Refer to this to train a GAN model for MNIST dataset, I want to save a model and restore it for further prediction. After having some understanding of Saving and Importing a Tensorflow Model I am able to save and restore some variables of inputs and outputs but for this network I am able to save the model only after some specific iterations and not able to predict some output.
Did you refer to this guide? It explains very clearly how to load and save tensorflow models in all possible formats.
If you are new to ML, I'd recommend you give Keras a try first, which is much easier to use. See https://keras.io/getting-started/faq/#how-can-i-save-a-keras-model, pretty much you can use:
model.save('my_model.h5')
to save your model to disk.
model = load_model('my_model.h5')
to load your model and make prediction
Related
I am new to Deep learning and I want to know, how can I save the final model in Pytorch? I tried some things that were mentioned but I got confused with, how to save the model and how to load it back?
to save:
# save the weights of the model to a .pt file
torch.save(model.state_dict(), "your_model_path.pt")
to load:
# load your model architecture/module
model = YourModel()
# fill your architecture with the trained weights
model.load_state_dict(torch.load("your_model_path.pt"))
I am struggling with restoring a keras model from a .pb file. I have seen a couple of posts explaining how to do inference using a model saved in .pb format, but what I need is to load the weights into a keras.Model class.
I have a function that returns an instance of the said original model, but untrained. I want to restore the weights of that model from the .pb file. My goal is to then truncate the model and use the truncated model for other purposes.
So far the furthest I've gotten is using the tf.saved_model.load(session, ['serving'], export_dir) function to get a tensorflow.core.protobuf.meta_graph_pb2.MetaGraphDef object. From here I can access the graph_def attribute, and from there the nodes.
How can I go from that to getting the weights and then loading those into the instance of the untrained keras Model?
Maybe if that's not doable there is a way to "truncate" the graph_def somehow and then make inference using that?
I am using model.save("cnn.model") and model.save("cnn.h5") to save the model after training.
What is the difference of the saving the model in 2 different extensions?
File name, which includes the extension, doesn't matter. Whatever it is, Keras will save a HDF5 formatted model into that file.
Doc: How can I save a Keras model?
You can use model.save(filepath) to save a Keras model into a single
HDF5 file which will contain:
the architecture of the model, allowing to re-create the model
the weights of the model
the training configuration (loss, optimizer)
the state of the optimizer, allowing to resume training exactly where you left off.
I am working on GANS and I need to save the model after my working hours. And then I have to retrain that previously saved model again where it was saved. I am saving these three models to continue training later on.
Discriminator Model.h5
Generator Model.h5
Generator-on-Discriminator Model.h5
For these models, I am using perceptual loss and Wasserstein loss. But when I load_model to retrain that saved model again it encounters the following error.
Unknown loss function:wasserstein_loss
I have also tried Discriminator.compile(loss=Wasserstein loss) but this still not solving my issue. Can anyone of you please guide me over this and can tell me wither its possible to retrain a saved model using train_on_batch().
solved at my own
Defining custom_objects={'wassertein_loss':wassertein_loss} along with path while loading the model solved my issue. i.e.
Discriminator=load_model(model_path, custom_objects={'wassertein_loss':wassertein_loss} )
Yesterday night I let a neural network model training and that took time, so I thought to add a statement to save weights model.save_weights('first_try.h5')
Now as I had the file, I want to benefit saved file.
Prediction is like
pred=model.predict_generator(test_generator, steps=4124, verbose=1)
If you saved your model's weights you can load using load_weights method. But first you have to define your model structure.
e.g.
model = method_to_create_the_model()
model.load_weights("path_to_weight_file")