train_images = train_images.transpose()
test_images = test_images.transpose()
groundTruth = np.zeros((10, train_images.shape[1]))
q = np.arange(0, train_images.shape[1])
groundTruth[trian_labels.transpose(), q] = 1
architect = [784, 500, 10]
option = {}
a = NNC(architect, option)
#start=clock()
#for i in range(4):
# a.test()
#finish=clock()
#print (finish-start)/10000
a.learningRate = 1
a.weightPenaltyL2 = 0.0001
a.nonSparsityPenalty = 0.0001
a.dropoutFraction = 0.1
a.inputZeroMaskedFraction = 0.1
opts = {'batchsize': 100, 'numepochs': 10}
a.output = 'softmax'
a.train(train_images, groundTruth, opts)
result = a.nnpred(test_images)
writer = csv.writer(file('nnpredict.csv', 'wb'))
writer.writerow(['ImageId', 'Label'])
i = 1
for p in result:
writer.writerow([i, p])
i = i + 1
#print np.array(c[0:6]).reshape(2,3)
im = q.ae[0].W[0][1:,:][:,i].reshape(28, 28)
plotwindow = fig.add_subplot(y_length, x_length, i + 1)
plt.imshow(im , cmap='gray')
plt.show()
architect = [784,200,10];
option ={}
a=NNC(architect,option)
groundTruth=a.handle_y_4classify(trian_labels);
#start=clock()
#for i in range(4):
# a.test()
#finish=clock()
#print (finish-start)/10000
a.learningRate=0.4
a.weightPenaltyL2 = 0.0001
opts={'batchsize':100,'numepochs':4}
a.output='softmax'
a.activation ='tanh'
a.W[0]=q.ae[0].W[0]
a.train(train_images,groundTruth,opts)
qq=a.nnpred(test_images)
print qq
print test_labels
tests=np.zeros((1,num_test_case)).reshape(1,-1);
tests[qq.reshape(1,-1)==test_labels.reshape(1,-1)]=1
print np.sum(tests)/num_test_case
#print np.array(c[0:6]).reshape(2,3)
test_images=test_images.transpose()
groundTruth=np.zeros((10,train_images.shape[1]))
q=np.arange(0,train_images.shape[1])
groundTruth[trian_labels.transpose(),q]=1
architect = [784,500,10];
option ={}
a=NNC(architect,option)
#start=clock()
#for i in range(4):
# a.test()
#finish=clock()
#print (finish-start)/10000
a.learningRate=1
a.weightPenaltyL2 = 0.0001
a.nonSparsityPenalty = 0.0001
a.dropoutFraction=0.1
a.inputZeroMaskedFraction=0.1
opts={'batchsize':100,'numepochs':10}
a.output='softmax'
a.train(train_images,groundTruth,opts)
result=a.nnpred(test_images)
writer = csv.writer(file('nnpredict.csv', 'wb'))
writer.writerow(['ImageId', 'Label'])
i=1
for p in result:
writer.writerow([i,p])
i=i+1
#print np.array(c[0:6]).reshape(2,3)
