Comments (1)
Hi @damianospark, if you save a model with custom objects (like AdaBelief or some custom layers), you will also need to specify the custom objects when you are loading the model, so please try the following code to load the model:
model = tf.keras.models.load_model('./model.h5', custom_objects={'AdaBeliefOptimizer':AdaBeliefOptimizer})
Another option is to load the model without compiling, so you don't need to keep the code:
model = tf.keras.models.load_model('./model.h5', compile=False)
from adabelief-optimizer.
Related Issues (20)
- Please add a license HOT 1
- Upgrade with Adas optimizer HOT 3
- MSVAG HOT 1
- Why does g_t substract m_t, instead of m_{t-1} ? HOT 1
- On imagenet accuracy result 70.08 HOT 1
- Documentation (at least for TF) and weight_decouple is not an option HOT 2
- FileNotFoundError for ImageNet HOT 1
- Changing init learning rate HOT 2
- Question about SGD optimizer in LSTM experiments HOT 1
- Compatibility with warmup HOT 2
- Inconsistent computation of weight_decay and grad_residual among pytorch versions HOT 5
- Your method is just equivalent to SGD with a changable global learning rate. HOT 3
- Some questions related to import adabelief HOT 2
- Tensorflow restoration issue HOT 1
- weight_decouple in adabelief tf HOT 1
- Inconsistent use of epsilon HOT 4
- Suppressing weight decoupling and rectification messages HOT 1
- The problem of reproducing the result of ImageNet HOT 4
- AttributeError: 'AdaBeliefOptimizer' object has no attribute '_set_hyper' HOT 4
- loss become nan when beta1=0
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from adabelief-optimizer.