app_files/src/defaults.py

# Written by Dr Daniel Buscombe, Marda Science LLC
# for the SandSnap Program
#
# MIT License
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##> Release v1.4 (Aug 2021)

## Contains values for defaults that you may change.
## They are listed in order of likelihood that you might change them:

# size of image in pixels. keep this consistent in training and application
# suggestd: 512 -- 1024 (larger = larger GPU required)
# integer
IM_HEIGHT = 1024
IM_WIDTH = IM_HEIGHT

# number of images to feed the network per step in epoch #suggested: as many as you have gpu memory for, probably
# integer

# BATCH_SIZE =8
# BATCH_SIZE =10
BATCH_SIZE =12

#use an ensemble of batch sizes like this
#BATCH_SIZE = [7,12,14]

# if True, use a smaller (shallower) network architecture
##True or False ##False=larger network
SHALLOW = False #True

## if True, carry out data augmentation. 2 x number of images used in training
##True or False
DO_AUG = False #True

# maximum learning rate ##1e-1 -- 1e-5
MAX_LR = 1e-4
# MAX_LR = 1e-5
# MAX_LR = 5e-3
# MAX_LR = 5e-4

# max. number of training epics (20 -1000)
# integer
NUM_EPOCHS = 300

## loss function for continuous models (2 choices)
#CONT_LOSS = 'pinball'
CONT_LOSS = 'mse'

## loss function for categorical (disrete) models (2 choices)
CAT_LOSS = 'focal'
#CAT_LOSS = 'categorical_crossentropy'

# optimizer (gradient descent solver) good alternative == 'rmsprop'
OPT = 'adam'

# base number of conv2d filters in categorical models
# integer
BASE_CAT = 30

# base number of conv2d filters in continuous models
# integer
# BASE_CONT = 30
BASE_CONT = 10

# number of Dense units for continuous prediction
# integer
# CONT_DENSE_UNITS = 3072
CONT_DENSE_UNITS = 2048
# CONT_DENSE_UNITS = 1024

# number of Dense units for categorical prediction
# integer
CAT_DENSE_UNITS = 128

# set to False if you wish to use cpu (not recommended)
##True or False
USE_GPU = True

## standardize imagery (recommended)
DO_STANDARDIZE = True


# STOP_PATIENCE = 10

# FACTOR = 0.2

# MIN_DELTA = 0.0001

# MIN_LR = 1e-4