def test_nueralnetwork(traindata, testdata):
times = int(input("max trainning time for one data set :"))
ratio = float(input("learning ratio: "))
totalnumber = traindata.number
#first - try with ordered datas
for p in range(10, 101, 10):
images, labels = traindata.orderedout(p)
al = []
il = []
nn = NeuralNetwork((traindata.width * traindata.height, 15, 15,
len(traindata.labeldomain)), traindata.labeldomain)
for i in range(times):
print("Train %d time" % i)
nn.train(images, labels, 1, ratio)
x = nn.classify(testdata.images)
a = Accuracy(x, testdata.labels)
al.append(a * 100)
il.append(i + 1)
print(a * 100)
plt.plot(il, al, label="size=%d" % (p * 0.01 * totalnumber))
leg = plt.legend(ncol=1, shadow=True, fancybox=True)
leg.get_frame().set_alpha(0.5)
plt.xlim([1, times])
plt.xlabel("trainning time")
plt.ylabel("accuracy")
plt.show()
def layface():
data = ImageDataSet(70, 60)
data.loadImageData("facedata/facedatatrain", -1)
data.loadLabelData("facedata/facedatatrainlabels", data.number)
nn = NeuralNetwork((70 * 60, 30, 25, 25, 2), ['0', '1'])
testdata = ImageDataSet(70, 60)
testdata.loadImageData("facedata/facedatatest", -1)
testdata.loadLabelData("facedata/facedatatestlabels", testdata.number)
for _ in range(50):
print("Time:%d" % _)
nn.train(data.images, data.labels, 2, 0.5)
x = nn.classify(testdata.images)
a = Accuracy(x, testdata.labels)
print()
print("Accuracy is : %d" % (a * 100))
def lay():
data = ImageDataSet(28, 28)
data.loadImageData("digitdata/trainingimages", -1)
data.loadLabelData("digitdata/traininglabels", data.number)
nn = NeuralNetwork((28 * 28, 28, 14, 10),
['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'])
testdata = ImageDataSet(28, 28)
testdata.loadImageData("digitdata/testimages", -1)
testdata.loadLabelData("digitdata/testlabels", testdata.number)
for _ in range(50):
print("Time:%d" % _)
nn.train(data.images, data.labels, 1, 0.3)
x = nn.classify(testdata.images)
a = Accuracy(x, testdata.labels)
print()
print("Accuracy is : %d" % (a * 100))
def nntest():
data = ImageDataSet(28, 28)
data.loadImageData("digitdata/trainingimages", 20)
data.loadLabelData("digitdata/traininglabels", data.number)
#print( np.array(data.images[0]) / 2 )
nn = NeuralNetwork(28 * 28, 15,
['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'])
nn.train(data.images, data.labels, 50)
testdata = ImageDataSet(28, 28)
testdata.loadImageData("digitdata/testimages", 3)
testdata.loadLabelData("digitdata/testlabels", testdata.number)
x = nn.classify(testdata.images)
print(testdata.labels)
print(x)
a = Accuracy(x, testdata.labels)
print()
print("Accuracy is : %d" % (a * 100))
def test_nueralnetwork_w(traindata, testdata, times, ratio, file):
totalnumber = traindata.number
#first - try with ordered datas
j = []
for p in range(10, 101, 10):
images, labels = traindata.orderedout(p)
al = []
il = []
nn = NeuralNetwork((traindata.width * traindata.height, 15, 15,
len(traindata.labeldomain)), traindata.labeldomain)
for i in range(times):
print("Train %d time" % i)
nn.train(images, labels, 1, ratio)
x = nn.classify(testdata.images)
a = Accuracy(x, testdata.labels)
al.append(a * 100)
il.append(i + 1)
print(a * 100)
j.append((al, il))
import json
with open(file, 'w') as f:
json.dump(j, f)
def stdmean():
limit = 0.7
ratio = 0.8
times = 5
print("digit")
traindata, testdata = dataloader_digit()
sal = []
mal = []
pal = []
for p in range(10, 101, 10):
al = []
il = []
for i in range(times):
images, labels = traindata.shuffleout(p)
pc = Perceptron(traindata.width * traindata.height,
traindata.labeldomain)
pc.train(images, labels, 3, ratio)
x = pc.classify(testdata.images)
a = Accuracy(x, testdata.labels)
al.append(a * 100)
il.append(i + 1)
print(a * 100)
sal.append(np.std(al))
mal.append(np.mean(al))
pal.append(p)
plt.plot(pal, sal, label="digitdata Perceptron std")
plt.plot(pal, mal, label="digitdata Perceptron mean")
feature_domians = [[i for i in np.arange(0, 1.1, 0.5)]
for _ in range(traindata.width * traindata.height)]
sal = []
mal = []
pal = []
for p in range(10, 101, 10):
al = []
for i in range(3):
images, labels = traindata.shuffleout(p)
nb = NaiveBayes(feature_domians, traindata.labeldomain, 1)
nb.train(images, labels)
