Comments (1)
Hey, thanks for checking out Larq! If your dataset is in TensorFlow Datasets, the easiest way is to define a Zookeeper @component
for it in larq_zoo/training/datasets.py
; you can then call it using the CLI as described in the docs.
Otherwise, the easiest way is to write your own training code (see Keras guide) and only importing the model from Larq Zoo, for example:
import larq_zoo as lqz
dataset = ... # https://www.tensorflow.org/guide/data
model = lqz.literature.BiRealNet() # https://docs.larq.dev/zoo/#available-models
model.compile(...)
model.fit(...)
Let me know if you have more questions!
from zoo.
Related Issues (20)
- Unexpected behavior of the "include_top" argument HOT 3
- Unexpected behavior of the "preprocess_input" function HOT 1
- Make ordering of docstring constistent
- Snapshot tests of model summaries HOT 2
- RFC: structure change HOT 3
- Add model accuracies to docstrings
- QuickNet(Large) models don't match released h5 files
- Support TensorFlow 2.2 HOT 3
- QuickNet model and flip_ratio metric do not work together HOT 3
- Update weights and parameters in docstrings
- QuickNet no-top models pretrained weights are not working as expected HOT 1
- No 'sota' module HOT 2
- Speech Models HOT 2
- About RealToBinaryNet model HOT 14
- Intermediate results of training R2B model HOT 5
- Reproducing R2B model HOT 6
- Help, no logs are printed! HOT 2
- The usage of data.cache() causes the run out of memory. HOT 5
- Drop Python 3.6 support
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from zoo.