Comments (1)
Hi, yes this is possible. Just export the model as a SavedModel. Use saved_model_cli
to see all the available inputs/outputs and use tfgo to load the model, feed the model using the correct input, and fetch from the correct output.
If mobilenet (that I imagine you download from somewhere) doesn't come with the output layer you need (some layer to treat as output for getting the vector embeddings) you need to use Python to export a new SavedModel and explicitly set your desired layer as output
from tfgo.
Related Issues (20)
- loading model HOT 2
- cc1.exe: sorry, unimplemented: 64-bit mode not compiled in HOT 1
- Error: W tensorflow/core/framework/op_kernel.cc:1745] OP_REQUIRES failed at lookup_table_op.cc:929 : FAILED_PRECONDITION: Table not initialized. HOT 1
- SparseTensor support HOT 2
- Failed to go get tfgo HOT 3
- tensor example support HOT 6
- Potential memory leak on reloading model HOT 8
- libtensorflow.so: .dynsym local symbol at index 3 (>= sh_info of 3) HOT 2
- Matching between output tensor name and prediction HOT 7
- Thread safety HOT 2
- Panic vs Error HOT 2
- Reading jpg image from disk and passing as input to a pretrained model loaded from disk HOT 4
- Error building "Getting Started" example: rebuild TensorFlow with the appropriate compiler flags HOT 5
- Can I use tfgo for tensorflow 2.11.x? HOT 2
- Convert opencv's `mat` to tfgo's `tensor` HOT 7
- Dark Souls level frustration with compilation project with this library HOT 1
- Discrepancy between python and go outputs HOT 2
- tfgo Object detection
- Panic when loading a keras LSTM Model HOT 2
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from tfgo.