<jupyter_start><jupyter_text>LatentODE(10) latent dim = 10, missing = 0, noise = 0<jupyter_code>!python3 run_models.py --niters 50 -n 1000 -s 1 -l 10 --dataset periodic --latent-ode --noise-weight 0 --viz<jupyter_output>Sampling dataset of 1000 training examples
/home/phd/mventura/latent_ode_expt/run_models.py
run_models.py --niters 50 -n 1000 -s 1 -l 10 --dataset periodic --latent-ode --noise-weight 0 --viz
Computing loss... 0
Experiment 34596
Epoch 0001 [Test seq (cond on sampled tp)] | Loss 2882.689453 | Likelihood -2915.130127 | KL fp 2.0846 | FP STD 0.8168|
KL coef: 0.0
Train loss (one batch): 2985.15087890625
Train CE loss (one batch): 0.0
Test MSE: 0.5838
Poisson likelihood: 0.0
CE loss: 0.0
plotting....
Computing loss... 0
Experiment 34596
Epoch 0002 [Test seq (cond on sampled tp)] | Loss 2364.738770 | Likelihood -2368.250732 | KL fp 4.5171 | FP STD 0.7637|
KL coef: 0.0
Train loss (one batch): 2378.921142578125
Train CE loss (one batch): 0.0
Test MSE: 0.4744
Poisson likelihood: 0.0
CE loss: 0.0
plotting....
Computing loss... 0
Experiment 34596
Epoch 0003 [Test seq (cond on sampled tp)] | Loss 2340.158691 | Likelihood -2343.839111 | KL fp 4.7384 | FP STD 0.5836|
KL coef: 0.0
Train loss (one bat[...]<jupyter_text>latent dim = 10, missing = 0, noise = 0.25<jupyter_code>!python3 run_models.py --niters 50 -n 1000 -s 1 -l 10 --dataset periodic --latent-ode --noise-weight 0.25 --viz<jupyter_output>Sampling dataset of 1000 training examples
/home/phd/mventura/latent_ode_expt/run_models.py
run_models.py --niters 50 -n 1000 -s 1 -l 10 --dataset periodic --latent-ode --noise-weight 0.25 --viz
Computing loss... 0
Experiment 60733
Epoch 0001 [Test seq (cond on sampled tp)] | Loss 2749.644287 | Likelihood -2779.954346 | KL fp 3.3951 | FP STD 1.1720|
KL coef: 0.0
Train loss (one batch): 2944.896484375
Train CE loss (one batch): 0.0
Test MSE: 0.5567
Poisson likelihood: 0.0
CE loss: 0.0
plotting....
Computing loss... 0
Experiment 60733
Epoch 0002 [Test seq (cond on sampled tp)] | Loss 2456.084717 | Likelihood -2458.683105 | KL fp 5.6530 | FP STD 0.9122|
KL coef: 0.0
Train loss (one batch): 2454.54296875
Train CE loss (one batch): 0.0
Test MSE: 0.4925
Poisson likelihood: 0.0
CE loss: 0.0
plotting....
Computing loss... 0
Experiment 60733
Epoch 0003 [Test seq (cond on sampled tp)] | Loss 2396.656494 | Likelihood -2397.910889 | KL fp 5.6607 | FP STD 0.5814|
KL coef: 0.0
Train loss (one batch)[...]<jupyter_text>latent dim = 10, missing = 0, noise = 0.5<jupyter_code>!python3 run_models.py --niters 50 -n 1000 -s 1 -l 10 --dataset periodic --latent-ode --noise-weight 0.5 --viz<jupyter_output>Sampling dataset of 1000 training examples
/home/phd/mventura/latent_ode_expt/run_models.py
run_models.py --niters 50 -n 1000 -s 1 -l 10 --dataset periodic --latent-ode --noise-weight 0.5 --viz
Computing loss... 0
Experiment 29360
Epoch 0001 [Test seq (cond on sampled tp)] | Loss 2594.708984 | Likelihood -2610.555664 | KL fp 5.7207 | FP STD 1.3208|
KL coef: 0.0
Train loss (one batch): 2632.50537109375
Train CE loss (one batch): 0.0
Test MSE: 0.5228
Poisson likelihood: 0.0
CE loss: 0.0
plotting....
Computing loss... 0
Experiment 29360
Epoch 0002 [Test seq (cond on sampled tp)] | Loss 2527.955811 | Likelihood -2534.052490 | KL fp 5.9983 | FP STD 1.0131|
KL coef: 0.0
Train loss (one batch): 2593.451171875
Train CE loss (one batch): 0.0
Test MSE: 0.5075
Poisson likelihood: 0.0
CE loss: 0.0
plotting....
