Comments (3)
@Blackoowhite Some TensorFlow operations might not be directly supported by TFLite. During conversion, the converter checks for compatibility and throws errors if it encounters unsupported operations.
In order to expedite the trouble-shooting process, please provide a code snippet to reproduce the issue reported here. Also find resources on exporting models to SavedModel format in the TensorFlow documentation here.
Thank you!
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Thanks everyone, I havd found the issue. I must import keras like tf.python.keras..... instead of tf.keras......
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Related Issues (20)
- Failed to build TF Lite benchmark tool on Windows
- The signatures in SavedModel do not contain serving_default when a subclass of the keras model has multiple inputs HOT 2
- MobileNetV3 quantization HOT 5
- Crash in `tf.raw_ops.ResizeNearestNeighbor/ResizeNearestNeighborGrad/ResizeArea/ResizeBicubic/ResizeBilinear` HOT 3
- Title: TensorFlow/Keras Integration Error: A KerasTensor cannot be used as input to a TensorFlow function HOT 1
- Add stop_evaluating attribute to Model class and add associated modification to evaluate method HOT 1
- Compilation of mlir:tf-opt fails with error "The repository '@llvm_zlib' could not be resolved" HOT 4
- GPU MaxPool gradient ops do not yet have a deterministic XLA implementation HOT 1
- DXGI format does not support cross-API sharing HOT 1
- tflite model maker not install HOT 1
- AttributeError: 'ModelCheckpoint' object has no attribute '_implements_train_batch_hooks' in MoViNet Streaming Model Training HOT 1
- Crash in `tf.raw_ops.SparseCountSparseOutput ` HOT 1
- 16KB so support HOT 1
- Segmentation fault (core dumped) in tf.raw_ops.FractionalMaxPoolGrad when col_pooling_sequence is a very small negative number
- Aborted (core dumped) in `tf.raw_ops.SparseReduceSum\tf.raw_ops.SparseReduceMax` HOT 1
- Aborted (core dumped) in `tf.experimental.numpy.diag/tf.compat.v1.linalg.diag/tf.experimental.numpy.diagflat/tf.keras.ops.diag` HOT 1
- Transfer learning and fine-tuning doc seems to have unexpected results
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