print "nGuesses :", nGuess
if __name__ == "__main__":
imageReader = ImageReader()
nHiddenLayer = 3
hiddenLayerSize = 1000
lmbda = 0.01
maxIter = 400
hiddenLayerSizes = []
for i in range(0, nHiddenLayer):
hiddenLayerSizes.append(hiddenLayerSize)
layerSizes = [imageReader.n] + hiddenLayerSizes + [imageReader.nGesture]
(X, y), (Xv, yv) = imageReader.readImages()
print len(X), "training data"
print len(Xv), "test data"
model = trainModel(layerSizes, X, y, lmbda, maxIter)
print "nHiddenLayer :", nHiddenLayer
print "hiddenLayerSize :", hiddenLayerSize
print "lmbda :", lmbda
print "maxIter :", maxIter
print "Training",
testModel(model, X, y, imageReader.nGesture)
print "Test",
testModel(model, Xv, yv, imageReader.nGesture)
import time
import numpy as np
from imageReader import ImageReader
from keras.preprocessing.image import ImageDataGenerator
from cnn import CNN
if __name__ == "__main__":
imageReader = ImageReader()
cnn = CNN(imageReader.nGesture)
(X, y), (Xv, yv) = imageReader.readImages(make1d=False,
validationRatio=0.08)
print len(X), "training data"
print len(Xv), "test data"
imageGenerator = ImageDataGenerator()
cnn.train_gen(imageGenerator.flow(X, y, batch_size=16), len(X), Xv, yv)
trainAccuracy = cnn.test(X, y)
print "Training Accuracy :", trainAccuracy, "%"
valAccuracy = cnn.test(Xv, yv)
print "Test Accuracy :", valAccuracy, "%"
#imageGenerator = ImageDataGenerator()
#data = imageGenerator.flow_from_directory(directory='cropp', target_size=(100,100), color_mode='grayscale')
#test = imageGenerator.flow_from_directory(directory='ASLval', target_size=(100,100), color_mode='grayscale')
#cnn.train_gen(data, test)
#trainAccuracy = cnn.test_gen(data, 11296)