Computing loss... 0
Experiment 29360
Epoch 0003 [Test seq (cond on sampled tp)] | Loss 2477.974365 | Likelihood -2481.937744 | KL fp 6.2868 | FP STD 0.7410|
KL coef: 0.0
Train loss (one batc[...]<jupyter_text>LatentODE(5) latent dim = 5, missing = 0, noise = 0<jupyter_code>!python3 run_models.py --niters 50 -n 1000 -s 1 -l 5 --dataset periodic --latent-ode --noise-weight 0 --viz<jupyter_output>Sampling dataset of 1000 training examples
/home/phd/mventura/latent_ode_expt/run_models.py
run_models.py --niters 50 -n 1000 -s 1 -l 5 --dataset periodic --latent-ode --noise-weight 0 --viz
Computing loss... 0
Experiment 97172
Epoch 0001 [Test seq (cond on sampled tp)] | Loss 2456.169189 | Likelihood -2457.620850 | KL fp 3.6085 | FP STD 0.2477|
KL coef: 0.0
Train loss (one batch): 2527.441650390625
Train CE loss (one batch): 0.0
Test MSE: 0.4923
Poisson likelihood: 0.0
CE loss: 0.0
plotting....
Computing loss... 0
Experiment 97172
Epoch 0002 [Test seq (cond on sampled tp)] | Loss 2410.403809 | Likelihood -2412.561279 | KL fp 5.0874 | FP STD 0.2149|
KL coef: 0.0
Train loss (one batch): 2403.742431640625
Train CE loss (one batch): 0.0
Test MSE: 0.4832
Poisson likelihood: 0.0
CE loss: 0.0
plotting....
Computing loss... 0
Experiment 97172
Epoch 0003 [Test seq (cond on sampled tp)] | Loss 2370.161865 | Likelihood -2371.301025 | KL fp 6.5860 | FP STD 0.1410|
KL coef: 0.0
Train loss (one bat[...]<jupyter_text>latent dim = 5, missing = 0, noise = 0.25<jupyter_code>!python3 run_models.py --niters 50 -n 1000 -s 1 -l 5 --dataset periodic --latent-ode --noise-weight 0.25 --viz<jupyter_output>Sampling dataset of 1000 training examples
/home/phd/mventura/latent_ode_expt/run_models.py
run_models.py --niters 50 -n 1000 -s 1 -l 5 --dataset periodic --latent-ode --noise-weight 0.25 --viz
Computing loss... 0
Experiment 37025
Epoch 0001 [Test seq (cond on sampled tp)] | Loss 2567.700195 | Likelihood -2570.710205 | KL fp 4.9346 | FP STD 0.2282|
KL coef: 0.0
Train loss (one batch): 2488.65771484375
Train CE loss (one batch): 0.0
Test MSE: 0.5149
Poisson likelihood: 0.0
CE loss: 0.0
plotting....
Computing loss... 0
Experiment 37025
Epoch 0002 [Test seq (cond on sampled tp)] | Loss 2408.203125 | Likelihood -2409.652100 | KL fp 5.7731 | FP STD 0.3180|
KL coef: 0.0
Train loss (one batch): 2425.593017578125
Train CE loss (one batch): 0.0
Test MSE: 0.4827
Poisson likelihood: 0.0
CE loss: 0.0
plotting....
Computing loss... 0
Experiment 37025
Epoch 0003 [Test seq (cond on sampled tp)] | Loss 2385.965576 | Likelihood -2387.153564 | KL fp 7.3006 | FP STD 0.2707|
KL coef: 0.0
Train loss (one b[...]<jupyter_text>latent dim = 5, missing = 0, noise = 0.5<jupyter_code>!python3 run_models.py --niters 50 -n 1000 -s 1 -l 5 --dataset periodic --latent-ode --noise-weight 0.5 --viz<jupyter_output>Sampling dataset of 1000 training examples
/home/phd/mventura/latent_ode_expt/run_models.py
run_models.py --niters 50 -n 1000 -s 1 -l 5 --dataset periodic --latent-ode --noise-weight 0.5 --viz
Computing loss... 0
Experiment 89782
Epoch 0001 [Test seq (cond on sampled tp)] | Loss 2671.145264 | Likelihood -2674.052979 | KL fp 5.9477 | FP STD 0.4118|
KL coef: 0.0
Train loss (one batch): 2724.444091796875
Train CE loss (one batch): 0.0
Test MSE: 0.5355
Poisson likelihood: 0.0
CE loss: 0.0
plotting....
Computing loss... 0
Experiment 89782
Epoch 0002 [Test seq (cond on sampled tp)] | Loss 2508.930420 | Likelihood -2510.211182 | KL fp 6.9988 | FP STD 0.3768|
KL coef: 0.0
Train loss (one batch): 2517.1650390625
Train CE loss (one batch): 0.0
Test MSE: 0.5028
Poisson likelihood: 0.0
CE loss: 0.0
plotting....
Computing loss... 0
Experiment 89782
Epoch 0003 [Test seq (cond on sampled tp)] | Loss 2469.965332 | Likelihood -2471.293945 | KL fp 8.1648 | FP STD 0.3536|
KL coef: 0.0
Train loss (one bat[...]
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