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sample_config_60.yaml
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network:
sol:
base0: 16 #architecture dependant - don't change
base1: 16 #architecture dependant - don't change
lf:
look_ahead_matrix:
step_bias:
hw:
num_of_outputs: 197
num_of_channels: 3
cnn_out_size: 1024 #architecture dependant
input_height: 60 #architecture dependant
char_set_path: "data/char_set.json"
lm:
fst_path: "../hwn5-comp-2017/data/comp_lm/data/graph/HCLG.fst"
mdl_path: "../hwn5-comp-2017/data/comp_lm/data/lang_test/basic.mdl"
words_path: "../hwn5-comp-2017/data/comp_lm/data/graph/words.txt"
phones_path: "../hwn5-comp-2017/data/comp_lm/data/lang_test/phones.txt"
beam: 8
pretraining:
training_set:
img_folder: ""
json_folder: ""
file_list: "data/train_a_training_set.json"
validation_set:
img_folder: ""
json_folder: ""
file_list: "data/train_a_validation_set.json"
sol:
alpha_alignment: 0.1
alpha_backprop: 0.1
learning_rate: 0.0001 #pyyaml bug: no scientific notation
crop_params:
prob_label: 0.5
crop_size: 256
training_rescale_range: [384, 640]
validation_rescale_range: [512,512] #Don't validate on random range
batch_size: 1 #During pretrain, only 45 images. If batch is 32 you would get 32 and 13 in an epoch
images_per_epoch: 1000
stop_after_no_improvement: 10
lf:
learning_rate: 0.0001 #pyyaml bug: no scientific notation
batch_size: 1
images_per_epoch: 1000
stop_after_no_improvement: 10
hw:
learning_rate: 0.0002 #pyyaml bug: no scientific notation
batch_size: 8
images_per_epoch: 1000
stop_after_no_improvement: 10
snapshot_path: "data/snapshots/init"
training:
training_set:
img_folder: ""
json_folder: ""
file_list: "data/train_b_training_set.json"
validation_set:
img_folder: ""
json_folder: ""
file_list: "data/train_b_validation_set.json"
sol:
alpha_alignment: 0.1
alpha_backprop: 0.1
learning_rate: 0.0001 #pyyaml bug: no scientific notation
crop_params:
prob_label: 0.5
crop_size: 256
training_rescale_range: [384, 640]
validation_rescale_range: [512,512] #You should not validation on random range
validation_subset_size: 1000
batch_size: 1
images_per_epoch: 10000
reset_interval: 3600 #seconds
lf:
learning_rate: 0.0001 #pyyaml bug: no scientific notation
batch_size: 1
refresh_interval: 3600 #seconds
images_per_epoch: 1000 #batches
validation_subset_size: 100 #images
reset_interval: 3600 #seconds
hw:
learning_rate: 0.0002 #pyyaml bug: no scientific notation
batch_size: 8
refresh_interval: 3600 #seconds
images_per_epoch: 20000 #batches
validation_subset_size: 2000 #images
reset_interval: 3600 #seconds
alignment:
accept_threshold: 0.1
sol_resize_width: 512
metric: "cer"
train_refresh_groups: 10
validation_post_processing:
sol_thresholds: [0.1,0.3,0.5,0.7,0.9]
lf_nms_ranges: [[0,6],[0,16],[0,20]]
lf_nms_thresholds: [0.1,0.3,0.5,0.7,0.9]
snapshot:
best_overall: "data/snapshots/best_overall"
best_validation: "data/snapshots/best_validation"
current: "data/snapshots/current"
pretrain: "data/snapshots/init"
post_processing:
sol_threshold: 0.1
lf_nms_range: [0,6]
lf_nms_threshold: 0.5