tensorflowlite_convertion.ipynb

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      "Saved artifact at 'C:\\Users\\vaibh\\AppData\\Local\\Temp\\tmpyy28f0xt'. The following endpoints are available:\n",
      "\n",
      "* Endpoint 'serve'\n",
      "  args_0 (POSITIONAL_ONLY): TensorSpec(shape=(32, 300, 300, 3), dtype=tf.float32, name='input_layer_1')\n",
      "Output Type:\n",
      "  TensorSpec(shape=(32, 5), dtype=tf.float32, name=None)\n",
      "Captures:\n",
      "  1973328661312: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
      "  1973328666240: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
      "  1973328672224: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
      "  1973345409632: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
      "  1973345413328: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
      "  1973345414032: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
      "  1973345414736: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
      "  1973345616992: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
      "  1973345623856: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
      "  1973345624560: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
      "  1973345631424: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
      "  1973345625264: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
      "  1973345869968: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
      "  1973345870672: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
      "  1973345876656: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
      "  1973345874896: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
      "✅ Model successfully converted to TensorFlow Lite with Float16 precision!\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "# Load the trained model\n",
    "model = tf.keras.models.load_model(\"trained_model.keras\")\n",
    "\n",
    "# Convert to TensorFlow Lite using Float16 to minimize accuracy loss\n",
    "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
    "converter.optimizations = [tf.lite.Optimize.DEFAULT]\n",
    "converter.target_spec.supported_types = [tf.float16]\n",
    "\n",
    "tflite_model = converter.convert()\n",
    "\n",
    "# Save the TFLite model\n",
    "with open(\"model_float16.tflite\", \"wb\") as f:\n",
    "    f.write(tflite_model)\n",
    "\n",
    "print(\"✅ Model successfully converted to TensorFlow Lite with Float16 precision!\")"
   ]
  },
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