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/Library/Java/JavaVirtualMachines/jdk1.7.0_25.jdk/Contents/Home/bin/java -Xmx10g -Didea.launcher.port=7532 "-Didea.launcher.bin.path=/Applications/IntelliJ IDEA 14 CE.app/Contents/bin" -Dfile.encoding=UTF-8 -classpath "/Library/Java/JavaVirtualMachines/jdk1.7.0_25.jdk/Contents/Home/lib/ant-javafx.jar:/Library/Java/JavaVirtualMachines/jdk1.7.0_25.jdk/Contents/Home/lib/dt.jar:/Library/Java/JavaVirtualMachines/jdk1.7.0_25.jdk/Contents/Home/lib/javafx-doclet.jar:/Library/Java/JavaVirtualMachines/jdk1.7.0_25.jdk/Contents/Home/lib/javafx-mx.jar:/Library/Java/JavaVirtualMachines/jdk1.7.0_25.jdk/Contents/Home/lib/jconsole.jar:/Library/Java/JavaVirtualMachines/jdk1.7.0_25.jdk/Contents/Home/lib/sa-jdi.jar:/Library/Java/JavaVirtualMachines/jdk1.7.0_25.jdk/Contents/Home/lib/tools.jar:/Library/Java/JavaVirtualMachines/jdk1.7.0_25.jdk/Contents/Home/jre/lib/charsets.jar:/Library/Java/JavaVirtualMachines/jdk1.7.0_25.jdk/Contents/Home/jre/lib/deploy.jar:/Library/Java/JavaVirtualMachines/jdk1.7.0_25.jdk/Contents/Home/jre/lib/h

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elastix is started at Fri Mar 25 16:08:57 2016.

-------------------------------------------------------------------------

Running elastix with parameter map 0
Current time: Fri Mar 25 16:08:57 2016.
Installing all components.
InstallingComponents was successful.

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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [

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#to_categorical() acquired from Keras implementation
#dice_coef() inspired by Marco Jocic's implementation: https://github.com/jocicmarko/ultrasound-nerve-segmentation/blob/master/train.py


def to_categorical(y, nb_classes=None):
    """Converts a class vector (integers) to binary class matrix.
    E.g. for use with categorical_crossentropy.
    # Arguments
        y: class vector to be converted into a matrix
            (integers from 0 to nb_classes).

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_author__ = 'Fabian Isensee'

from collections import OrderedDict
from lasagne.layers import InputLayer, ConcatLayer, Pool2DLayer, ReshapeLayer, DimshuffleLayer, NonlinearityLayer, DropoutLayer, Upscale2DLayer, Upscale3DLayer, BatchNormLayer, batch_norm
from lasagne.layers.dnn import Conv3DDNNLayer as ConvLayer, Pool3DDNNLayer as PoolLayer
import lasagne
from lasagne.init import HeNormal

def build_UNet(n_input_channels=1, BATCH_SIZE=None, num_output_classes=2, pad='same', nonlinearity=lasagne.nonlinearities.elu, input_dim=(128, 128), depth=32, base_n_filters=32, kernel_size=3, do_dropout=False):
    net = OrderedDict()

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____________________________________________________________________________________________________
convolution2d_134 (Convolution2D (None, 2, 64, 80)     66          convolution2d_133[0][0]          
____________________________________________________________________________________________________
permute_5 (Permute)              (None, 64, 80, 2)     0           convolution2d_134[0][0]          
____________________________________________________________________________________________________
reshape_7 (Reshape)              (None, 5120, 2)       0           permute_5[0][0]                  
____________________________________________________________________________________________________
activation_6 (Activation)        (None, 5120, 2)       0           reshape_7[0][0]  

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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {

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