cnn-bcdr/config.yaml at master · johnarevalo/cnn-bcdr · GitHub

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###path params###
models_path: 'models/'
#used for filename
model_name: 'random'
save_freq: 10

#ds_dir: 'BCDR-F01'
#csv_file: 'bcdr_f01_outlines.csv'
#csv_features_file: 'bcdr_f01_features.csv'
#csv_point_separator: ' '
csv_class_column: 'classification'
ds_dir: 'BCDR-F03'
csv_file: 'bcdr_f03_outlines.csv'
csv_features_file: 'bcdr_f03_features.csv'
csv_point_separator: ' '
csv_positive_class: 'Malign'
###Split dataset params###

# Dataset name. Used for filename
ds_name: 'miwad01-random'
# CraneoCaudal images with nodules only. Check bcdr_f01_outlines.csv for
# more info
filters: {
#   image_view: ['1', '2'],
   mammography_nodule: ['1']
}
img_width: 720
img_height: 1167

train_on_memory: True
valid_on_memory: True
valid_percent: 0.1
test_percent: 0.4
rng_seed: [2014, 10, 29]

###Region sampling params##
region_size: 150
region_stride: 30
min_overlap: 0.64
oversampling: True
apply_mask: False
crop_image: True
scale_image: True
convex_hull: True
num_channels: 1

###Preprocessing params###
preprocessing: {
    per_roi: False,
    lcn: True,
    gcn: True,
    remove_mean: False,
    batch_size: 5000
}
###Patch (filter) params###
patch_size: 11
npatches_train: 400000
npatches_valid: 150000

###sae params###
sae: {
    nhid: 64,
    beta: 5,
    rho: 0.035,
    lmbda: 0.0035,
    init: 'irange: 0.005'
}
pool_fn: 'max'

### conv params###
cnn_layers: 3
load_pretrained: False

h0: {
    nhid: 64,
    init: 'irange: 0.0069585550803',
    bias: 0,
    drop_prob: 0.8,
    drop_scale: 1.,
    col_norm: 1.9845590751341953,
    kernel_shape: 11,
    pool_shape: 5,
    pool_stride: 4,
    pool_type: 'max'
}

h1: {
    nhid: 64,
    init: 'irange: 0.01',
    bias: 0,
    col_norm: 0.12,
    kernel_shape: 4,
    pool_shape: 4,
    pool_stride: 4,
    pool_type: 'max'
}

hfc: {
    nhid: 400,
    init: 'sparse_init: 30',
    bias: 0,
    col_norm: 4.390266941491841,
    num_pieces: 2
}

y: {
    init: 'sparse_init: 20',
    col_norm: 2.6839111498943513,
}

regre: {
    init: 'irange: 0.005',
    min_beta: 1.,
    max_beta: 100.,
    beta_lr_scale: 1.,
}
alpha_layers: '[0.9, 0.1]'

## SGD Params ##
learning_rate: 0.02
lr_sat: 80
lr_decay: 0.04
init_momentum: 0.5
final_momentum: 0.99
msat: 64
max_epochs: 100
batch_size: 32
### evaluate on whole image escenario ###
stride: 30