Convert .h5 to TensorFlow tflite models.ipynb

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      "INFO:tensorflow:Assets written to: C:\\Users\\Andrew\\AppData\\Local\\Temp\\tmp_99bv1be\\assets\n"
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      "WARNING:absl:Buffer deduplication procedure will be skipped when flatbuffer library is not properly loaded\n"
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   "source": [
    "from tensorflow import lite\n",
    "from tensorflow.keras import models\n",
    "\n",
    "# Parameters\n",
    "keras_model_filename = 'model_v3.h5'\n",
    "tflite_filename = 'model_v3.tflite'\n",
    "\n",
    "# Convert model to TF Lite model\n",
    "model = models.load_model(keras_model_filename)\n",
    "converter = lite.TFLiteConverter.from_keras_model(model)\n",
    "tflite_model = converter.convert()\n",
    "open(tflite_filename, 'wb').write(tflite_model)"
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      "INFO:tensorflow:Assets written to: C:\\Users\\Andrew\\AppData\\Local\\Temp\\tmpl5gqmhwy\\assets\n"
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      "INFO:tensorflow:Assets written to: C:\\Users\\Andrew\\AppData\\Local\\Temp\\tmpl5gqmhwy\\assets\n",
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   "source": [
    "from tensorflow import lite\n",
    "from tensorflow.keras import models\n",
    "\n",
    "# Parameters\n",
    "keras_model_filename = 'model_v4.h5'\n",
    "tflite_filename = 'model_v4.tflite'\n",
    "\n",
    "# Convert model to TF Lite model\n",
    "model = models.load_model(keras_model_filename)\n",
    "converter = lite.TFLiteConverter.from_keras_model(model)\n",
    "tflite_model = converter.convert()\n",
    "open(tflite_filename, 'wb').write(tflite_model)"
   ]
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   "source": [
    "## Raspberry Pi function:"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9778d5f6",
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   "source": [
    "# Load model (interpreter)\n",
    "from tflite_runtime.interpreter import Interpreter\n",
    "\n",
    "interpreter = Interpreter('model.tflite')\n",
    "interpreter.allocate_tensors()\n",
    "input_details = interpreter.get_input_details()\n",
    "output_details = interpreter.get_output_details()\n",
    "print(input_details)\n",
    "\n",
    "# Make prediction from model\n",
    "def predect(image)\n",
    "    in_tensor = np.float32(image)\n",
    "    interpreter.invoke()\n",
    "    output_data = interpreter.get_tensor(output_details[0]['index'])\n",
    "    val = output_data[0][0]\n",
    "    return(val)\n"
   ]
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