optimizer = torch.optim.Adam(model.parameters(), lr=flags.learning_rate)
epoch = 1
iterator = 0
for i in range(flags.epochs * flags.max_batch):
if iterator >= epoch * flags.max_batch:
epoch += 1
state_h, state_c = model.zero_state(flags.batch_size)
state_h = state_h.to(device)
state_c = state_c.to(device)
iterator += 1
inputs, targets = dataset()
model.train()
optimizer.zero_grad()
x = torch.tensor(inputs).to(device)
y = torch.tensor(targets).to(device)
logits, (state_h, state_c) = model(x, (state_h, state_c))
loss = criterion(logits.transpose(1, 2), y)
loss_value = loss.item()
loss.backward()
state_h = state_h.detach()
state_c = state_c.detach()
'state_dict': deepsets.state_dict(),
'optimizer': optimizer.state_dict(),
}, 'model_deepsets.pth.tar')
print("Finished training for DeepSets model")
print()
# Initializes LSTM model and optimizer
lstm = LSTM(n_digits, embedding_dim, hidden_dim).to(device)
optimizer = optim.Adam(lstm.parameters(), lr=learning_rate)
loss_function = nn.L1Loss()
# Trains the LSTM model
for epoch in range(epochs):
t = time.time()
lstm.train()
train_loss = 0
count = 0
idx = np.random.permutation(n_train)
for i in range(0, n_train, batch_size):
############## Task 5
##################
# your code here #
##################
x_batch = X_train[idx[i:min(n_train, i + batch_size)], :]
y_batch = y_train[idx[i:min(n_train, i + batch_size)]]
x_batch = torch.LongTensor(x_batch).to(device)
