Push Keras model using huggingface_hub.

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,26 +1,7 @@
 ---
-license: apache-2.0
-base_model: google/vit-base-patch16-224-in21k
-tags:
-- generated_from_keras_callback
-model-index:
-- name: arieg/bw_spec_cls_8_00_noise_200
-  results: []
+library_name: keras
 ---
 
-<!-- This model card has been generated automatically according to the information Keras had access to. You should
-probably proofread and complete it, then remove this comment. -->
-
-# arieg/bw_spec_cls_8_00_noise_200
-
-This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Train Loss: 0.0115
-- Train Sparse Categorical Accuracy: 1.0
-- Validation Loss: 0.0109
-- Validation Sparse Categorical Accuracy: 1.0
-- Epoch: 19
-
 ## Model description
 
 More information needed
@@ -38,38 +19,30 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- optimizer: {'name': 'AdamWeightDecay', 'clipnorm': 1.0, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 30600, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
-- training_precision: float32
 
-### Training results
+| Hyperparameters | Value |
+| :-- | :-- |
+| name | SGD |
+| weight_decay | None |
+| clipnorm | None |
+| global_clipnorm | None |
+| clipvalue | None |
+| use_ema | False |
+| ema_momentum | 0.99 |
+| ema_overwrite_frequency | None |
+| jit_compile | True |
+| is_legacy_optimizer | False |
+| learning_rate | 0.0010000000474974513 |
+| momentum | 0.0 |
+| nesterov | False |
+| training_precision | float32 |
+
 
-| Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch |
-|:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:|
-| 0.8708     | 0.9739                            | 0.3432          | 1.0                                    | 0     |
-| 0.2554     | 1.0                               | 0.1989          | 1.0                                    | 1     |
-| 0.1700     | 1.0                               | 0.1456          | 1.0                                    | 2     |
-| 0.1286     | 1.0                               | 0.1130          | 1.0                                    | 3     |
-| 0.1011     | 1.0                               | 0.0900          | 1.0                                    | 4     |
-| 0.0812     | 1.0                               | 0.0729          | 1.0                                    | 5     |
-| 0.0664     | 1.0                               | 0.0602          | 1.0                                    | 6     |
-| 0.0551     | 1.0                               | 0.0503          | 1.0                                    | 7     |
-| 0.0463     | 1.0                               | 0.0425          | 1.0                                    | 8     |
-| 0.0394     | 1.0                               | 0.0363          | 1.0                                    | 9     |
-| 0.0338     | 1.0                               | 0.0314          | 1.0                                    | 10    |
-| 0.0293     | 1.0                               | 0.0273          | 1.0                                    | 11    |
-| 0.0255     | 1.0                               | 0.0239          | 1.0                                    | 12    |
-| 0.0224     | 1.0                               | 0.0210          | 1.0                                    | 13    |
-| 0.0198     | 1.0                               | 0.0187          | 1.0                                    | 14    |
-| 0.0176     | 1.0                               | 0.0166          | 1.0                                    | 15    |
-| 0.0157     | 1.0                               | 0.0149          | 1.0                                    | 16    |
-| 0.0141     | 1.0                               | 0.0134          | 1.0                                    | 17    |
-| 0.0127     | 1.0                               | 0.0121          | 1.0                                    | 18    |
-| 0.0115     | 1.0                               | 0.0109          | 1.0                                    | 19    |
+ ## Model Plot
 
+<details>
+<summary>View Model Plot</summary>
 
-### Framework versions
+![Model Image](./model.png)
 
-- Transformers 4.35.0
-- TensorFlow 2.14.0
-- Datasets 2.14.6
-- Tokenizers 0.14.1
+</details>

fingerprint.pb

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history.json

@@ -0,0 +1,30 @@
+{
+  "categorical_accuracy": [
+    0.7444444298744202,
+    0.97124183177948,
+    0.98562091588974,
+    0.998039186000824,
+    0.9993463754653931
+  ],
+  "loss": [
+    1.2079797983169556,
+    0.4493885636329651,
+    0.24449706077575684,
+    0.16368263959884644,
+    0.11630311608314514
+  ],
+  "val_categorical_accuracy": [
+    0.8823529481887817,
+    1.0,
+    1.0,
+    1.0,
+    1.0
+  ],
+  "val_loss": [
+    0.6126745939254761,
+    0.20612265169620514,
+    0.11283677071332932,
+    0.07244613766670227,
+    0.05332567170262337
+  ]
+}

keras_metadata.pb

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saved_model.pb

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variables/variables.data-00000-of-00001

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