def train(model, optimizer, criterion=nn.BCELoss(), train_loader=train_iter, valid_loader=valid_iter, test_loader=test_iter, num_epochs=5, eval_every=(len(train_iter) // 2), file_path='training_process', best_valid_loss=float('Inf')):
running_loss = 0.0
valid_running_loss = 0.0
global_step = 0
train_loss_list = []
valid_loss_list = []
global_steps_list = []
model.train()
for epoch in flor.it(range(num_epochs)):
if flor.SkipBlock.step_into('batchwise-loop'):
for (((words, words_len), labels), _) in train_loader:
labels = labels.to(device)
words = words.to(device)
words_len = words_len.to(device)
output = model(words, words_len)
loss = criterion(output, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
running_loss += loss.item()
global_step += 1
if ((global_step % eval_every) == 0):
model.eval()
with torch.no_grad():
for (((words, words_len), labels), _) in valid_loader:
labels = labels.to(device)
words = words.to(device)
words_len = words_len.to(device)
output = model(words, words_len)
loss = criterion(output, labels)
valid_running_loss += loss.item()
average_train_loss = (running_loss / eval_every)
average_valid_loss = (valid_running_loss / len(valid_loader))
train_loss_list.append(average_train_loss)
valid_loss_list.append(average_valid_loss)
global_steps_list.append(global_step)
running_loss = 0.0
valid_running_loss = 0.0
model.train()
print('Epoch [{}/{}], Step [{}/{}], Train Loss: {:.4f}, Valid Loss: {:.4f}'.format((epoch + 1), num_epochs, global_step, (num_epochs * len(train_loader)), average_train_loss, average_valid_loss))
flor.log('avg_train_loss,avg_val_loss', (average_train_loss, average_valid_loss))
flor.SkipBlock.end(model)
y_pred = []
model.eval()
with torch.no_grad():
for ((words, words_len), _) in test_loader:
words = words.to(device)
words_len = words_len.to(device)
output = model(words, words_len)
output = (output > 0.5).int()
y_pred.extend(output.tolist())
print('Finished Training!')
return y_pred
def __init__(self, dimension=128):
super(LSTM, self).__init__()
self.embedding = nn.Embedding(len(text_field.vocab), dimension)
flor.log("embedding", self.embedding)
self.lstm = nn.LSTM(
input_size=dimension,
hidden_size=dimension,
num_layers=1,
batch_first=True,
bidirectional=True,
)
self.drop = nn.Dropout(p=0.85)
self.dimension = dimension
self.fc = nn.Linear(2 * dimension, 1)
self.relu = nn.ReLU()
def train(
model,
optimizer,
criterion=nn.BCELoss(),
train_loader=train_iter,
valid_loader=valid_iter,
test_loader=test_iter,
num_epochs=5,
eval_every=len(train_iter) // 2,
file_path="training_process",
best_valid_loss=float("Inf"),
):
running_loss = 0.0
valid_running_loss = 0.0
global_step = 0
train_loss_list = []
valid_loss_list = []
global_steps_list = []
# training loop
best_accuracy = float("inf")
model.train()
for epoch in flor.it(range(num_epochs)):
if flor.SkipBlock.step_into("batchwise-loop"):
for ((words, words_len), labels), _ in train_loader:
labels = labels.to(device)
words = words.to(device)
words_len = words_len.detach().cpu()
output = model(words, words_len)
loss = criterion(output, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# update running values
running_loss += loss.item()
global_step += 1
# evaluation step
if global_step % eval_every == 0:
model.eval()
with torch.no_grad():
# validation loop
for ((words, words_len), labels), _ in valid_loader:
labels = labels.to(device)
words = words.to(device)
words_len = words_len.detach().cpu()
output = model(words, words_len)
loss = criterion(output, labels)
valid_running_loss += float(loss.item())
# evaluation
average_train_loss = running_loss / eval_every
average_valid_loss = valid_running_loss / len(valid_loader)
if average_valid_loss < best_accuracy:
best_accuracy = average_valid_loss
torch.save(model.state_dict(), "best-model.pt")
train_loss_list.append(average_train_loss)
valid_loss_list.append(average_valid_loss)
global_steps_list.append(global_step)
# resetting running values
running_loss = 0.0
valid_running_loss = 0.0
model.train()
# print progress
print(
"Epoch [{}/{}], Step [{}/{}], Train Loss: {:.4f}, Valid Loss: {:.4f}"
.format(
epoch + 1,
num_epochs,
global_step,
num_epochs * len(train_loader),
average_train_loss,
average_valid_loss,
))
flor.log("avg_train_loss,avg_val_loss",
(average_train_loss,
average_valid_loss)) # type: ignore
flor.SkipBlock.end(model, optimizer)
flor.log("eval", None) # type:ignore
# model.load_state_dict(torch.load("best-model.pt"))
# predict test
y_pred = []
model.eval()
with torch.no_grad():
for ((words, words_len)), _ in test_loader:
# labels = labels.to(device)
words = words.to(device)
words_len = words_len.detach().cpu()
output = model(words, words_len)
output = (output > 0.5).int()
y_pred.extend(output.tolist())
print("Finished Training!")
