# TensorFlow 1.x compatibility

The `tf.lite.TFLiteConverter` was updated between TensorFlow 1.X and 2.0. This
document explains the differences between the 1.X and 2.0 versions of the
converter, and provides information about how to use the 1.X version if
required.

## Summary of changes in Python API between 1.X and 2.0 <a name="differences"></a>

The following section summarizes the changes in the Python API from 1.X to 2.0.
If any of the changes raise concerns, please file a
[GitHub issue](https://github.com/tensorflow/tensorflow/issues).

### Formats supported by `TFLiteConverter`

The 2.0 version of the converter supports SavedModel and Keras model files
generated in both 1.X and 2.0. However, the conversion process no longer
supports "frozen graph" `GraphDef` files generated in 1.X.

#### Converting frozen graphs

Users who want to convert frozen graph `GraphDef` files (`.pb` files) to
TensorFlow Lite should use `tf.compat.v1.lite.TFLiteConverter`.

The following snippet shows a frozen graph file being converted:

```python
# Path to the frozen graph file
graph_def_file = 'frozen_graph.pb'
# A list of the names of the model's input tensors
input_arrays = ['input_name']
# A list of the names of the model's output tensors
output_arrays = ['output_name']
# Load and convert the frozen graph
converter = lite.TFLiteConverter.from_frozen_graph(
  graph_def_file, input_arrays, output_arrays)
tflite_model = converter.convert()
# Write the converted model to disk
open("converted_model.tflite", "wb").write(tflite_model)
```

### Quantization-aware training

The following attributes and methods associated with
[quantization-aware training](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/quantize)
have been removed from `TFLiteConverter` in TensorFlow 2.0:

*   `inference_type`
*   `inference_input_type`
*   `quantized_input_stats`
*   `default_ranges_stats`
*   `reorder_across_fake_quant`
*   `change_concat_input_ranges`
*   `post_training_quantize` - Deprecated in the 1.X API
*   `get_input_arrays()`

The rewriter function that supports quantization-aware training does not support
models generated by TensorFlow 2.0. Additionally, TensorFlow Lite’s quantization
API is being reworked and streamlined in a direction that supports
quantization-aware training through the Keras API. These attributes will be
removed in the 2.0 API until the new quantization API is launched. Users who
want to convert models generated by the rewriter function can use
`tf.compat.v1.lite.TFLiteConverter`.

### Changes to `TFLiteConverter` attributes

The `target_ops` attribute has become an attribute of `TargetSpec` and renamed
to `supported_ops` in line with future additions to the optimization framework.

Additionally, the following attributes have been removed:

*   `drop_control_dependency` (default: `True`)
*   _Graph visualization_ - The recommended approach for visualizing a
    TensorFlow Lite graph in TensorFlow 2.0 will be to use
    [visualize.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/tools/visualize.py).
    Unlike GraphViz, it enables users to visualize the graph after post training
    quantization has occurred. The following attributes related to graph
    visualization will be removed:
    *   `output_format`
    *   `dump_graphviz_dir`
    *   `dump_graphviz_video`

### General API changes

The following section explains several significant API changes between
TensorFlow 1.X and 2.0.

#### Conversion methods

The following methods that were previously deprecated in 1.X will no longer be
exported in 2.0:

*   `lite.toco_convert`
*   `lite.TocoConverter`

#### `lite.constants`

The `lite.constants` API was removed in 2.0 in order to decrease duplication
between TensorFlow and TensorFlow Lite. The following list maps the
`lite.constant` type to the TensorFlow type:

*   `lite.constants.FLOAT`: `tf.float32`
*   `lite.constants.INT8`: `tf.int8`
*   `lite.constants.INT32`: `tf.int32`
*   `lite.constants.INT64`: `tf.int64`
*   `lite.constants.STRING`: `tf.string`
*   `lite.constants.QUANTIZED_UINT8`: `tf.uint8`

Additionally, `lite.constants.TFLITE` and `lite.constants.GRAPHVIZ_DOT` were
removed due to the deprecation of the `output_format` flag in `TFLiteConverter`.

#### `lite.OpHint`

The `OpHint` API is currently not available in 2.0 due to an incompatibility
with the 2.0 APIs. This API enables conversion of LSTM based models. Support for
LSTMs in 2.0 is being investigated. All related `lite.experimental` APIs have
been removed due to this issue.