I have been getting an error when try to load a model that I trained.
model_path = r'I:\\ECGMODELCP\\0.467-0.840-010-0.408-0.860.reg.hdf5'
model = keras.models.load_model(model_path)
ValueError: Unknown regularizer: l2_cond
Ive tried
model = keras.models.load_model(model_path, custom_objects={'l2_cond': l2_cond(weight_matrix)})
But get an error of weight_matrix not defined. l2_cond is a custom kernal regularizer that I defined and depends on the weight matrix of the last layer of my model. Any help is appreciated
I figured it out already. I just loaded the weights of the model into an identical model's architecture.
Related
I have created a Pytorch object from the class Sequential (see official page).
As they suggest, I am saving it using the command torch.save(model.state_dict(), PATH).
It seems that everything has worked fine, since when I use torch.load(PATH) in another file I get
an ordered Dict like
'0.weight': tensor([[ 0.1202, ...]]) ,
'0.bias': tensor([ 0.1422, ...]) ,
...
with all the shapes of the tensors being correct. However, when doing
model = Sequential()
model.load_state_dict(torch.load(PATH))
I get the error
RuntimeError: Error(s) in loading state_dict for Sequential:
Unexpected key(s) in state_dict: "0.weight", "0.bias", "2.weight", "2.bias", "4.weight", "4.bias".
When trying to load, the model you are trying to load into (model) is an empty Sequential object with no layers. On the other hand, looking at the error message, the state dictionary of the model you are trying to load from indicates that it has at least five layers, with the first, third, and fifth layers containing a weight and bias parameter. This is a mismatch since the corresponding layers and parameters do not exist in model.
To fix this, model should have the same architecture as the saved model you are trying to load. Since you say you saved the original model yourself, use an identical initialization step when creating model.
I am working through the Barlow Twins tutorial with PyTorch Lightning and I am having trouble loading the encoder portion of the model using the checkpoint after training.
During model training, checkpoints are saved with ModelCheckpoint. In the tutorial, the author offers two options for then getting the encoder portion of the model with trained weights: 1) calling model.encoder (model has to have been trained in the active kernel for this to work) or 2) loading the trained model with:
ckpt_model = torch.load('[checkpoint name].ckpt')
And then calling
encoder = ckpt_model.encoder
I would like to be able to load the model/encoder from a saved checkpoint but when I try to do this, I get the error: AttributeError: 'dict' object has no attribute 'encoder'
This seems to make sense to me because the model is being loaded with a simple Torch loader rather than the lightning loader.
When I print the contents of ckpt_model using ckpt_model.keys() I get:
dict_keys(['epoch', 'global_step', 'pytorch-lightning_version', 'state_dict', 'loops', 'callbacks', 'optimizer_states', 'lr_schedulers']). The state_dict contains weights and biases for the encoder layer.
Then when I try loading the model with the PyTorch Lightning loader:
BarlowTwins.load_from_checkpoint('[checkpoint name].ckpt')
I get the error: TypeError: init() missing 4 required positional arguments: 'encoder', 'encoder_out_dim', 'num_training_samples', and 'batch_size'. I might be able to save those in the checkpoint with save_hyperparameters.
My question: how can I load the encoder portion of the model in the simple way possible? I only need the encoder portion of the model loaded.
Thanks so much for help in advance!
I have a problem making a prediction using a pre-trained model that contains an encoder and decoder for handwritten text recognition.
What I did is the following:
checkpoint = torch.load("Model/SPAN/SPAN-PT-RA_rimes.pt",map_location=torch.device('cpu'))
encoder_state_dict = checkpoint['encoder_state_dict']
decoder_state_dict = checkpoint['decoder_state_dict']
img = torch.LongTensor(img).unsqueeze(1).to(torch.device('cpu'))
global_pred = decoder_state_dict(encoder_state_dict(img))
This generates this error:
TypeError: 'collections.OrderedDict' object is not callable
I would highly appreciate your help! ^_^
encoder_state_dict and decoder_state_dict are not the torch Models, but a collection (dictionary) of tensors that include pre-trained parameters of the checkpoint you loaded.
Feeding inputs (such as the input image you got transformed) to such collection of tensors does not make sense. In fact, you should use these stat_dicts (i.e., a collection of pre-trained tensors) to load them into the parameters of your model object that is mapped to the network. See torch.nn.Module class.
I was following the tutorial for classifying structured data from here. I trained the model as described with my data which only comprised of numeric data.
I have trained and optimised my model on google colab and downloaded it locally to test it on some new data.
However, when I load the model using this snippet.
from keras.models import load_model
model = load_model("my_model.h5")
I get the following error
Unknown layer: DenseFeatures
...along with the trace.
I have tried setting up custom_objects while loading the model like this
model = load_model("my_model.h5", custom_objects={'DenseFeatures': tf.keras.layers.DenseFeatures})
But I still get the following error:
__init__() takes at least 2 arguments (3 given)
What could I be doing wrong? I tried going through documentation but couldn't find anything of help on github.
I am trying to load the weights from a Keras 1.0 Model into a Keras 2.0 model I created. I am sure the model architecture is exactly the same. The issues I am having is the load_weights() function is loading all the weights.
When I print the weights to a text file from the original model (loaded via load_model) and from the new model with load_weights() the later is missing many entry and are actually different. This also shows itself when making predictions as the accuracy is lower.
This problem only occurs in my LSTM layers. The embedding layers is fine and the Dense layer is also fine.
Any thoughts? I can not use load_model() as the original saved model was done in keras 1.0 and I need to use keras 2.0
EDIT MORE:
I should note I think the issue is the internal states not being loaded. Let me explain though. When I use get_weights() on each layer and I print it too terminal or a file the original model outputs a much larger matrix.
After using load_weights and then get_weights and print the weight matrix is missing many elements. I'm thinking it's the internal states.
The problem was that there was parameters for a compiled graph that were saved. I think it's safe to just port over the weights and continue training to let it catch up (maybe 1-2 epochs) if you can.
Gl