x = nb.classify(testdata.images)
a = Accuracy(x, testdata.labels)
al.append(a * 100)
sal.append(np.std(al))
mal.append(np.mean(al))
pal.append(p)
print(a)
plt.plot(pal, sal, label="digitdata NaiveBayes std")
plt.plot(pal, mal, label="digitdata NaiveBayes mean")
sal = []
mal = []
pal = []
for p in range(10, 101, 10):
al = []
il = []
for i in range(times):
images, labels = traindata.shuffleout(p)
pc = NeuralNetwork((traindata.width * traindata.height, 15, 15,
len(traindata.labeldomain)),
traindata.labeldomain)
pc.train(images, labels, 50, ratio)
x = pc.classify(testdata.images)
a = Accuracy(x, testdata.labels)
al.append(a * 100)
il.append(i + 1)
sal.append(np.std(al))
mal.append(np.mean(al))
pal.append(p)
print(a)
plt.plot(pal, sal, label="digitdata NeuralNetwork std")
plt.plot(pal, mal, label="digitdata NeuralNetwork mean")
print("face")
traindata, testdata = dataloader_face()
sal = []
mal = []
pal = []
for p in range(10, 101, 10):
al = []
il = []
for i in range(times):
images, labels = traindata.shuffleout(p)
pc = Perceptron(traindata.width * traindata.height,
traindata.labeldomain)
pc.train(images, labels, 3, ratio)
x = pc.classify(testdata.images)
a = Accuracy(x, testdata.labels)
al.append(a * 100)
il.append(i + 1)
print(a * 100)
sal.append(np.std(al))
mal.append(np.mean(al))
pal.append(p)
plt.plot(pal, sal, label="facedata Perceptron std")
plt.plot(pal, mal, label="facedata Perceptron mean")
feature_domians = [[i for i in np.arange(0, 1.1, 0.5)]
for _ in range(traindata.width * traindata.height)]
sal = []
mal = []
pal = []
for p in range(10, 101, 10):
al = []
for i in range(3):
images, labels = traindata.shuffleout(p)
nb = NaiveBayes(feature_domians, traindata.labeldomain, 1)
nb.train(images, labels)
x = nb.classify(testdata.images)
a = Accuracy(x, testdata.labels)
al.append(a * 100)
sal.append(np.std(al))
mal.append(np.mean(al))
pal.append(p)
print(a)
plt.plot(pal, sal, label="facedata NaiveBayes std")
plt.plot(pal, mal, label="facedata NaiveBayes mean")
sal = []
mal = []
pal = []
for p in range(10, 101, 10):
al = []
il = []
for i in range(times):
images, labels = traindata.shuffleout(p)
pc = NeuralNetwork((traindata.width * traindata.height, 15, 15,
len(traindata.labeldomain)),
traindata.labeldomain)
pc.train(images, labels, 50, ratio)
x = pc.classify(testdata.images)
a = Accuracy(x, testdata.labels)
al.append(a * 100)
il.append(i + 1)
sal.append(np.std(al))
mal.append(np.mean(al))
pal.append(p)
print(a)
plt.plot(pal, sal, label="facedata NeuralNetwork std")
plt.plot(pal, mal, label="facedata NeuralNetwork mean")
leg = plt.legend(ncol=1, shadow=True, fancybox=True)
leg.get_frame().set_alpha(0.5)
plt.xlabel("data size precentage")
plt.ylabel("time(in second)")
plt.show()
def timeana():
import time
limit = 0.7
ratio = 1
times = 200
print("digit")
traindata, testdata = dataloader_digit()
fal = []
pal = []
for p in range(20, 101, 10):
images, labels = traindata.orderedout(p)
al = []
il = []
start = time.time()
pc = Perceptron(traindata.width * traindata.height,
traindata.labeldomain)
for i in range(times):
pc.train(images, labels, 1, ratio)
x = pc.classify(testdata.images)
a = Accuracy(x, testdata.labels)
al.append(a * 100)
il.append(i + 1)
print(a * 100)
if a > limit:
end = time.time()
break
fal.append(end - start)
pal.append(p)
plt.plot(pal, fal, label="digitdata Perceptron")
feature_domians = [[i for i in np.arange(0, 1.1, 0.5)]
for _ in range(traindata.width * traindata.height)]
fal = []
pal = []
for p in range(20, 101, 10):
start = time.time()
nb = NaiveBayes(feature_domians, traindata.labeldomain, 1)
images, labels = traindata.orderedout(p)
nb.train(images, labels)
x = nb.classify(testdata.images)
a = Accuracy(x, testdata.labels)
end = time.time()
fal.append(end - start)
pal.append(p)
print(a)
plt.plot(pal, fal, label="digitdata NaiveBayes")
fal = []
pal = []
for p in range(20, 101, 10):
images, labels = traindata.orderedout(p)
al = []
il = []
start = time.time()
pc = NeuralNetwork((traindata.width * traindata.height, 15, 15,
len(traindata.labeldomain)), traindata.labeldomain)
for i in range(times):
pc.train(images, labels, 1, ratio)
x = pc.classify(testdata.images)
a = Accuracy(x, testdata.labels)
al.append(a * 100)
