def choose_mnist(self):
print "CHOSE MNIST"
global predictor, autoencode_model, embeddings
predictor = autoencode_predict.predict(
name="meta-data/mnist/autoencode_model", color_depth=1)
predictor.stop()
predictor.restore()
autoencode_model = predictor.autoencode_model
embeddings = Embeddings(predictor)
print "Loading images ..."
if 'mnist' not in self.data_sets:
print "Key missing. Building ImageData"
imageData = LazyLoadWrapper(
BatchWrapper(
ResizeWrapper(ReshapeWrapper(Mnist(), [28, 28, 1]),
[32, 32])))
imageData.getImages()
self.data_sets['mnist'] = imageData
print " mnist shape is", self.data_sets['mnist'].getImages().shape
print "... loading images done"
embeddings.data_set = self.data_sets['mnist'].getImages()
return self.data_sets['mnist']
def choose_garden(self):
print "CHOSE GARDEN"
global predictor, autoencode_model, embeddings
predictor = autoencode_predict.predict(
name="meta-data/garden/garden_model", color_depth=3)
predictor.stop()
predictor.restore()
autoencode_model = predictor.autoencode_model
embeddings = Embeddings(predictor)
config_data = json.load(open("data/file_data.json", "r"))
print "Loading images ..."
if 'garden' not in self.data_sets:
print "Key missing. Building ImageData"
print "Loading files ...",
files = LazyLoadWrapper(
ResizeWrapper(
FileReader(config_data["file_names"],
config_data["labels"]), [64, 64]))
files.init()
print "done."
print "Calculating full size ...",
full_size = LazyLoadWrapper(ResizeWrapper(files, [32, 32]))
full_size.init()
print "done."
print "Calculating half size ...",
half_size = LazyLoadWrapper(SliceWrapper(files, 32, 16))
half_size.init()
print "done."
print "Calculating concat the whole thing ...",
self.data_sets['garden'] = LazyLoadWrapper(
BatchWrapper(ConcatWrapper([full_size, half_size])))
print "done."
self.data_sets['garden'].getImages()
print " garden shape is", self.data_sets['garden'].getImages().shape
print "... loading images done"
embeddings.data_set = self.data_sets['garden'].getImages()
return self.data_sets['garden']
import numpy as np
import label_predict
from embeddings import Embeddings
import nearest_neighbour
from data_source import LazyLoadWrapper, ResizeWrapper, ReshapeWrapper, Mnist
embeddings = Embeddings()
imageData = LazyLoadWrapper(
ResizeWrapper(ReshapeWrapper(Mnist(False), [28, 28, 1]), [32, 32]))
embeddings.data_set = imageData.getImages()
number = 4
print "The number is", imageData.getLabels()[number]
nearest = nearest_neighbour.byIndex(number,
embeddings.getEmbeddings(),
size=200)
result = zip(imageData.getLabels()[nearest], nearest)
nearest = []
negative_examples = []
for label, data_index in result:
label = np.argmax(label)
if label == 4:
nearest.append(data_index)
else:
negative_examples.append(data_index)
print "Pretend labeling the first", len(nearest), " ..."