JBed

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JBed

JBed

# Inception-v3: http://arxiv.org/abs/1512.00567

from lasagne.layers import InputLayer
from lasagne.layers import Conv2DLayer
from lasagne.layers import Pool2DLayer
from lasagne.layers import DenseLayer
from lasagne.layers import GlobalPoolLayer
from lasagne.layers import ConcatLayer
from lasagne.layers.normalization import batch_norm
from lasagne.nonlinearities import softmax

JBed

from sklearn.cluster.k_means_ import _k_init 
from operator import itemgetter
import numpy as np

def quasi_random_sampling(X, n_samples=25):
    def row_norms(X, squared=True):
        squared_norms = (X**2).sum(axis=1)
        if squared: return squared_norms 
        else: return np.sqrt(squared_norms)

JBed

import re, math
from collections import Counter


def sentence_to_vector(text):
     words = WORD.findall(text)
     return Counter(words)

JBed

#PCA whitening involves finding the inverse square root of the covariance matrix 
#of a set of observations, which is prohibitively expensive when dealing 
#with natural images 

#starting with a path to a single image (img_path)
import numpy as np
from PIL import Image
from sklearn import preprocessing

JBed

# http://jmlr.org/papers/volume11/erhan10a/erhan10a.pdf
import cPickle as pickle
import numpy as np
 
from matplotlib import pyplot as plt
from os.path import join
from sklearn.cluster import KMeans
 
# download data here: http://www.cs.toronto.edu/~kriz/cifar.html
with open(join('data','cifar-10-batches-py','data_batch_1'),'rb') as f:

JBed

from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization

#AlexNet with batch normalization in Keras 
#input image is 224x224

model = Sequential()
model.add(Convolution2D(64, 3, 11, 11, border_mode='full'))