# tf.compat.v1.lite.TFLiteConverter

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/lite/python/lite.py#L2755-L3128) |

Convert a TensorFlow model into `output_format`.

    tf.compat.v1.lite.TFLiteConverter(
        graph_def,
        input_tensors,
        output_tensors,
        input_arrays_with_shape=None,
        output_arrays=None,
        experimental_debug_info_func=None
    )

This is used to convert from a TensorFlow GraphDef, SavedModel or tf.keras
model into either a TFLite FlatBuffer or graph visualization.

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| ## Args ||
|---|---|
| `graph_def` | Frozen TensorFlow GraphDef. |
| `input_tensors` | List of input tensors. Type and shape are computed using `foo.shape` and `foo.dtype`. |
| `output_tensors` | List of output tensors (only .name is used from this). |
| `input_arrays_with_shape` | Tuple of strings representing input tensor names and list of integers representing input shapes (e.g., \[("foo" : \[1, 16, 16, 3\])\]). Use only when graph cannot be loaded into TensorFlow and when `input_tensors` and `output_tensors` are None. (default None) |
| `output_arrays` | List of output tensors to freeze graph with. Use only when graph cannot be loaded into TensorFlow and when `input_tensors` and `output_tensors` are None. (default None) |
| `experimental_debug_info_func` | An experimental function to retrieve the graph debug info for a set of nodes from the `graph_def`. |

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| ## Raises ||
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| `ValueError` | Invalid arguments. |

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| ## Attributes ||
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| `optimizations` | Experimental flag, subject to change. Set of optimizations to apply. e.g {tf.lite.Optimize.DEFAULT}. (default None, must be None or a set of values of type [`tf.lite.Optimize`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/lite/Optimize)) |
| `representative_dataset` | A generator function used for integer quantization where each generated sample has the same order, type and shape as the inputs to the model. Usually, this is a small subset of a few hundred samples randomly chosen, in no particular order, from the training or evaluation dataset. This is an optional attribute, but required for full integer quantization, i.e, if [`tf.int8`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf#int8) is the only supported type in `target_spec.supported_types`. Refer to [`tf.lite.RepresentativeDataset`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/lite/RepresentativeDataset). (default None) |
| `target_spec` | Experimental flag, subject to change. Specifications of target device, including supported ops set, supported types and a set of user's defined TensorFlow operators required in the TensorFlow Lite runtime. Refer to [`tf.lite.TargetSpec`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/lite/TargetSpec). |
| `inference_type` | Data type of numeric arrays, excluding the input layer. (default tf.float32, must be in {tf.float32, tf.int8, tf.uint8}) |
| `inference_input_type` | Data type of the numeric arrays in the input layer. If `inference_input_type` is in {tf.int8, tf.uint8}, then `quantized_input_stats` must be provided. (default is the value assigned to `inference_type`, must be in {tf.float32, tf.int8, tf.uint8}) |
| `inference_output_type` | Data type of the numeric arrays in the output layer. (default is the value assigned to `inference_type`, must be in {tf.float32, tf.int8, tf.uint8}) |
| `quantized_input_stats` | Map of input tensor names to a tuple of floats representing the mean and standard deviation of the training data. (e.g., {"foo" : (0., 1.)}). Required if `inference_input_type` is tf.int8 or tf.uint8. (default None) |
| `default_ranges_stats` | Tuple of integers (min, max) representing range values for all numeric arrays without a specified range. Intended for experimenting with quantization via "dummy quantization". (default None) |
| `allow_custom_ops` | Boolean indicating whether to allow custom operations. When False any unknown operation is an error. When True, custom ops are created for any op that is unknown. The developer will need to provide these to the TensorFlow Lite runtime with a custom resolver. (default False) |
| `drop_control_dependency` | Boolean indicating whether to drop control dependencies silently. This is due to TFLite not supporting control dependencies. (default True) |
| `reorder_across_fake_quant` | Boolean indicating whether to reorder FakeQuant nodes in unexpected locations. Used when the location of the FakeQuant nodes is preventing graph transformations necessary to convert the graph. Results in a graph that differs from the quantized training graph, potentially causing differing arithmetic behavior. (default False) |
| `change_concat_input_ranges` | Boolean to change behavior of min/max ranges for inputs and outputs of the concat operator for quantized models. Changes the ranges of concat operator overlap when true. (default False) |
| `output_format` | Output file format. (default tf.compat.v1.lite.constants.TFLITE, must be in {tf.compat.v1.lite.constants.TFLITE, tf.compat.v1.lite.constants.GRAPHVIZ_DOT}) |
| `dump_graphviz_dir` | Full filepath of folder to dump the graphs at various stages of processing GraphViz .dot files. Preferred over `output_format=tf.compat.v1.lite.constants.GRAPHVIZ_DOT` in order to keep the requirements of the output file. (default None) |
| `dump_graphviz_video` | Boolean indicating whether to dump the GraphViz .dot files after every graph transformation. Requires the `dump_graphviz_dir` flag to be specified. (default False) |
| `conversion_summary_dir` | Full path of the directory to store conversion logs. (default None) |
| `exclude_conversion_metadata` | Whether not to embed the conversion metadata into the converted model. (default False) |
| `target_ops` | Deprecated. Please use `target_spec.supported_ops` instead. |
| `post_training_quantize` | Deprecated. Please use `optimizations` instead and set it to `{tf.lite.Optimize.DEFAULT}`. (default False) |
| `experimental_new_converter` | Experimental flag, subject to change. Enables MLIR-based conversion. (default True) |
| `experimental_new_quantizer` | Experimental flag, subject to change. Enables MLIR-based quantization conversion instead of Flatbuffer-based conversion. (default True) Example usage: `python # Converting a GraphDef from session. converter = tf.compat.v1.lite.TFLiteConverter.from_session( sess, in_tensors, out_tensors) tflite_model = converter.convert() open("converted_model.tflite", "wb").write(tflite_model) # Converting a GraphDef from file. converter = tf.compat.v1.lite.TFLiteConverter.from_frozen_graph( graph_def_file, input_arrays, output_arrays) tflite_model = converter.convert() open("converted_model.tflite", "wb").write(tflite_model) # Converting a SavedModel. converter = tf.compat.v1.lite.TFLiteConverter.from_saved_model( saved_model_dir) tflite_model = converter.convert() open("converted_model.tflite", "wb").write(tflite_model) # Converting a tf.keras model. converter = tf.compat.v1.lite.TFLiteConverter.from_keras_model_file( keras_model) tflite_model = converter.convert() open("converted_model.tflite", "wb").write(tflite_model)` |