return y_pred
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
import torch.optim as optim
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
import seaborn as sns
import flor
from multiprocessing import set_start_method
try:
set_start_method("spawn")
except RuntimeError:
pass
flor.flags.NAME = "kaggle-nlp-disasters-rnn"
flor.flags.REPLAY = False
device = flor.log(
"device", torch.device("cuda:0" if torch.cuda.is_available() else "cpu"))
device
label_field = Field(sequential=False,
use_vocab=False,
batch_first=True,
dtype=torch.float)
text_field = Field(tokenize="spacy",
lower=True,
include_lengths=True,
batch_first=True)
fields = [("words", text_field), ("target", label_field)]
fields_test = [("words", text_field)]
train, valid = TabularDataset.splits(
path="data",
set_start_method("spawn")
except RuntimeError:
pass
flor.flags.NAME = "kaggle-nlp-disasters-rnn"
flor.flags.REPLAY = False
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
device
label_field = Field(sequential=False,
use_vocab=False,
batch_first=True,
dtype=torch.float)
text_field = Field(
tokenize=flor.log("tokenizer", "spacy"),
lower=True,
include_lengths=True,
batch_first=True,
)
fields = [("words", text_field), ("target", label_field)]
fields_test = [("words", text_field)]
train, valid = TabularDataset.splits(
path="data",
train="train_rnn.csv",
validation="valid_rnn.csv",
format="CSV",
fields=fields,
skip_header=True,
)
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import torch
from torchtext.legacy.data import Field, TabularDataset, BucketIterator
import torch.nn as nn
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
import torch.optim as optim
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
import seaborn as sns
import flor
flor.flags.NAME = "kaggle-nlp-disasters-rnn"
flor.flags.REPLAY = False
device = flor.log(
"device", torch.device("cuda:0" if torch.cuda.is_available() else "cpu"))
device
label_field = Field(sequential=False,
use_vocab=False,
batch_first=True,
dtype=torch.float)
text_field = Field(tokenize="spacy",
lower=True,
include_lengths=True,
batch_first=True)
fields = [("words", text_field), ("target", label_field)]
fields_test = [("words", text_field)]
train, valid = TabularDataset.splits(
path="data",
for ((words, words_len)), _ in test_loader:
words = words.to(device)
words_len = words_len.detach().cpu()
output = model(words, words_len)
output = (output > 0.5).int()
y_pred.extend(output.tolist())
print("Finished Training!")
return y_pred
EPOCHS = 80
MIN_LR = 1e-4
model = LSTM(8).to(device)
optimizer = optim.SGD(model.parameters(), lr=MIN_LR)
flor.log("optimizer", str(type(optimizer)))
clr_scheduler = CLR_Scheduler(
optimizer,
net_steps=(len(train_iter) * EPOCHS),
min_lr=MIN_LR,
max_lr=4.0,
tail_frac=0.0,
)
pred = train(model=model, optimizer=optimizer, num_epochs=EPOCHS)
# save result as .csv file
# test_data = pd.read_csv("data/test.csv")
# preds_df = pd.DataFrame({"id": test_data["id"], "target": pred})
# preds_df.to_csv(f"data/output_lstm_3.csv", index=False)
