How to directly rebuild the model from saved zip file?

Currently I want to rebuild the stable baseline model in keras and pytorch. It seems that the model cannot directly save to a h5 file. Thus I want to rebuild from the zip file save in this code.

log_dir = "test_save/"
model_dqn.save(log_dir + "pong")

This file contains the model structure in parameter_list, and weights in parameters. However, I cannot rebuild the model from these two files with the code below:

# load json and create model
json_file = open('parameter_list', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)

loaded_model.load_weights("parameters")

[xinghua-qu]

Please take a look at the documentation on exporting models. You have to get the parameters as numpy arrays with get_parameters, and map these to correct Keras layers. I had a mapping like this for Keras model to stable-baselines. The key tells stable-baselines parameter name, and value is a lambda function that retrieves corresponding layer from a Keras model.

# Mapping from stable-baselines agent variable
# name to a function that takes in keras model
# and assigns right variables
CNN_PARAMS = {
    "model/cnn1/w:0": lambda model: model.get_layer("conv2d_1").get_weights()[0],
    "model/cnn1/b:0": lambda model: model.get_layer("conv2d_1").get_weights()[1][None, :, None, None],
    "model/cnn2/w:0": lambda model: model.get_layer("conv2d_2").get_weights()[0],
    "model/cnn2/b:0": lambda model: model.get_layer("conv2d_2").get_weights()[1][None, :, None, None],
    "model/cnn3/w:0": lambda model: model.get_layer("conv2d_3").get_weights()[0],
    "model/cnn3/b:0": lambda model: model.get_layer("conv2d_3").get_weights()[1][None, :, None, None],
    "model/cnn_fc1/w:0": lambda model: model.get_layer("dense_1").get_weights()[0],
    "model/cnn_fc1/b:0": lambda model: model.get_layer("dense_1").get_weights()[1]
}

Another way would be: extracting the dqn model from the build TF graph and save it independently.

This is planned for v3 with PyTorch backend.

Thanks for your reply. This mapping looks like ignore the last fully connected layer (i.e., from 512 hidden nodes to the action outputs). It would be soooo nice if you can share one example for loading from pkl file and then converting weights to a keras model. In that way, I think people can also save models to other formats (e.g., .h5 that is mentioned by ChunJyeBehBeh). However, this is based on your team has such an extension plan. Thanks so much for your help anyway:)

[araffin]

Note to self: For v3, I should look into a possible common format for saving the model architecture as well, if in any way possible (in a convenient way).

ONNX export ? or tracing using pytorch jit?