【Python-ML】神经网络-深度学习库Keras

# -*- coding: utf-8 -*-
'''
Created on 2018年1月26日
@author: Jason.F
@summary: pip install Keras
Keras 神经网络训练库，可使用GPU加速神经网络的训练，基于张量库Theano支持多维数组处理
'''
import numpy as np
import time
import os
import struct
import theano
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers.core import Dense
from keras.optimizers import SGD

def load_mnist(path,kind='train'):
#load mnist data from path
labels_path = os.path.join(path,'%s-labels.idx1-ubyte'%kind)
images_path = os.path.join(path,'%s-images.idx3-ubyte'%kind)
with open(labels_path,'rb') as lbpath:
magic,n =struct.unpack('>II',lbpath.read(8))
labels = np.fromfile(lbpath,dtype = np.uint8)
with open(images_path,'rb') as imgpath:
magic,num,rows,cols =struct.unpack('>IIII',imgpath.read(16))
images = np.fromfile(imgpath,dtype = np.uint8).reshape(len(labels),784)#28X28像素
return images,labels

if __name__ == "__main__":
start = time.clock()

#导入数据集
homedir = os.getcwd()#获取当前文件的路径
X_train,y_train = load_mnist(homedir+'\\mnist', kind='train')
print ('Rows:%d,columns:%d'%(X_train.shape[0],X_train.shape[1]))
X_test,y_test = load_mnist(homedir+'\\mnist', kind='t10k')
print ('Rows:%d,columns:%d'%(X_test.shape[0],X_test.shape[1]))
#将MNIST图像的数组转换为32位浮点数格式
theano.config.floatX='float32'
X_train = X_train.astype(theano.config.floatX)
X_test = X_test.astype(theano.config.floatX)
#onehot编码
print ('First 3 labels:',y_train[:3])
y_train_ohe = np_utils.to_categorical(y_train)
print('\nFirst 3 labels(one-hot):\n',y_train_ohe[:3])
#实现神经网络，隐层使用双曲正切函数，输出层使用softmax函数
np.random.seed(1)
model = Sequential()
model.add(Dense(input_dim=X_train.shape[1],output_dim=50,init='uniform',activation='tanh'))
model.add(Dense(input_dim=50,output_dim=50,init='uniform',activation='tanh'))
model.add(Dense(input_dim=50,output_dim=y_train_ohe.shape[1],init='uniform',activation='tanh'))
sgd =SGD(lr=0.001,decay=1e-7,momentum=.9)
model.compile(loss='categorical_crossentropy',optimizer=sgd)
model.fit(X_train,y_train_ohe,nb_epoch=50,batch_size=300,verbose=1,validation_split=0.1)#show_accuracy=True
y_train_pred = model.predict_classes(X_train,verbose=0)
print ('First 3 predictions:',y_train_pred[:3])
train_acc = np.sum(y_train==y_train_pred,axis=0)/float(X_train.shape[0])
print ('Training accuracy:%.2f%%'%(train_acc*100))
y_test_pred=model.predict_classes(X_test,verbose=0)
test_acc = np.sum(y_test==y_test_pred,axis=0)/float(X_test.shape[0])
print ('Test accuracy:%.2f%%'%(test_acc*100))

end = time.clock()
print('finish all in %s' % str(end - start))

设置：

windows，pip install keras后，到C:\Users\user\.keras目录下找到keras.json文件，默认设置如下图：



修改backend为：





结果：

Training accuracy:13.63%
Test accuracy:13.45%
finish all in 13968.1836241

执行一次好长时间，准确率这么低要找下原因。