy = %0.2f%% \n" \
          % (eval[0], eval[1] * 100) )

训练100次,输出的结果如下(中间部分的训练展示已忽略):

Starting training 
Epoch 1/100

  1/105 [..............................] - ETA: 17s - loss: 0.3679 - acc: 1.0000
 42/105 [===========>..................] - ETA: 0s - loss: 1.8081 - acc: 0.3095 
 89/105 [========================>.....] - ETA: 0s - loss: 1.5068 - acc: 0.4270
105/105 [==============================] - 0s 3ms/step - loss: 1.4164 - acc: 0.4667
Epoch 2/100

  1/105 [..............................] - ETA: 0s - loss: 0.4766 - acc: 1.0000
 45/105 [===========>..................] - ETA: 0s - loss: 1.0813 - acc: 0.4889
 93/105 [=========================>....] - ETA: 0s - loss: 1.0335 - acc: 0.4839
105/105 [==============================] - 0s 1ms/step - loss: 1.0144 - acc: 0.4857

......

Epoch 99/100

  1/105 [..............................] - ETA: 0s - loss: 0.0013 - acc: 1.0000
 43/105 [===========>..................] - ETA: 0s - loss: 0.0447 - acc: 0.9767
 84/105 [=======================>......] - ETA: 0s - loss: 0.0824 - acc: 0.9524
105/105 [==============================] - 0s 1ms/step - loss: 0.0711 - acc: 0.9619
Epoch 100/100

  1/105 [..............................] - ETA: 0s - loss: 2.3032 - acc: 0.0000e+00
 51/105 [=============>................] - ETA: 0s - loss: 0.1122 - acc: 0.9608    
 99/105 [===========================>..] - ETA: 0s - loss: 0.0755 - acc: 0.9798
105/105 [==============================] - 0s 1ms/step - loss: 0.0756 - acc: 0.9810
Training finished 

Evaluation on test data: loss = 0.094882 accuracy = 97.78% 

可以看到,训练完100次后,在测试集上的准确率已达到97.78%,效果相当好。
  最后是对新数据集进行预测,我们假设一朵鸢尾花的4个特征为6.1,3.1,5.1,1.1,我们想知道这个DNN模型会把它预测到哪一类,其Python代码如下:

    import numpy as np
    # 5. 使用模型进行预测
    np.set_printoptions(precision=4)
    unknown = np.array([[6.1, 3.1, 5.1, 1.1]], dtype=np.float32)
    predicted = model.predict(unknown)
    print("Using model to predict species for features: ")
    print(unknown)
    print("\nPredicted softmax vector is: ")
    print(predicted)
    species_dict = {v:k for k,v in Class_dict.items()}
    print("\nPredicted species is: ")
    print(species_dict[np.argmax(predicted)])

输出的结果如下:

Using model to predict species for features: 
[[ 6.1  3.1  5.1  1.1]]

Predicted softmax vector is: 
[[  2.0687e-07   9.7901e-01   2.0993e-02]]

Predicted species is: 
versicolor

如果我们仔细地比对IRIS数据集,就会发现,这个预测结果令人相当满意,这个鸢尾花样本的预测结果,以人类的眼光来看,也应当是versicolor。

 

最后,附上该DNN模型的完整Python代码:
# iris_keras_dnn.py
# Python 3.5.1, TensorFlow 1.6.0, Keras 2.1.5
# ========================================================
# 导入模块
import os
import numpy as np
import keras as K
import tensorflow as tf
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
os.environ[\'TF_CPP_MIN_LOG_LEVEL\']=\'2\'

# 读取CSV数据集,并拆分为训练集和测试集
# 该函数的传入参数为CSV_FILE_PATH: csv文件路径
def load_data(CSV_FILE_PATH):
    IRIS = pd.read_csv(CSV_FILE_PATH)
    target_var = \'class\'  # 目标变量
    # 数据集的特征
    features = list(IRIS.columns)
    features.remove(target_var)
    # 目标变量的类别
    Class = IRIS[target_var].unique()
    # 目标变量的类别字典
    Class_dict = dict(zip(Class, range(len(Class))))
    # 增加一列target, 将目标变量进行编码
    IRIS[\'target\'] = IRIS[target_var].apply(lambda x: Class_dict[x])
    # 对目标变量进行0-1编码(One-hot Encoding)
    lb = LabelBinarizer()
    lb.fit(list(Class_dict.values()))
    transformed_labels = lb.transform(IRIS[\'target\'])
    y_bin_labels = []  # 对多分类进行0-1编码的变量
    for i in range(transformed_labels.shape[1]):
        y_bin_labels.append(\'y\' + str(i))
        IRIS[\'y\' + str(i)] = transformed_labels[:, i]
    # 将数据集分为训练集和测试集
    train_x, test_x, train_y, test_y = train_test_split(IRIS[features], IRIS[y_bin_labels], \
                                                        train_size=0.7, test_size=0.3, random_state=0)
    return train_x, test_x, train_y, test_y, Class_dict

def main():

    # 0. 开始
    print("\nIris dataset using Keras/TensorFlow ")
    np.random.seed(4)
    tf.set_random_seed(13)

    # 1. 读取CSV数据集
    print("Loading Iris data into memory")
    CSV_FILE_PATH = \'E://iris.csv\'
    train_x, test_x, train_y, test_y, Class_dict = load_data(CSV_FILE_PATH)

    # 2. 定义模型
    init = K.initializers.glorot_uniform(seed=1)
    simple_adam = K.optimizers.Adam()
    model = K.models.Sequential()
    model.add(K.layers.Dense(units=5, input_dim=4, kernel_initializer=init, activation=\'relu\'))
    model.add(K.layers.Dense(units=6, kernel_initializer=init, activation=\'relu\'))
    model.add(K.layers.Dense(units=3, kernel_initializer=init, activation=\'softmax\'))
    model.compile(loss=\'categorical_crossentropy\', optimizer=simple_adam, metrics=[\'accuracy\'])

    # 3. 训练模型
    b_size = 1
    max_epochs = 100
    print("Starting training ")
    h = model.fit(train_x, train_y, batch_size=b_size, epochs=max_epochs, shuffle=True, verbose=1)
    print("Training finished \n")

    # 4. 评估模型
    eval = model.evaluate(test_x, test_y, verbose=0)
    print("Evaluation on test data: loss = %0.6f accuracy = %0.2f%% \n" \
          % (eval[0], eval[1] * 100) )

    # 5. 使用模型进行预测
    np.set_printoptions(precision=4)
    unknown = np.array([[6.1, 3.1, 5.1, 1.1]], dtype=np.float32)
    predicted = model.predict(unknown)
    print("Using model to predict species for features: ")
    print(unknown)
    print("\nPredicted softmax vector is: ")
    print(predicted)
    species_dict = {v:k for k,v in Class_dict.items()}
    print("\nPredicted species is: ")
    print(species_dict[np.argmax(predicted)])

main()
 
链接:https://www.jianshu.com/p/1d88a6ed707e

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