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experiment_group: Embedding Experiments
experiments:
- train_args:
batch_size: 256
max_epochs: &max_epochs 8
dataset:
type: EmnistDataset
args:
sample_to_balance: true
subsample_fraction: null
transform: null
target_transform: null
seed: 4711
train_args:
num_workers: 8
train_fraction: 0.85
model: CharacterModel
metrics: []
network:
type: ResidualNetwork
args:
in_channels: 1
num_classes: 64 # Embedding
depths: [2,2]
block_sizes: [32, 64]
activation: selu
stn: false
criterion:
type: EmbeddingLoss
args:
margin: 0.2
type_of_triplets: semihard
optimizer:
type: AdamW
args:
lr: 1.e-02
betas: [0.9, 0.999]
eps: 1.e-08
weight_decay: 5.e-4
amsgrad: false
lr_scheduler:
type: CosineAnnealingLR
args:
T_max: *max_epochs
callbacks: [Checkpoint, ProgressBar, WandbCallback]
callback_args:
Checkpoint:
monitor: val_loss
mode: min
ProgressBar:
epochs: *max_epochs
WandbCallback:
log_batch_frequency: 10
verbosity: 1 # 0, 1, 2
resume_experiment: null
train: true
test: true
test_metric: mean_average_precision_at_r
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