def test_time_distributed_reshapes_correctly(self):
char_embedding = Embedding(2, 2)
char_embedding.weight = Parameter(torch.FloatTensor([[.4, .4], [.5, .5]]))
distributed_embedding = TimeDistributed(char_embedding)
char_input = torch.LongTensor([[[1, 0], [1, 1]]])
output = distributed_embedding(char_input)
assert_almost_equal(output.data.numpy(),
[[[[.5, .5], [.4, .4]], [[.5, .5,], [.5, .5]]]])
def test_time_distributed_reshapes_positional_kwarg_correctly(self):
char_embedding = Embedding(2, 2)
char_embedding.weight = Parameter(torch.FloatTensor([[.4, .4], [.5, .5]]))
distributed_embedding = TimeDistributed(char_embedding)
char_input = torch.LongTensor([[[1, 0], [1, 1]]])
output = distributed_embedding(input=char_input)
assert_almost_equal(output.data.numpy(),
[[[[.5, .5], [.4, .4]], [[.5, .5], [.5, .5]]]])
def __copyEmbeddings(embeddings: nn.Embedding, index1, index2):
#print(embeddings.weight.size())
weight = embeddings.weight.detach().numpy()
weight[index2] = deepcopy(weight[index1])
weight = torch.tensor(weight, requires_grad=True)
embeddings.weight = nn.Parameter(weight, requires_grad=True)
def run_cora():
np.random.seed(1)
random.seed(1)
num_nodes = 2708
feat_data, labels, adj_lists = load_cora()
features = Embedding(2708, 1433)
features.weight = Parameter(torch.tensor(feat_data, dtype=torch.float),
requires_grad=False)
agg1 = MeanAggregator(features)
enc1 = Encoder(features, 1433, 128, adj_lists, agg1, gcn=True)
agg2 = MeanAggregator(lambda nodes: enc1(nodes).t())
enc2 = Encoder(lambda nodes: enc1(nodes).t(),
enc1.embed_dim,
128,
adj_lists,
agg2,
base_model=enc1,
gcn=True)
enc1.num_samples = 5
enc2.num_samples = 5
graphsage = SupervisedGraphSage(7, enc2)
rand_indices = np.random.permutation(num_nodes)
test = rand_indices[:1000]
val = rand_indices[1000:1500]
train = list(rand_indices[1500:])
all_grad_params = filter(lambda p: p.requires_grad, graphsage.parameters())
optimizer = torch.optim.SGD(all_grad_params, lr=0.7)
times = []
for batch in range(100):
batch_nodes = train[:256]
random.shuffle(train)
start_time = time.time()
optimizer.zero_grad()
loss = graphsage.loss(batch_nodes,
torch.tensor(labels[np.array(batch_nodes)]))
loss.backward()
optimizer.step()
end_time = time.time()
times.append(end_time - start_time)
print(batch, loss.item())
val_output = graphsage.forward(val)
print(
"Validation F1:",
f1_score(labels[val],
val_output.data.numpy().argmax(axis=1),
average="micro"))
print("Average batch time:", np.mean(times))
def update_weights(emb: nn.Embedding, weights: Tensor) -> None:
emb.weight = nn.Parameter(weights)
max_length = 20
dataset = DyadDataset()
dyad_lut = dataset.get_dyad_lut()
embs_path = DATA_PATH + EMBS_FILE
cuda = True
with open(embs_path, 'rb') as f:
emb_params = pickle.load(f)
num_entities, dim = emb_params.size()
emb = Embedding(num_entities, dim)
status_emb = StatusEmb(dim, dyad_lut, cuda)
emb.weight = Parameter(emb_params)
encoder = DyadEncoder(emb, dim, cuda=cuda)
decoder = DyadDecoder(emb, dim, num_entities, cuda=cuda)
encoder_optim = torch.optim.Adam(encoder.parameters(), lr=10e-4)
decoder_optim = torch.optim.Adam(decoder.parameters(), lr=10e-4)
status_optim = torch.optim.Adam(status_emb.parameters(), lr=10e-4)
SOS_token = 0
criterion = torch.nn.NLLLoss()
i = 0
for item in dataset:
dyad, acts = item
if len(acts) > 1:
from torch.utils.data import Dataset, DataLoader
from torch.utils.data.sampler import SubsetRandomSampler
import random
from sklearn.metrics import r2_score
from torch.nn.utils.rnn import pack_padded_sequence
EMBS_FILE = 'embs.pickle'
BATCH_SIZE = 1000
CUDA = True
NUM_EPOCHS = 2
with open(EMBS_FILE, 'rb') as f:
emb_params = pickle.load(f)
num_entities, dim = emb_params.size()
emb = Embedding(num_entities, dim, padding_idx=0)
emb.weight = Parameter(emb_params, requires_grad=True)
data = []
lstm = torch.nn.LSTM(100, 100, bidirectional=True)
model = torch.nn.Sequential(
#torch.nn.Linear(10, 10),
#torch.nn.LeakyReLU(),
torch.nn.Dropout(),
torch.nn.Linear(100, 30),
torch.nn.LeakyReLU(),
#torch.nn.Linear(30, 10),
#torch.nn.LeakyReLU(),
torch.nn.Linear(30, 2),
# torch.nn.Softplus(),
)
