程序代码
#-*- coding: utf-8 -*-
#使用神经网络算法预测销量高低
import pandas as pd
#参数初始化
inputfile = 'D:/python/chapter5/demo/data/sales_data.xls'
data = pd.read_excel(inputfile, index_col = u'序号') #导入数据
#数据是类别标签，要将它转换为数据
#用1来表示“好”、“是”、“高”这三个属性，用0来表示“坏”、“否”、“低”
data[data == u'好'] = 1
data[data == u'是'] = 1
data[data == u'高'] = 1
data[data != 1] = 0
x = data.iloc[:,:3].as_matrix().astype(int)
y = data.iloc[:,3].as_matrix().astype(int)
from keras.models import Sequential
from keras.layers.core import Dense, Activation
model = Sequential() #建立模型
model.add(Dense(input_dim = 3, output_dim = 10))
model.add(Activation('relu')) #用relu函数作为激活函数，能够大幅提供准确度
model.add(Dense(input_dim = 10, output_dim = 1))
model.add(Activation('sigmoid')) #由于是0-1输出，用sigmoid函数作为激活函数
model.compile(loss = 'binary_crossentropy', optimizer = 'adam', class_mode = 'binary')
#编译模型。由于我们做的是二元分类，所以我们指定损失函数为binary_crossentropy，以及模式为binary
#另外常见的损失函数还有mean_squared_error、categorical_crossentropy等，请阅读帮助文件。
#求解方法我们指定用adam，还有sgd、rmsprop等可选
model.fit(x, y, nb_epoch = 1000, batch_size = 10) #训练模型，学习一千次
yp = model.predict_classes(x).reshape(len(y)) #分类预测
from cm_plot import * #导入自行编写的混淆矩阵可视化函数
cm_plot(y,yp).show() #显示混淆矩阵可视化结果
错误提示
Using TensorFlow backend.
Traceback (most recent call last):
File "D:\python\chapter5\demo\code\5-3_neural_network.py", line 19, in
from keras.models import Sequential
File "C:\Python27\lib\site-packages\keras__init__.py", line 3, in
from . import utils
File "C:\Python27\lib\site-packages\keras\utils__init__.py", line 6, in
from . import conv_utils
File "C:\Python27\lib\site-packages\keras\utils\conv_utils.py", line 3, in
from .. import backend as K
File "C:\Python27\lib\site-packages\keras\backend__init__.py", line 83, in
from .tensorflow_backend import *
File "C:\Python27\lib\site-packages\keras\backend\tensorflow_backend.py", line 1, in
import tensorflow as tf
ImportError: No module named tensorflow
