def inputTest():
inputM = ioex.InputManager()
trainingData = inputM.getMnistTrainingData()
print(trainingData.images.shape)
print(trainingData.answers.shape)
# trainingData.shuffle()
sample = trainingData.getImageBatch(3)
print(sample)
print(trainingData.images.shape)
print(trainingData.answers.shape)
idx = inputM.selectNumber()
sample = trainingData.getSingleData(idx)
print("answer: " + str(sample.answer))
print(sample.getSize())
print("show picture?")
selected = inputM.selectYesOrNo()
if selected:
plt.imshow(sample.image, cmap=cm.gray)
plt.show()
return sample
def dataTest():
inputM = ioex.InputManager()
trainingData = inputM.getMnistTrainingData()
print("\ngetImageBatch")
for i in range(1, 11):
#計測スタート
start = time.time()
batch = trainingData.getImageBatch(50 * i)
#print(batch)
#print(batch.shape)
#計測終了
elapsed_time = time.time() - start
print(50 * i, end=": ")
print("elapsed_time:{0}".format(elapsed_time) + "[sec]")
print("\ngetAnswerVectorBatch")
for i in range(1, 11):
#計測スタート
start = time.time()
batch = trainingData.getAnswerVecotrBatch(50 * i)
#計測終了
elapsed_time = time.time() - start
print(50 * i, end=": ")
print("elapsed_time:{0}".format(elapsed_time) + "[sec]")
print("\nshaffle")
#計測スタート
start = time.time()
trainingData.shuffle()
#計測終了
elapsed_time = time.time() - start
print("elapsed_time:{0}".format(elapsed_time) + "[sec]")
def inputConvTest():
inputM = ioex.InputManager()
outputM = ioex.OutputManager()
trainingData = inputM.getMnistTrainingData()
sample = trainingData.getImageBatch(5)
outputM.showPictureFromBatch(sample, (28, 28))
inputLayer = layer.InputLayer(28 * 28)
conv = layer.ConvolutionalLayer(4, 3, inputLayer)
outputLayer = layer.OutputLayer(10, conv)
#conv.setWeight(np.array([[1,1,1,1,-8,1,1,1,1],[1,0,1,0,-4,0,1,0,1],[0,1,0,1,-4,1,0,1,0],[0,0,0,0,1,0,0,0,0]]))
inputLayer.setInputBatch(sample)
for i in range(0, 10000):
outputLayer.calculate()
if i % 1000 == 0:
outputM.showPictureFromBatch(conv.getOutput(), (28 * 4, 28))
print(conv.getWeight())
outputLayer.update(trainingData.getAnswerVecotrBatch(5))
import ioex
import layer
import util
print("### task1 ###")
# 入出力準備
inputM = ioex.InputManager()
outputM = ioex.OutputManager()
testingData = inputM.getMnistTestingData()
# ３層ニューラルネットワーク
inputLayer = layer.InputLayer(28 * 28)
hiddenLayer = layer.HiddenLayer(50, inputLayer, 1, 1)
outputLayer = layer.OutputLayer(10, hiddenLayer, 1, 1)
# ニューラルネットワークの設定
#outputLayer.setActivator(util.correctedSoftmax)
#入出力
loop = True
while (loop):
# 入力
targetNum = inputM.selectNumber()
sample = testingData.getSingleData(targetNum)
inputLayer.setInput(sample.getImage())
# 出力
result = outputLayer.calculate()
outputM.printMaxLikelihood(result)
print(result)