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tf.compat.v1.lite.TFLiteConverter

Convert a TensorFlow model into output_format.

Used in the notebooks

Used in the guide

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

Example usage:

# 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)

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.

ValueError Invalid arguments.

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)
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 is the only supported type in target_spec.supported_types. Refer to 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.
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)
target_ops Deprecated. Please use target_spec.supported_ops instead.
post_training_quantize Deprecated. Please use optimization