def evaluateWordSort(model, epoch):
"""Evaluate after a train epoch"""
print('Epoch [{}] -- Evaluate'.format(epoch))
x_val, y_val = batch(4)
out, _ = model(x_val, y_val, teacher_force_ratio=0.)
out = out.permute(1, 0)
for i in range(out.size(0)):
print("=============================================")
print(
"yref",
y_val[i],
out[i],
y_val[i] - out[i],
)
xv = convertToWordSingle(x_val[i])
print("orig", xv)
v = out[i].numpy()
print("[", end="")
for index in v:
print(xv[index] + ", ", end="")
print("]")
def evaluateWordSort(model, epoch):
"""Evaluate after a train epoch"""
print('Epoch [{}] -- Evaluate'.format(epoch))
model.eval()
x_val, y_val = batch(1)
trg_seq_len, batch_size = y_val.shape
sentence_tensor = x_val
outputs = [[30]]
# outputs = list(y_val[0][0].numpy())
for i in range(trg_seq_len - 1):
trg_tensor = torch.LongTensor(outputs).to(device)
# trg_tensor = trg_tensor.permute(1, 0)
with torch.no_grad():
output = model(sentence_tensor, trg_tensor)
best_guess = output.argmax(2)[-1, :].item()
outputs.append([best_guess])
print(outputs, y_val)
"""
def train(pNet, optimizer, epoch, clip=1.):
print('Epoch [{}] -- Train'.format(epoch))
for step in range(STEPS_PER_EPOCH):
optimizer.zero_grad()
x, y = batch(BATCH_SIZE)
out, loss = pNet(x, y)
loss.backward()
torch.nn.utils.clip_grad_norm_(pNet.parameters(), clip)
optimizer.step()
if (step + 1) % 100 == 0:
print('Epoch [{}] loss: {}'.format(epoch, loss.item()))
def train(model, optimizer, epoch, clip=1.):
"""Train single epoch"""
model.train()
print('Epoch [{}] -- Train'.format(epoch))
criterion = nn.CrossEntropyLoss()
count = 0
for step in range(STEPS_PER_EPOCH):
# print(count)
optimizer.zero_grad()
# Forward
inp_data, target = batch(32)
# w = convertToWordsBatch(x)
#output = output.reshape(-1, output.shape[2])
#target = target[1:].reshape(-1)
# if(count == 3):
# target = target[:3]
trg = target[:-1, :]
# trg = target
output = model(inp_data, trg)
best_guess = output.argmax(2).transpose(0, 1)
# [-1, :].item()
output = output.reshape(-1, output.shape[2])
target = target[1:].reshape(-1)
# target = target.reshape(-1)
# if count == 3:
# print(count, "...............test")
# print(target, best_guess, best_guess-target)
# count += 1
# output = output.reshape(-1, output.shape[2])
# target = target[1:].reshape(-1)
# optimizer.zero_grad()
loss = criterion(output, target)
# Backward
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), clip)
optimizer.step()
if (step + 1) % 100 == 0:
count += 1
print('Epoch [{}] loss: {}'.format(epoch, loss.item()))
from dataGen.dataGenerator import batch, convertToWordsBatch, convertToWordSingle
#from data import batch
print("=========Generating words================")
X, Y = batch(5)
print(X)
print(Y)
print(X.shape)
print(X.numpy().shape)
Xa = convertToWordsBatch(X.numpy())
print("--------------------***************")
print(Xa)
for x in X:
xv = convertToWordSingle(x)
print(">>", xv)
from dataGen.dataGenerator import batch, convertToWordsBatch, convertToWordSingle
#from data import batch
print("=========Generating words 1================")
X, Y = batch(4)
# exit()
print(X)
print(Y)
print(X.shape)
# exit()
Xa = convertToWordsBatch(X)
print("--------------------***************")
print(Xa)
for x in X:
xv = convertToWordSingle(x)
print(">>", xv)