AttributeError: 'Optimization' object has no attribute 'train'. When trying to implement multivariate time series

Question

I am trying to implement a Multivariate Time-series using pytorch. Here i am giving only that part of the code where i am getting the error, i have included all the mentioned classes in my full code though. The code has been referred from https://towardsdatascience.com/building-rnn-lstm-and-gru-for-time-series-using-pytorch-a46e5b094e7b

class Optimization:
    def __init__(self, model, loss_fn, optimizer):
        self.model = model
        self.loss_fn = loss_fn
        self.optimizer = optimizer
        self.train_losses = []
        self.val_losses = []
    
    def train_step(self, x, y):
        # Sets model to train mode
        self.model.train()

        # Makes predictions
        yhat = self.model(x)

        # Computes loss
        loss = self.loss_fn(y, yhat)

        # Computes gradients
        loss.backward()

        # Updates parameters and zeroes gradients
        self.optimizer.step()
        self.optimizer.zero_grad()

        # Returns the loss
        return loss.item() 

import torch.optim as optim def get_model(model, model_params): models = { "rnn": RNNModel, "lstm": LSTMModel, "gru": GRUModel, } return models.get(model.lower())(**model_params)

input_dim = len(X_train.columns)
output_dim = 1
hidden_dim = 64
layer_dim = 3
batch_size = 64
dropout = 0.2
n_epochs = 100
learning_rate = 1e-3
weight_decay = 1e-6

model_params = {'input_dim': input_dim,
                'hidden_dim' : hidden_dim,
                'layer_dim' : layer_dim,
                'output_dim' : output_dim,
                'dropout_prob' : dropout}

model = get_model('lstm', model_params)

loss_fn = nn.MSELoss(reduction="mean")
optimizer = optim.Adam(model.parameters(), lr=learning_rate, weight_decay=weight_decay)

opt = Optimization(model=model, loss_fn=loss_fn, optimizer=optimizer)

opt.train(train_loader, val_loader, batch_size=batch_size,
          n_epochs=n_epochs, n_features=input_dim)
opt.plot_losses()

predictions, values = opt.evaluate(test_loader_one, batch_size=1, n_features=input_dim)

Is giving me this error.

AttributeError Traceback (most recent call last) e:\codefolder\multivar.ipynb Cell 21' in <cell line: 25>() 22 optimizer = optim.Adam(model.parameters(), lr=learning_rate, weight_decay=weight_decay) 24 opt = Optimization(model=model, loss_fn=loss_fn, optimizer=optimizer) ---> 25 opt.train(train_loader, val_loader, batch_size=batch_size, n_epochs=n_epochs, n_features=input_dim) 26 opt.plot_losses() 28 predictions, values = opt.evaluate(test_loader_one, batch_size=1, n_features=input_dim)

AttributeError: 'Optimization' object has no attribute 'train'

P.S.- I have included all the classes and codes into my code as given in the article to which i referred to. Because of space and length of the codes i have included only the main code where i am getting the error.

Answer 1

You've written

opt = Optimization(...)

Without either importing or defining that class. Python is telling you it doesn't recognize that object or its methods

I found torch.optim.Optimizer here, is it possible that's what you're looking for?