def testTrainingOnSepervisedDataset(self):
DS = SupervisedDataSet(2, 1)
DS.addSample([ 0, 0 ] , [0])
DS.addSample([ 0, 1 ] , [1])
DS.addSample([ 1, 0 ] , [1])
DS.addSample([ 1, 1 ] , [0])
network = N = buildNetwork(2, 4, 1)
trainer = BackpropTrainer(N, learningrate = 0.01, momentum = 0.99)
trainer.verbose = False
nnf = NeuralNetworkFactory(network, trainer, seed=2, iterationsNum=500)
nnClassifier = nnf.buildClassifier(DS)
self.assertAlmostEqual(nnClassifier.getPrediction([0, 0]), 0, delta=0.01)
self.assertAlmostEqual(nnClassifier.getPrediction([0, 1]), 1, delta=0.01)
self.assertAlmostEqual(nnClassifier.getPrediction([1, 0]), 1, delta=0.01)
self.assertAlmostEqual(nnClassifier.getPrediction([1, 1]), 0, delta=0.01)
def load_image_vector(img_path):
M = cv2.imread(img_path, cv2.CV_LOAD_IMAGE_GRAYSCALE)
M = cv2.resize(M, (IM_WIDTH, IM_HEIGHT))
M = M.reshape((1, IM_AREA))
return M[0]
def save_network_state(signum, stack):
file_name = "autoencoder_100px_300faces_{}{}".format(int(time()), randrange(0, 10))
f = open(file_name, "wb")
pickle.dump(network, f)
f.close()
print "network saved as : {}".format(file_name)
network = buildNetwork(IM_AREA, IM_AREA * 2 // 3, IM_AREA)
dataset = SupervisedDataSet(IM_AREA, IM_AREA)
for img_name in os.listdir(IMAGES_PATH):
try:
vector = load_image_vector(IMAGES_PATH + img_name)
if vector != None and len(vector) == IM_AREA:
dataset.addSample(vector, vector)
except:
continue
print "dataset with {} elements done...".format(dataset.getLength())
signal.signal(signal.SIGUSR1, save_network_state)
trainer = BackpropTrainer(network, dataset)
#preload all images
images = []
for img_name in os.listdir(IMAGES_PATH):
try:
vector = load_image_vector(IMAGES_PATH + img_name)
if vector != None and len(vector) == IM_AREA:
images.append(vector)
except:
continue
print "dataset with {} elements done...".format(len(images))
signal.signal(signal.SIGUSR1, save_network_state)
#stochastic training
for i in range(len(images) // 2):
img = choice(images)
dataset = SupervisedDataSet(IM_AREA, IM_AREA)
dataset.addSample(img, img)
trainer = BackpropTrainer(network, dataset)
trainer.train()
print "training completed... (1 pass)"
save_network_state(0, 0)
print "done !! :D"
def loadNetwork(filename):
fileObject = open(filename,'r')
return pickle.load(fileObject)
if __name__ == '__main__':
# read the training file
print "Reading in training file..."
raw_data = numpy.genfromtxt('mydata.csv', delimiter=",")
numFeatures = raw_data[0].size
numData = raw_data.size / numFeatures
# initialize the neural network
print "Initializing neural network..."
net = buildNetwork(numFeatures - 1, numFeatures - 1, 2, bias=True)
ds = SupervisedDataSet(numFeatures - 1, 2)
# add the input to the dataset
print "Loading the dataset"
for i in range(0, numData):
data = raw_data[i]
features = tuple(data[0:numFeatures - 1])
label = int(data[numFeatures - 1])
ds.addSample(features, (label,))
# train the network on the dataset
print "Training neural net"
trainer = BackpropTrainer(net, ds)
for i in range(0, 10):
print "error: " + str(trainer.train())
"difficulty",
"miners-revenue",
"blocks-size",
] # "avg-confirmation-time"
# features = ["market-price", "transaction-fees", "n-transactions", "estimated-transaction-volume-usd"]
(train, test, trainOutput, testOutput) = getData(features)
# take out yesterday's data for later
# x_yesterday = X[len(X)-1]
# y_yesterday = y[len(y)-1]
# X = X[:len(X)-1]
# y = y[:len(y)-1]
num_features = len(features)
print len(train), len(test)
d = SupervisedDataSet(len(features) - 1, 1)
# d = ClassificationDataSet(len(features)-1)
for i in range(len(train)):
# print(tuple(x[i]),tuple(y[i],))
d.addSample(tuple(train[i]), tuple(trainOutput[i]))
# for sample_x,sample_y in x,y: d.addSample(sample_x,sample_y)
# d.setField('class', y)
# nn = buildNetwork(len(features)-1, len(features)-1, 1)
nn = FeedForwardNetwork()
inLayer = LinearLayer(len(features) - 1)
hiddenLayer = SigmoidLayer(len(features) - 1)
outLayer = LinearLayer(1)
nn.addInputModule(inLayer)
nn.addModule(hiddenLayer)
nn.addOutputModule(outLayer)
