m,_ = x.shape
p = np.zeros((m,1),dtype='uint8')
for i in xrange(m):
h = hypothesis(x[i],theta)
s = np.sum(h)
l = np.argmax(h)
if (1-s)<h[l]:
p[i][0] = l
else:
p[i][0] = 9
return p
m,rows,cols,xTrain,yTrain = mnistData.getTrainData()
mTest,_,_,xTest,yTest = mnistData.getTestData()
k = 10
xTrain = xTrain.reshape((m,rows*cols))
xTrain = addColumn(xTrain)
xTrain = xTrain/255
xTest = xTest.reshape((mTest,rows*cols))
xTest = addColumn(xTest)
xTest = xTest/255
theta = np.zeros((k-1,rows*cols+1))
iterations = 250
alpha = .2
for i in xrange(iterations):
theta = gradientAcsent(xTrain,yTrain,theta,alpha,m)
print 'iteration = {}'.format(i+1)
pTrain = predict(xTrain,theta)
print 'train error = {}'.format(np.sum(pTrain!=yTrain)/float(m))
import numpy as np
import mnistData
from neuralNetworks import NeuralNetwork
def createY(y, k, m):
Y = np.zeros((m, k), dtype=int)
temp = np.array(range(m))
Y[temp, y[:, 0]] = 1
return Y
m, rows, cols, x_train, y = mnistData.getTrainData()
y_train = createY(y, 10, m)
x_train = x_train.reshape((m, rows * cols))
mtest, rtest, ctest, x_test, y_t = mnistData.getTestData()
y_test = createY(y_t, 10, mtest)
x_test = x_test.reshape((mtest, rtest * ctest))
nn = NeuralNetwork('mnist', rows * cols, 1, [50], 10, .1)
nn.train(x_train, y_train, 250, .1)
count = 0
for x, y in zip(x_test, y_t):
res = nn.predict(x)
if res == y[0]:
count += 1
print count
import numpy as np
import mnistData
from neuralNetworks import NeuralNetwork
def createY(y,k,m):
Y = np.zeros((m,k),dtype=int)
temp = np.array(range(m))
Y[temp,y[:,0]] = 1
return Y
m,rows,cols,x_train,y = mnistData.getTrainData()
y_train = createY(y,10,m)
x_train = x_train.reshape((m,rows*cols))
mtest,rtest,ctest,x_test,y_t = mnistData.getTestData()
y_test = createY(y_t,10,mtest)
x_test = x_test.reshape((mtest,rtest*ctest))
nn = NeuralNetwork('mnist',rows*cols,1,[50],10,.1)
nn.train(x_train,y_train,250,.1)
count = 0
for x,y in zip(x_test,y_t):
res = nn.predict(x)
if res==y[0]:
count+=1
print count
