LABEL_DIM = 2
N_FILTERS = 128
BATCH_SIZE = 1
gamma = args.gamma
alpha = args.alpha
learning_rate = 0.001
# the number of training epoches
num_of_epoch = 8
# the number of batch size for gradient descent when training
batch_sz = 64
# set up the criterion
criterion = nn.CrossEntropyLoss().to(device)
# set up models
clstm = CNN_LSTM(INPUT_DIM, EMBEDDING_DIM, KER_SIZE, N_FILTERS,
HIDDEN_DIM).to(device)
print(clstm)
policy_s = Policy_S(HIDDEN_DIM, HIDDEN_DIM, OUTPUT_DIM).to(device)
policy_n = Policy_N(HIDDEN_DIM, HIDDEN_DIM, MAX_K).to(device)
policy_c = Policy_C(HIDDEN_DIM, HIDDEN_DIM, LABEL_DIM).to(device)
value_net = ValueNetwork(HIDDEN_DIM, HIDDEN_DIM, OUTPUT_DIM).to(device)
# set up optimiser
params_pg = list(policy_s.parameters()) + list(policy_c.parameters()) + list(
policy_n.parameters())
optim_loss = optim.Adam(clstm.parameters(), lr=learning_rate)
optim_policy = optim.Adam(params_pg, lr=learning_rate)
optim_value = optim.Adam(value_net.parameters(), lr=learning_rate)
# add pretrained embeddings
pretrained_embeddings = TEXT.vocab.vectors
# set up parameters
INPUT_DIM = len(TEXT.vocab)
EMBEDDING_DIM = 100
KER_SIZE = 5
HIDDEN_DIM = 128
LABEL_DIM = 2
N_FILTERS = 128
learning_rate = 0.001
# the number of training epoches
num_of_epoch = 10
# set up the criterion
criterion = nn.CrossEntropyLoss().to(device)
# set up models
clstm = CNN_LSTM(INPUT_DIM, EMBEDDING_DIM, KER_SIZE, N_FILTERS,
HIDDEN_DIM).to(device)
policy_c = Policy_C(HIDDEN_DIM, HIDDEN_DIM, LABEL_DIM).to(device)
# set up optimiser
params = list(clstm.parameters()) + list(policy_c.parameters())
optimizer = optim.Adam(params, lr=learning_rate)
# add pretrained embeddings
pretrained_embeddings = TEXT.vocab.vectors
clstm.embedding.weight.data.copy_(pretrained_embeddings)
clstm.embedding.weight.requires_grad = True # update the initial weights
def evaluate(iterator):
clstm.eval()
policy_c.eval()