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.gitattributes

@@ -25,7 +25,6 @@
 *.safetensors filter=lfs diff=lfs merge=lfs -text
 saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
 *.tflite filter=lfs diff=lfs merge=lfs -text
 *.tgz filter=lfs diff=lfs merge=lfs -text
 *.wasm filter=lfs diff=lfs merge=lfs -text
@@ -33,3 +32,4 @@ 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

.ipynb_checkpoints/Untitled-checkpoint.ipynb

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+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

README.md

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+---
+library_name: keras
+---
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+
+| Hyperparameters | Value |
+| :-- | :-- |
+| name | SGD |
+| learning_rate | 0.05000000074505806 |
+| decay | 0.0 |
+| momentum | 0.0 |
+| nesterov | False |
+| training_precision | float32 |
+

Untitled.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "4081444c-cadc-42e3-81df-227556ca307a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model output: {'dense_1': <tf.Tensor: shape=(169, 6), dtype=float32, numpy=\n",
+      "array([[ 0.6966887 ,  1.955204  , -0.78022176, -2.2263775 , -0.14889973,\n",
+      "         2.2231045 ],\n",
+      "       [ 0.68961495,  1.9612552 , -0.774499  , -2.2331095 , -0.15998632,\n",
+      "         2.219545  ],\n",
+      "       [ 0.66707706,  1.9735199 , -0.78785485, -2.2285533 , -0.15105623,\n",
+      "         2.2114253 ],\n",
+      "       ...,\n",
+      "       [ 0.67654526,  1.9316783 , -0.76229304, -2.2165785 , -0.13325018,\n",
+      "         2.219839  ],\n",
+      "       [ 0.68046916,  1.9528831 , -0.7709062 , -2.2316525 , -0.15479451,\n",
+      "         2.2055793 ],\n",
+      "       [ 0.69711024,  1.9614836 , -0.7781251 , -2.2383246 , -0.1600563 ,\n",
+      "         2.239264  ]], dtype=float32)>}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from keras.layers import TFSMLayer\n",
+    "\n",
+    "# Path to the folder containing saved_model.pb and variables/\n",
+    "model = TFSMLayer('./', call_endpoint='serving_default')\n",
+    "\n",
+    "# Run inference\n",
+    "# Example: dummy input shape (change to match model input)\n",
+    "import numpy as np\n",
+    "dummy_input = np.random.rand(1, 224, 224, 3).astype(np.float32)\n",
+    "output = model(dummy_input)\n",
+    "\n",
+    "print(\"Model output:\", output)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9bdf9e9e-9ca3-4c44-b686-36de115ef5a1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

fingerprint.pb

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keras_metadata.pb

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saved_model.pb

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variables/variables.data-00000-of-00001

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