'What is difference between .h5 and .hdf5 format? [closed]
Suppose we have saved our model as h5 then we can load model using load_model function of keras.. I have gone through multiple github links where the author provides weights file (.h5 or .hdf5 file).
So can we use those pretrained weights/model using load_model?
what is difference between load_model and load_weights?
Can I load weight file with load_model (keras) or should I use (load_weight). If we are loading weight then we mush also load whole architecture of the model..
Please provide deeper understanding by taking some example..
Solution 1:[1]
Defining the model
model = ...
Training the model
model.fit...
Saving the model:
model.save('model_topology.h5)
Saving the weights:
model.save_weights(`weights.h5`)
When calling load_model(), if you want to run inference, you have to call load weights to load the pretrained weights.
model = load_model('model_toplogy.h5)
model.load_weights('weights.h5)
This is true when dealing with models saved in the HDF5 format (.h5).
Tensorflow provides another format for saving models: the saved_model format.
The save_model format saves the model topology (graph) and trained parameters (weights and biases) in a directory. This enables loading a model with one command.
model = tf.saved_model.load(path_to_dir)
About h5 and HDF5:
h5 is just a shortened extension for files saved in the HDF5 format.
Sources
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Source: Stack Overflow
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