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config.yml
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config.yml
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# Configuration for dataset
dataset:
seed: 42
dataset_path: '../dataset_folder/recv_sig_noise_01.npy'
sigdata_path: '../dataset_folder/sig_amp_and_noise_std_01.mat'
label_path: '../dataset_folder/traindata_01.csv'
test_size: 0.1
val_size: 0.1
n_snap: 30
max_val: [80, 5]
max_angle: 90
last_dim: True
batch_size: 512
num_workers: 10
model:
# Activation function for network.
act_fn1: 'relu' # relu, tanh, gelu, swish
act_fn2: 'sigmoid'
act_fn3: 'tanh'
c_in: 1
# make sure c_outsum[1:] is equal to the sum of number of channels of different kernel size.
c_outsum: [16, 32, 48, 64]
c_out: [{'1x1': 6, '1x3': 6, '3x1': 6, '3x3': 8, '5x1': 6},
{'1x1': 8, '1x3': 8, '3x1': 8, '3x3': 16, '5x1': 8},
{'1x1': 10, '1x3': 10, '3x1': 10, '3x3': 24, '5x1': 10}]
c_red: [{'1x3': 12, '3x1': 12, '3x3': 16, '5x1': 12},
{'1x3': 16, '3x1': 16, '3x3': 32, '5x1': 16},
{'1x3': 20, '3x1': 20, '3x3': 48, '5x1': 20}]
k_size: {'1x1': [1, 1], '1x3': [1, 3], '2x1': [2, 1], '3x1': [3, 1], '3x3': [3, 3], '3x5': [3, 5],'5x1': [5, 1]}
# Convolutional hyperparameters.
conv_stride: [1, 1]
conv_padding: {'1x1': [0, 0], '1x3': [0, 1], '2x1': [0, 0], '3x1': [1, 0], '3x3': [1, 1], '5x1': [2, 0]}
conv_dilation: [1, 1]
# Downsampling hyperparameters.
pool_stride: [1, 1]
pool_padding: [1, 0]
pool_dilation: [2, 1]
# Others.
use_bias: True
use_bn: True
is_1dkernel: True
is_2dkernel: True
n_layers: 2 # fixed.
n_inception: 2
resnet_stride1: [1, 1] # fixed
resnet_stride2: [2, 1] # fixed
resnet_subsample: True
is_concat: True
is_skip: True
is_ampskip: True
is_doaskip: False
is_se_net: True
squeeze_ratio: 8
rnn_hidden_size: 64
rnn_nlayers: 3
n_snap: 30
ndoa_param: 2
training:
# config: 1
checkpoint_path: './saved_models/exp2_largedata_largeparam'
max_epochs: 50
optimizer_type: 'adamw' # adam, sgd, adamw,
learning_rate: 0.001
loss_type: 'loss4' # try 2, 3, 3_1, 4, 4_1 and 5 also.
weight_decay: 0
is_norm: False
loss_weights: [1, 1, 1]
metrics:
rmse_threshold: 2.4
# exp1.
# parameters: 287218
# c_outsum: [8, 24, 36, 48]
# c_out: [{'1x1': 4, '1x3': 4, '3x1': 4, '3x3': 8, '5x1': 4},
# {'1x1': 6, '1x3': 6, '3x1': 6, '3x3': 12, '5x1': 6},
# {'1x1': 8, '1x3': 8, '3x1': 8, '3x3': 16, '5x1': 8}]
# c_red: [{'1x3': 8, '3x1': 8, '3x3': 16, '5x1': 8},
# {'1x3': 12, '3x1': 12, '3x3': 24, '5x1': 12},
# {'1x3': 16, '3x1': 16, '3x3': 32, '5x1': 16}]
# exp2
# parameters:
# c_outsum: [16, 32, 48, 64]
# c_out: [{'1x1': 6, '1x3': 6, '3x1': 6, '3x3': 8, '5x1': 6},
# {'1x1': 8, '1x3': 8, '3x1': 8, '3x3': 16, '5x1': 8},
# {'1x1': 10, '1x3': 10, '3x1': 10, '3x3': 24, '5x1': 10}]
# c_red: [{'1x3': 12, '3x1': 12, '3x3': 16, '5x1': 12},
# {'1x3': 16, '3x1': 16, '3x3': 32, '5x1': 16},
# {'1x3': 20, '3x1': 20, '3x3': 48, '5x1': 20}]