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jblitzar
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github-python-corpus

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return data_pred_params
class PredDistToDataDistFactory(DiscreteDistributionFactory):
def __init__(self, data_dist_factory, min_variance, min_t=1e-6):
self.data_dist_factory = data_dist_factory
self.data_dist_factory.log_dev = False
self.min_variance = min_variance
self.min_t = min_t
def get_dist(self, params, input_params, t):
data_pred_params = noise_pred_params_to_data_pred_params(params, input_params[0], t, self.min_variance, self.min_t)
return self.data_dist_factory.get_dist(data_pred_params)
# <FILESEP>
from __future__ import print_function
import inbreast
import keras.backend as K
from roc_auc import RocAucScoreOp, PrecisionOp, RecallOp, F1Op
from roc_auc import AUCEpoch, PrecisionEpoch, RecallEpoch, F1Epoch, LossEpoch, ACCEpoch
#from keras.preprocessing.image import ImageDataGenerator
from image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten, BatchNormalization, SpatialDropout2D
from keras.layers import advanced_activations
from keras.layers import Convolution2D, MaxPooling2D
from keras.optimizers import SGD, Adam, RMSprop
from keras.utils import np_utils
import numpy as np
from keras.callbacks import ModelCheckpoint
from keras.regularizers import l1l2
import inbreast
#import googlenet
from convnetskeras.convnets import preprocess_image_batch, convnet
import os
from sklearn.metrics import roc_auc_score, roc_curve
np.random.seed(1)
#srng = RandomStreams(1)
fold = 4# 4
valfold = 2
lr = 5e-5#5e-5
nb_epoch = 500
batch_size = 80
l2factor = 1e-5
l1factor = 0#2e-7
usedream = False
weighted = False #True
noises = 50
data_augmentation = True
modelname = 'alexnet' # miccai16, alexnet, levynet, googlenet
pretrain = True
mil=True
savename = modelname+'_fd'+str(fold)+'_vf'+str(valfold)+'_lr'+str(lr)+'_l2'+str(l2factor)+'_l1'\
+str(l1factor)+'_ep'+str(nb_epoch)+'_bs'+str(batch_size)+'_w'+str(weighted)+'_dr'+str(usedream)+str(noises)+str(pretrain)+'_mil'+str(mil)
print(savename)
nb_classes = 2
# input image dimensions
img_rows, img_cols = 227, 227
# the CIFAR10 images are RGB
img_channels = 1
# the data, shuffled and split between train and test sets
trX, y_train, teX, y_test, teteX, y_test_test = inbreast.loaddataenhance(fold, 5, valfold=valfold)
trY = y_train.reshape((y_train.shape[0],1))
teY = y_test.reshape((y_test.shape[0],1))
teteY = y_test_test.reshape((y_test_test.shape[0],1))
print('tr, val, te pos num and shape')
print(trY.sum(), teY.sum(), teteY.sum(), trY.shape[0], teY.shape[0], teteY.shape[0])
ratio = trY.sum()*1./trY.shape[0]*1.
print('tr ratio'+str(ratio))
weights = np.array((ratio, 1-ratio))
#trYori = np.concatenate((1-trY, trY), axis=1)
#teY = np.concatenate((1-teY, teY), axis=1)
#teteY = np.concatenate((1-teteY, teteY), axis=1)
X_train = trX.reshape(-1, img_channels, img_rows, img_cols)
X_test = teX.reshape(-1, img_channels, img_rows, img_cols)
X_test_test = teteX.reshape(-1, img_channels, img_rows, img_cols)
print('tr, val, te mean, std')
print(X_train.mean(), X_test.mean(), X_test_test.mean())
# convert class vectors to binary class matrices
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)
Y_test_test = np_utils.to_categorical(y_test_test, nb_classes)
print('X_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'val samples')
print(X_test_test.shape[0], 'test samples')
model = Sequential()
lrelu = advanced_activations.LeakyReLU(alpha=0.1)
if modelname == 'alexnet':
X_train_extend = np.zeros((X_train.shape[0],3, 227, 227))
for i in xrange(X_train.shape[0]):
rex = np.resize(X_train[i,:,:,:], (227, 227))
X_train_extend[i,0,:,:] = rex
X_train_extend[i,1,:,:] = rex
X_train_extend[i,2,:,:] = rex
X_train = X_train_extend
X_test_extend = np.zeros((X_test.shape[0], 3,227, 227))
for i in xrange(X_test.shape[0]):
rex = np.resize(X_test[i,:,:,:], (227, 227))