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MeteoSwiss / mlpp-lib

Collection of methods for ML-based postprocessing of weather forecasts.
BSD 3-Clause "New" or "Revised" License
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Val_loss NaN for any training #58

Closed louisPoulain closed 1 week ago

louisPoulain commented 2 weeks ago 
sum_vobs_nan.ref().deref()=<tf.Tensor: shape=(), dtype=int32, numpy=0>/500000
tf.reduce_sum(tf.cast(tf.math.is_nan(y_pred.distribution.normal.mean()), tf.int32))=<tf.Tensor: shape=(), dtype=int32, numpy=544>/500000
tf.reduce_sum(tf.cast(tf.math.is_nan(y_pred.distribution.normal.stddev()), tf.int32))=<tf.Tensor: shape=(), dtype=int32, numpy=544>/500000
tf.reduce_sum(tf.cast(tf.math.is_nan(y_pred.mean()), tf.int32))=<tf.Tensor: shape=(), dtype=int32, numpy=544>/500000
sum_samp1_nan.ref().deref()=<tf.Tensor: shape=(), dtype=int32, numpy=54400>/50000000
sum_samp2_nan.ref().deref()=<tf.Tensor: shape=(), dtype=int32, numpy=54400>/50000000
sum_e1_nan.ref().deref()=<tf.Tensor: shape=(), dtype=int32, numpy=544>/500000
sum_e2_nan.ref().deref()=<tf.Tensor: shape=(), dtype=int32, numpy=544>/500000
sum_twcrps_nan.ref().deref()=<tf.Tensor: shape=(), dtype=int32, numpy=544>/500000

For any training there is exactly one batch of data (size 500'000) that produces exactly 544 NaNs in the predicted distribution. The distribution is a doubly-censored normal. The actual loss, on the other hand is never NaN

Other infos

normalizer: fillvalue: -5 default: Standardizer

targets:

sample_weights:

data_partitioning: time_split: train:

model: fully_connected_network: hidden_layers: [512, 512, 512] activations: relu dropout: [0.3, 0.2, 0.1] probabilistic_layer: IndependentDoublyCensoredNormal mc_dropout: True

optimizer: Adam: learning_rate: CosineDecayRestarts: initial_learning_rate: 0.001 first_decay_steps: 20 t_mul: 2.0 m_mul: 1.025 alpha: 0.0 beta_1: 0.94 loss: WeightedCRPSEnergy: threshold: 0.0 n_samples: 100 epochs: 4 steps_per_epoch: 1

metrics:

callbacks:

Env

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file:///home/conda/feedstock_root/build_artifacts/ipython_1701831663892/work itsdangerous @ file:///home/conda/feedstock_root/build_artifacts/itsdangerous_1713372668944/work jax==0.4.30 jaxlib==0.4.30 jedi @ file:///home/conda/feedstock_root/build_artifacts/jedi_1696326070614/work Jinja2 @ file:///home/conda/feedstock_root/build_artifacts/jinja2_1715127149914/work jmespath @ file:///home/conda/feedstock_root/build_artifacts/jmespath_1655568249366/work joblib @ file:///home/conda/feedstock_root/build_artifacts/joblib_1714665484399/work keras==2.12.0 kiwisolver @ file:///home/conda/feedstock_root/build_artifacts/kiwisolver_1725459266648/work libclang==18.1.1 llvmlite==0.43.0 locket @ file:///home/conda/feedstock_root/build_artifacts/locket_1650660393415/work lz4 @ file:///home/conda/feedstock_root/build_artifacts/lz4_1725089417274/work Mako @ file:///home/conda/feedstock_root/build_artifacts/mako_1715711344987/work Markdown @ 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louisPoulain commented 1 week ago

After reviewing the features set that we have, it seems that we have Inf or -Inf values in some of our variables. The cause could be that Dataset.drop_nans only checks for the presence of NaNs and not infinite values. I'll propose a fix