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#################################################        CREATION OF THE NETWORK       #####################################################

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############## =================== General Options  =================  ################

[General]

networkName = liviaTest

# Saving Options

folderName = LiviaNet_Test

############## =================== CNN_Architecture  =================  ################

[CNN_Architecture]

numkernelsperlayer = [10,20,30,100]

# Kernels shapes:  (Note, if kernel size is equal to 1 on one layer means that this layer is fully connected)

# In this example there will be 3 conv layers and 1 fully connected layer (+ classification layer)

kernelshapes = [[3, 3, 3], [3, 3, 3], [3, 3, 3], [1]]

# Intermediate layers to connect to the last conv layer (just before the first fully connected layer)

intermediateConnectedLayers = []

# In the current implementation it does not support pooling (To be done...)

pooling_scales = [[1,1,1],[1,1,1],[1,1,1]]

# Array size should be equal to number of fully connected (FC) layers + classification layer

dropout_Rates = [0.25,0.5] 

# Non-linear activations

# Type: 0: Linear

#       1: ReLU

#       2: PReLU

#       3: LeakyReLU

activationType = 2

# TODO. Include activation type for Softmax layer

# Number of classes: background + classes to segment

n_classes = 9

# ------- Weights initialization ----------- #

# There are some ways to initialize the weights. This is defined by the variable "weight_Initialization"

# Here, there is a list of supported methods

# 0, Classic

# 1: Delving (He, Kaiming, et al. "Delving deep into rectifiers: Surpassing human-level performance on imagenet classification." ICCV'15)

# 2: Load Pre-trained

#      ----------

# There is also the choice of which layers will be initialized with pre-trained weights. This is specified in the variable

# "load weight layers". This can be either empty (i.e. all layers will be initialized with pre-trained weights in case 

# "weight_Initialization" is 1)

weight_Initialization_CNN = 1

weight_Initialization_FCN = 1

#load weight layers = [] # Next release

# If using pre-trained models, specify the folder that contains the weights and the indexes of those weights to use

# To ease the transfer between different softwares (i.e matlab for instance), and between different architectures,

# the weights for each layer should be stored as a single file.

# Right now weights have to be in .npy format

weights folderName = /~yourpath/trainedWeights

# Same length as conv layers

weights trained indexes = [0,1,2] 

#weight_Initialization_Sec = 1

############## =================== Training Options  =================  ################

[Training Parameters]

#n_epochs=20

batch_size=10

number Of Epochs = 3

number Of SubEpochs = 2 

number of samples at each SubEpoch Train = 1000

# TODO. To define some changes in the learning rate

learning Rate change Type = 0

# Subvolumes (i.e. samples) sizes.

# Validation equal to testing samples

sampleSize_Train = [25,25,25]

sampleSize_Test  = [45,45,45]

# Cost function values

# 0:

# 1:

costFunction = 0 

SoftMax temperature = 1.0

#### ========= Learning rate ========== #####

L1 Regularization Constant = 1e-6

L2 Regularization Constant = 1e-4

# TO check

# The array size has to be equal to the total number of layers (i.e. CNNs + FCs + Classification layer)

#Leraning Rate = [0.0001, 0.0001, 0.0001, 0.0001,0.0001, 0.0001, 0.0001, 0.0001,0.0001, 0.0001, 0.0001, 0.0001,0.0001, 0.0001 ]

Leraning Rate = [0.001]

# First epoch to change learning rate

First Epoch Change LR = 1

# Each how many epochs change learning rate

Frequency Change LR = 2

# TODO. Add learning rate for each layer

#### ========= Momentum ========== #####

# Type of momentum

# 0: Classic

# 1: Nesterov

Momentum Type = 1

Momentum Value = 0.6

# Use momentum normalized?

momentumNormalized = 1

#### ======== Optimizer ===== ######

# Type: 0-> SGD

#       1-> RMSProp (TODO. Check why RMSProp complains....)

Optimizer Type = 1

#In case we chose RMSProp

Rho RMSProp = 0.9

Epsilon RMSProp = 1e-4

# Apply Batch normalization

# 0: False, 1: True

applyBatchNormalization = 1

BatchNormEpochs = 20

# Apply padding to images

# 0: False, 1: True

applyPadding = 1

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#################################################            TRAINING VALUES           #####################################################

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[Training Images]

imagesFolder = /~yourpath/Dataset/MR/ 

GroundTruthFolder = /~yourpath/Dataset/Label/

# ROI folder will contain the ROI where to extract the pacthes and where to perform the segmentation.

# Values of the ROI should be 0 (non interest) and 1 (region of interest)

ROIFolder = /~yourpath/Dataset/ROI/

# If you have no ROIs

#ROIFolder = []

# Type of images in the dataset

#     0: nifti format

#     1: matlab format

# IMPORTANT: All the volumes should have been saved as 'vol'

imageTypes = 1

# Indexes for training/validation images. Note that indexes should correspond to the position = inex + 1 in the folder,

# since python starts indexing at 0

indexesForTraining = [0,1,2,3,4]

indexesForValidation = [5]