# Converter command-line reference

This page is complete reference of command-line flags used by the TensorFlow Lite Converter's command line starting from TensorFlow 1.9 up until the most recent build of TensorFlow.

## High-level flags

The following high level flags specify the details of the input and output files. The flag --output_file is always required. Additionally, either --graph_def_file, --saved_model_dir or --keras_model_file is required.

• --output_file. Type: string. Specifies the full path of the output file.
• --graph_def_file. Type: string. Specifies the full path of the input GraphDef file frozen using freeze_graph.py.
• --saved_model_dir. Type: string. Specifies the full path to the directory containing the SavedModel.
• --keras_model_file. Type: string. Specifies the full path of the HDF5 file containing the tf.keras model.
• --output_format. Type: string. Default: TFLITE. Specifies the format of the output file. Allowed values:
• TFLITE: TensorFlow Lite FlatBuffer format.
• GRAPHVIZ_DOT: GraphViz .dot format containing a visualization of the graph after graph transformations.
• Note that passing GRAPHVIZ_DOT to --output_format leads to loss of TFLite specific transformations. Therefore, the resulting visualization may not reflect the final set of graph transformations. To get a final visualization with all graph transformations use --dump_graphviz_dir instead.

The following flags specify optional parameters when using SavedModels.

• --saved_model_tag_set. Type: string. Default: kSavedModelTagServe. Specifies a comma-separated set of tags identifying the MetaGraphDef within the SavedModel to analyze. All tags in the tag set must be specified.
• --saved_model_signature_key. Type: string. Default: DEFAULT_SERVING_SIGNATURE_DEF_KEY. Specifies the key identifying the SignatureDef containing inputs and outputs.

## Model flags

Model flags provide additional information about the model stored in the input file.

• --input_arrays. Type: comma-separated list of strings. Specifies the list of names of input activation tensors.
• --output_arrays. Type: comma-separated list of strings. Specifies the list of names of output activation tensors.

The following flags define properties of the input tensors. Each item in the --input_arrays flag should correspond to each item in the following flags based on index.

• --input_shapes. Type: colon-separated list of comma-separated lists of integers. Each comma-separated list of integers gives the shape of one of the input arrays specified in TensorFlow convention.
• Example: --input_shapes=1,60,80,3 for a typical vision model means a batch size of 1, an input image height of 60, an input image width of 80, and an input image depth of 3 (representing RGB channels).
• Example: --input_arrays=foo,bar --input_shapes=2,3:4,5,6 means "foo" has a shape of [2, 3] and "bar" has a shape of [4, 5, 6].
• --std_dev_values, --mean_values. Type: comma-separated list of floats. These specify the (de-)quantization parameters of the input array, when it is quantized. This is only needed if inference_input_type is QUANTIZED_UINT8.
• The meaning of mean_values and std_dev_values is as follows: each quantized value in the quantized input array will be interpreted as a mathematical real number (i.e. as an input activation value) according to the following formula:
• real_value = (quantized_input_value - mean_value) / std_dev_value.
• When performing float inference (--inference_type=FLOAT) on a quantized input, the quantized input would be immediately dequantized by the inference code according to the above formula, before proceeding with float inference.
• When performing quantized inference (--inference_type=QUANTIZED_UINT8), no dequantization is performed by the inference code. However, the quantization parameters of all arrays, including those of the input arrays as specified by mean_value and std_dev_value, determine the fixed-point multipliers used in the quantized inference code. mean_value must be an integer when performing quantized inference.

## Transformation flags

Transformation flags specify options of the transformations to be applied to the graph, i.e. they specify requested properties that the output file should have.

• --inference_type. Type: string. Default: FLOAT. Data type of all real-number arrays in the output file except for input arrays (defined by --inference_input_type). Must be {FLOAT, QUANTIZED_UINT8}.

This flag only impacts real-number arrays including float and quantized arrays. This excludes all other data types including plain integer arrays and string arrays. Specifically:

• If FLOAT, then real-numbers arrays will be of type float in the output file. If they were quantized in the input file, then they get dequantized.
• If QUANTIZED_UINT8, then real-numbers arrays will be quantized as uint8 in the output file. If they were float in the input file, then they get quantized.
• --inference_input_type. Type: string. Data type of a real-number input array in the output file. By default the --inference_type is used as type of all of the input arrays. Flag is primarily intended for generating a float-point graph with a quantized input array. A Dequantized operator is added immediately after the input array. Must be {FLOAT, QUANTIZED_UINT8}.

The flag is typically used for vision models taking a bitmap as input but requiring floating-point inference. For such image models, the uint8 input is quantized and the quantization parameters used for such input arrays are their mean_value and std_dev_value parameters.

• --default_ranges_min, --default_ranges_max. Type: floating-point. Default value for the (min, max) range values used for all arrays without a specified range. Allows user to proceed with quantization of non-quantized or incorrectly-quantized input files. These flags produce models with low accuracy. They are intended for easy experimentation with quantization via "dummy quantization".

• --drop_control_dependency. Type: boolean. Default: True. Indicates whether to drop control dependencies silently. This is due to TensorFlow Lite not supporting control dependencies.

• --reorder_across_fake_quant. Type: boolean. Default: False. Indicates 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.

• --allow_custom_ops. Type: string. Default: False. Indicates 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.

• --post_training_quantize. Type: boolean. Default: False. Boolean indicating whether to quantize the weights of the converted float model. Model size will be reduced and there will be latency improvements (at the cost of accuracy).

## Logging flags

The following flags generate graph visualizations of the graph as GraphViz .dot files at various points during graph transformations:

• --dump_graphviz_dir. Type: string. Specifies the full path of the directory to output GraphViz .dot files. Outputs the graph immediately after reading in the graph and after all of the transformations have been completed.
• --dump_graphviz_video. Type: boolean. Outputs GraphViz after every graph transformation. Requires --dump_graphviz_dir to be specified.