def test_imageloader():
data = ImageDataSet(28, 28)
data.loadImageData("digitdata/trainingimages", 1)
matrix = np.array(data.images[0])
print(matrix)
print(data.number)
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 ops():
testdata = ImageDataSet(28, 28)
testdata.loadImageData("digitdata/testimages", -1)
testdata.loadLabelData("digitdata/testlabels", testdata.number)
data = ImageDataSet(28, 28)
data.loadImageData("digitdata/trainingimages", -1)
data.loadLabelData("digitdata/traininglabels", data.number)
for t in range(20, 101, 20):
images, labels = data.shuffleout(t)
al = []
il = []
pc = Perceptron(28 * 28,
['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'])
for i in range(100):
pc.train(images, labels, 1, 0.8)
x = pc.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" % (t * 0.01 * data.number))
leg = plt.legend(ncol=2, shadow=True, fancybox=True)
leg.get_frame().set_alpha(0.5)
plt.xlim([1, 100])
plt.xlabel("trainning time")
plt.ylabel("accuracy")
plt.show()
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 dataloader_digit(n_train=-1, n_test=-1):
data = ImageDataSet(
28, 28, labeldomain=['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'])
data.loadImageData("digitdata/trainingimages", n_train)
data.loadLabelData("digitdata/traininglabels", data.number)
testdata = ImageDataSet(28, 28)
testdata.loadImageData("digitdata/testimages", n_test)
testdata.loadLabelData("digitdata/testlabels", testdata.number)
return data, testdata
def dataloader_face(n_train=-1, n_test=-1):
data = ImageDataSet(70, 60, labeldomain=['0', '1'])
data.loadImageData("facedata/facedatatrain", n_train)
data.loadLabelData("facedata/facedatatrainlabels", data.number)
testdata = ImageDataSet(70, 60)
testdata.loadImageData("facedata/facedatatest", n_test)
testdata.loadLabelData("facedata/facedatatestlabels", testdata.number)
return data, testdata
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 dataloader_face(Tranum=-1, Testnum=-1):
data = ImageDataSet(70, 60, labeldomain=['0', '1'])
# data.loadImageData("facedata/facedatatrain", Tranum)
# data.loadLabelData("facedata/facedatatrainlabels", data.number)
data.loadImageData("facedata/facedatatrain", Tranum)
data.loadLabelData("facedata/facedatatrainlabels", data.number)
Data_test = ImageDataSet(70, 60)
Data_test.loadImageData("facedata/facedatavalidation", Testnum)
Data_test.loadLabelData("facedata/facedatavalidationlabels",
Data_test.number)
# Data_test.loadImageData("facedata/facedatatest", Testnum)
# Data_test.loadLabelData("facedata/facedatatestlabels", Data_test.number)
return data, Data_test
def dataloader_digit(Tranum=-1, Testnum=-1):
data = ImageDataSet(
28, 28, labeldomain=['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'])
# data.loadImageData("digitdata/trainingimages", Tranum)
# data.loadLabelData("digitdata/traininglabels", data.number)
data.loadImageData("digitdata/trainingimages", Tranum)
data.loadLabelData("digitdata/traininglabels", data.number)
Data_test = ImageDataSet(28, 28)
Data_test.loadImageData("digitdata/validationimages", Testnum)
Data_test.loadLabelData("digitdata/validationlabels", Data_test.number)
# Data_test.loadImageData("digitdata/testimages", Testnum)
# Data_test.loadLabelData("digitdata/testlabels", Data_test.number)
return data, Data_test