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CNN LITE HOW TO
CNN LITE CODE

Convert the TF model to a TFLite model and run inference.

Supported in TF: The error occurs because the TF op is missing from the (leave a comment if your request hasn’t already been mentioned) orĮrror. If you want to generate a model with TFLite ops only, you can either add a

Using the TF op in the TFLite model (recommended). Solution: The error occurs as your model has TF ops that don't have aĬorresponding TFLite implementation. The following are common conversion errors and their solutions:Įrror: Some ops are not supported by the native TFLite runtime, you canĮnable TF kernels fallback using TF Select.

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Specific wrapper code when deploying models on devices. Which can further reduce your model latency and size with minimal loss inĪdd metadata, which makes it easier to create platform # > converter = tf._concrete_functions()Ĭonverter = tf._concrete_functions(, # from_concrete_functions API is able to work when there is only the first # Notes that for the versions earlier than TensorFlow 2.7, the

# (to generate a SavedModel) tf.saved_model.save(model, "saved_model_tf_dir")Ĭoncrete_func = model._call_.get_concrete_function() # (ro run your model) result = Squared(5.0) # This prints "25.0" # Create a model using low-level tf.* APIs

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The following example shows how to convert # (to generate a SavedModel) tf.saved_model.save(model, "saved_model_keras_dir")Ĭonverter = tf._keras_model(model) Model.fit(x=, y=, epochs=5) # train the model pile(optimizer='sgd', loss='mean_squared_error') # compile the model # Create a model using high-level tf.keras.* APIs The following example shows how to convert aĬonverter = tf._saved_model(saved_model_dir) # path to the SavedModel directory Note: The following sections assume you've both installed TensorFlow 2.x and It to a Frozen GraphDef file and then use this API as shown If you have checkpoints, then first convert tf.compat.v1._frozen_graph(): Converts aįrozen GraphDef from a file.tf.compat.v1._session(): Converts a GraphDef.tf.compat.v1._keras_model_file(): Converts a.tf.compat.v1._saved_model(): Converts a.tf._saved_model() ( recommended): Converts.Result, you have the following three options (examples are in the next few The low-level tf.* APIs (from which you generate concrete functions). Generated either using the high-level tf.keras.* APIs (a Keras model) or You have the following two options: ( if you'veĪ TensorFlow 2.x model is stored using the SavedModel format and is

Print(tf._version_) and to learn more about the TensorFlow Lite converterĪPI, run print(help(tf.lite.TFLiteConverter)). Helper code: To identify the installed TensorFlow version, run Note: In case you encounter any issues during model conversion, create a

Command line: This only supports basic model conversion.

Optimizations, add metadata and has many more features.

Python API ( recommended): This makes it easier toĬonvert models as part of the model development pipeline, apply.

You have the following two options for using the The TensorFlow Lite converter takes a TensorFlow model and generates a