il.append(i + 1)
print(a * 100)
if a > limit:
end = time.time()
break
fal.append(end - start)
pal.append(p)
plt.plot(pal, fal, label="digitdata NeuralNetwork")
print("face")
traindata, testdata = dataloader_face()
fal = []
pal = []
for p in range(20, 101, 10):
images, labels = traindata.orderedout(p)
al = []
il = []
start = time.time()
pc = Perceptron(traindata.width * traindata.height,
traindata.labeldomain)
for i in range(times):
pc.train(images, labels, 1, ratio)
x = pc.classify(testdata.images)
a = Accuracy(x, testdata.labels)
al.append(a * 100)
il.append(i + 1)
print(a * 100)
if a > limit:
end = time.time()
break
fal.append(end - start)
pal.append(p)
plt.plot(pal, fal, label="facedata Perceptron")
feature_domians = [[i for i in np.arange(0, 1.1, 0.5)]
for _ in range(traindata.width * traindata.height)]
fal = []
pal = []
for p in range(20, 101, 10):
start = time.time()
nb = NaiveBayes(feature_domians, traindata.labeldomain, 1)
images, labels = traindata.orderedout(p)
nb.train(images, labels)
x = nb.classify(testdata.images)
a = Accuracy(x, testdata.labels)
end = time.time()
fal.append(end - start)
pal.append(p)
print(a)
plt.plot(pal, fal, label="facedata NaiveBayes")
fal = []
pal = []
for p in range(20, 101, 10):
images, labels = traindata.orderedout(p)
al = []
il = []
start = time.time()
pc = NeuralNetwork((traindata.width * traindata.height, 15, 15,
len(traindata.labeldomain)), traindata.labeldomain)
for i in range(times):
pc.train(images, labels, 1, ratio)
x = pc.classify(testdata.images)
a = Accuracy(x, testdata.labels)
al.append(a * 100)
il.append(i + 1)
print(a * 100)
if a > limit:
end = time.time()
break
fal.append(end - start)
pal.append(p)
plt.plot(pal, fal, label="facedata NeuralNetwork")
leg = plt.legend(ncol=1, shadow=True, fancybox=True)
leg.get_frame().set_alpha(0.5)
plt.xlabel("data size precentage")
plt.ylabel("time(in second)")
plt.show()
def test_nn_argmax_all():
print("Initializing nn")
times = 300
ratio = 0.6
traindata, testdata = dataloader_digit()
#first - try with ordered datas
fal = []
pal = []
for p in range(10, 101, 10):
images, labels = traindata.orderedout(p)
al = []
il = []
pc = NeuralNetwork((traindata.width * traindata.height, 15, 15,
len(traindata.labeldomain)), traindata.labeldomain)
for i in range(times):
pc.train(images, labels, 1, ratio)
x = pc.classify(testdata.images)
a = Accuracy(x, testdata.labels)
al.append(a * 100)
il.append(i + 1)
print(a * 100)
fal.append(max(al))
pal.append(p)
plt.plot(pal, fal, label="digitdata ordered")
fal = []
pal = []
for p in range(10, 101, 10):
images, labels = traindata.shuffleout(p)
al = []
il = []
pc = NeuralNetwork((traindata.width * traindata.height, 15, 15,
len(traindata.labeldomain)), traindata.labeldomain)
for i in range(times):
pc.train(images, labels, 1, ratio)
x = pc.classify(testdata.images)
a = Accuracy(x, testdata.labels)
al.append(a * 100)
il.append(i + 1)
print(a * 100)
fal.append(max(al))
pal.append(p)
plt.plot(pal, fal, label="digitdata random")
traindata, testdata = dataloader_face()
#first - try with ordered datas
fal = []
pal = []
for p in range(10, 101, 10):
images, labels = traindata.orderedout(p)
al = []
il = []
pc = NeuralNetwork((traindata.width * traindata.height, 15, 15,
len(traindata.labeldomain)), traindata.labeldomain)
for i in range(times):
pc.train(images, labels, 1, ratio)
x = pc.classify(testdata.images)
a = Accuracy(x, testdata.labels)
al.append(a * 100)
il.append(i + 1)
print(a * 100)
fal.append(max(al))
pal.append(p)
plt.plot(pal, fal, label="facedata ordered")
#first - try with ordered datas
fal = []
pal = []
for p in range(10, 101, 10):
images, labels = traindata.shuffleout(p)
al = []
il = []
pc = NeuralNetwork((traindata.width * traindata.height, 15, 15,
len(traindata.labeldomain)), traindata.labeldomain)
for i in range(times):
pc.train(images, labels, 1, ratio)
x = pc.classify(testdata.images)
a = Accuracy(x, testdata.labels)
al.append(a * 100)
il.append(i + 1)
print(a * 100)
fal.append(max(al))
pal.append(p)
plt.plot(pal, fal, label="facedata random")
leg = plt.legend(ncol=1, shadow=True, fancybox=True)
leg.get_frame().set_alpha(0.5)
plt.xlabel("data size precentage")
plt.ylabel("accuracy")
plt.show()