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## Methods

### `convert`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/lite/python/lite.py#L3116-L3128)

    convert()

Converts a TensorFlow GraphDef based on instance variables.

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| Returns ||
|---|---|
| The converted data in serialized format. Either a TFLite Flatbuffer or a Graphviz graph depending on value in `output_format`. ||

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| Raises ||
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| `ValueError` | Input shape is not specified. None value for dimension in input_tensor. |

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### `from_frozen_graph`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/lite/python/lite.py#L2914-L3014)

    @classmethod
    from_frozen_graph(
        graph_def_file, input_arrays, output_arrays, input_shapes=None
    )

Creates a TFLiteConverter class from a file containing a frozen GraphDef.

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| Args ||
|---|---|
| `graph_def_file` | Full filepath of file containing frozen GraphDef. |
| `input_arrays` | List of input tensors to freeze graph with. |
| `output_arrays` | List of output tensors to freeze graph with. |
| `input_shapes` | Dict of strings representing input tensor names to list of integers representing input shapes (e.g., {"foo" : \[1, 16, 16, 3\]}). Automatically determined when input shapes is None (e.g., {"foo" : None}). (default None) |

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| Returns ||
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| TFLiteConverter class. ||

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| Raises ||
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| `IOError` | File not found. Unable to parse input file. |
| `ValueError` | The graph is not frozen. input_arrays or output_arrays contains an invalid tensor name. input_shapes is not correctly defined when required |

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### `from_keras_model_file`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/lite/python/lite.py#L3079-L3113)

    @classmethod
    from_keras_model_file(
        model_file,
        input_arrays=None,
        input_shapes=None,
        output_arrays=None,
        custom_objects=None
    )

Creates a TFLiteConverter class from a tf.keras model file.

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| Args ||
|---|---|
| `model_file` | Full filepath of HDF5 file containing the tf.keras model. |
| `input_arrays` | List of input tensors to freeze graph with. Uses input arrays from SignatureDef when none are provided. (default None) |
| `input_shapes` | Dict of strings representing input tensor names to list of integers representing input shapes (e.g., {"foo" : \[1, 16, 16, 3\]}). Automatically determined when input shapes is None (e.g., {"foo" : None}). (default None) |
| `output_arrays` | List of output tensors to freeze graph with. Uses output arrays from SignatureDef when none are provided. (default None) |
| `custom_objects` | Dict mapping names (strings) to custom classes or functions to be considered during model deserialization. (default None) |

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| Returns ||
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| TFLiteConverter class. ||

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### `from_saved_model`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/lite/python/lite.py#L3016-L3077)

    @classmethod
    from_saved_model(
        saved_model_dir,
        input_arrays=None,
        input_shapes=None,
        output_arrays=None,
        tag_set=None,
        signature_key=None
    )

Creates a TFLiteConverter class from a SavedModel.

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| Args ||
|---|---|
| `saved_model_dir` | SavedModel directory to convert. |
| `input_arrays` | List of input tensors to freeze graph with. Uses input arrays from SignatureDef when none are provided. (default None) |
| `input_shapes` | Dict of strings representing input tensor names to list of integers representing input shapes (e.g., {"foo" : \[1, 16, 16, 3\]}). Automatically determined when input shapes is None (e.g., {"foo" : None}). (default None) |
| `output_arrays` | List of output tensors to freeze graph with. Uses output arrays from SignatureDef when none are provided. (default None) |
| `tag_set` | Set of tags identifying the MetaGraphDef within the SavedModel to analyze. All tags in the tag set must be present. (default {tf.saved_model.SERVING}) |
| `signature_key` | Key identifying SignatureDef containing inputs and outputs. (default tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY) |

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| Returns ||
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| TFLiteConverter class. ||

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### `from_session`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/lite/python/lite.py#L2888-L2912)

    @classmethod
    from_session(
        sess, input_tensors, output_tensors
    )

Creates a TFLiteConverter class from a TensorFlow Session.

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| Args ||
|---|---|
| `sess` | TensorFlow Session. |
| `input_tensors` | List of input tensors. Type and shape are computed using `foo.shape` and `foo.dtype`. |
| `output_tensors` | List of output tensors (only .name is used from this). |

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| Returns ||
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| TFLiteConverter class. ||

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### `get_input_arrays`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/lite/python/lite.py#L2372-L2381)

    get_input_arrays()

Returns a list of the names of the input tensors.

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| Returns ||
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| List of strings. ||

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