Tabular Regression
Keras
Joblib
English
battery
state-of-health
remaining-useful-life
time-series
regression
lstm
transformer
xgboost
lightgbm
random-forest
ensemble
Instructions to use NeerajCodz/aiBatteryLifeCycle with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use NeerajCodz/aiBatteryLifeCycle with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://NeerajCodz/aiBatteryLifeCycle") - Notebooks
- Google Colab
- Kaggle
File size: 3,881 Bytes
710d76b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 | {
"version": "v3",
"figures": [
{
"name": "best ensemble prediction",
"tags": [
"best",
"ensemble",
"prediction"
],
"location": "best_ensemble_prediction.png"
},
{
"name": "capacity and rul",
"tags": [
"capacity",
"and",
"rul"
],
"location": "capacity_and_rul.png"
},
{
"name": "ced curves",
"tags": [
"ced",
"curves"
],
"location": "ced_curves.png"
},
{
"name": "degradation state distribution",
"tags": [
"degradation",
"state",
"distribution"
],
"location": "degradation_state_distribution.png"
},
{
"name": "dg itransformer predictions",
"tags": [
"dg",
"itransformer",
"predictions"
],
"location": "dg_itransformer_predictions.png"
},
{
"name": "dg itransformer training",
"tags": [
"dg",
"itransformer",
"training"
],
"location": "dg_itransformer_training.png"
},
{
"name": "ensemble comparison",
"tags": [
"ensemble",
"comparison"
],
"location": "ensemble_comparison.png"
},
{
"name": "ensemble confusion matrix",
"tags": [
"ensemble",
"confusion",
"matrix"
],
"location": "ensemble_confusion_matrix.png"
},
{
"name": "ensemble weights",
"tags": [
"ensemble",
"weights"
],
"location": "ensemble_weights.png"
},
{
"name": "feature correlation heatmap",
"tags": [
"feature",
"correlation",
"heatmap"
],
"location": "feature_correlation_heatmap.png"
},
{
"name": "lstm actual vs predicted",
"tags": [
"lstm",
"actual",
"vs",
"predicted"
],
"location": "lstm_actual_vs_predicted.png"
},
{
"name": "lstm training curves",
"tags": [
"lstm",
"training",
"curves"
],
"location": "lstm_training_curves.png"
},
{
"name": "mc dropout uncertainty lstm",
"tags": [
"mc",
"dropout",
"uncertainty",
"lstm"
],
"location": "mc_dropout_uncertainty_lstm.png"
},
{
"name": "soc coulomb counting demo",
"tags": [
"soc",
"coulomb",
"counting",
"demo"
],
"location": "soc_coulomb_counting_demo.png"
},
{
"name": "soh degradation trends",
"tags": [
"soh",
"degradation",
"trends"
],
"location": "soh_degradation_trends.png"
},
{
"name": "transformer pt training curves",
"tags": [
"transformer",
"pt",
"training",
"curves"
],
"location": "transformer_pt_training_curves.png"
},
{
"name": "transformer tf training curves",
"tags": [
"transformer",
"tf",
"training",
"curves"
],
"location": "transformer_tf_training_curves.png"
},
{
"name": "unified model comparison",
"tags": [
"unified",
"model",
"comparison"
],
"location": "unified_model_comparison.png"
},
{
"name": "vae anomaly detection",
"tags": [
"vae",
"anomaly",
"detection"
],
"location": "vae_anomaly_detection.png"
},
{
"name": "vae latent umap",
"tags": [
"vae",
"latent",
"umap"
],
"location": "vae_latent_umap.png"
},
{
"name": "vae lstm prediction",
"tags": [
"vae",
"lstm",
"prediction"
],
"location": "vae_lstm_prediction.png"
}
]
} |