def __init__(self):
#Build dataloaders, vocabulary, and numericalize texts
self.databunch = TextClasDataBunch.from_csv(args.data, bs = 10, csv_name='data.csv', pad_first=True, pad_idx = 1)
'''
Build word_to_idx and idx_to_word dictionaries
for the dataset's vocabulary
'''
def build_word_to_idx(idx_to_word):
word_to_idx = {}
for i in range(len(idx_to_word)):
word_to_idx[idx_to_word[i]] = i
return word_to_idx
idx_to_word = self.databunch.vocab.itos
word_to_idx = build_word_to_idx(idx_to_word)
models = {}
models['LSTM'] = LSTM(vocab_size = len(idx_to_word), embedding_dim = 300, hidden_size = 300, word_to_idx = word_to_idx, glove_path = args.embedding)
models['GloVe'] = Word_Vector_Model(vocab_size = len(idx_to_word), embedding_dim = 300, word_to_idx = word_to_idx, glove_path = args.embedding)
models['GRU'] = GRU(vocab_size=len(idx_to_word), embedding_dim = 300, hidden_size = 300, word_to_idx = word_to_idx, glove_path = args.embedding)
self.model = models[args.model]
#self.model = nn.DataParallel(self.model)
self.device = torch.device("cuda:0")
self.model.to(self.device)
self.train_dataloader = self.databunch.train_dl
self.valid_dataloader = self.databunch.valid_dl
self.epochs = 20
self.learning_rate = 0.0001
self.optimizer = optim.Adam(self.model.parameters(), lr=self.learning_rate)
self.loss_function = nn.CrossEntropyLoss()
def init_model(self):
'''
pooling, rnn, lstm, bilstm, cnn, multi_cnn, gru
:return:
'''
if self.opts.model == 'pooling':
self.model = Pooling(opts=self.opts,
vocab=self.vocab,
label_vocab=self.label_vocab)
elif self.opts.model == 'cnn':
self.model = CNN(opts=self.opts,
vocab=self.vocab,
label_vocab=self.label_vocab)
elif self.opts.model == 'multi_channel_cnn':
self.model = Multi_Channel_CNN(opts=self.opts,
vocab=self.vocab,
label_vocab=self.label_vocab)
elif self.opts.model == 'multi_layer_cnn':
self.model = Multi_Layer_CNN(opts=self.opts,
vocab=self.vocab,
label_vocab=self.label_vocab)
elif self.opts.model == 'char_cnn':
self.char = True
self.model = Char_CNN(opts=self.opts,
vocab=self.vocab,
char_vocab=self.char_vocab,
label_vocab=self.label_vocab)
elif self.opts.model == 'lstm':
self.model = LSTM(opts=self.opts,
vocab=self.vocab,
label_vocab=self.label_vocab)
elif self.opts.model == 'gru':
self.model = GRU(opts=self.opts,
vocab=self.vocab,
label_vocab=self.label_vocab)
elif self.opts.model == 'lstm_cnn':
self.model = LSTM_CNN(opts=self.opts,
vocab=self.vocab,
label_vocab=self.label_vocab)
elif self.opts.model == 'treelstm':
self.tree = True
self.model = BatchChildSumTreeLSTM(opts=self.opts,
vocab=self.vocab,
label_vocab=self.label_vocab)
elif self.opts.model == 'cnn_treelstm':
self.tree = True
self.model = CNN_TreeLSTM(opts=self.opts,
vocab=self.vocab,
label_vocab=self.label_vocab)
elif self.opts.model == 'lstm_treelstm':
self.tree = True
self.model = LSTM_TreeLSTM(opts=self.opts,
vocab=self.vocab,
label_vocab=self.label_vocab)
else:
raise RuntimeError('please choose your model first!')
if self.opts.use_cuda:
self.model = self.model.cuda()
def __init__(self, embedding_dim, output_dim, hidden_size, num_layers,
bidirectional, dropout, pretrained_embeddings):
super(TextRNN, self).__init__()
self.embedding = nn.Embedding.from_pretrained(pretrained_embeddings,
freeze=False)
self.rnn = LSTM(embedding_dim, hidden_size, num_layers, bidirectional,
dropout)
self.fc = Linear(hidden_size * 2, output_dim)
self.dropout = nn.Dropout(dropout)
def __init__(self, vocab_size, embedding_dim, output_dim, hidden_size,
num_layers, bidirectional, dropout, pad_idx):
super(TextRNN, self).__init__()
self.embedding = nn.Embedding(vocab_size,
embedding_dim,
padding_idx=pad_idx)
self.rnn = LSTM(embedding_dim, hidden_size, num_layers, bidirectional,
dropout)
self.fc = nn.Linear(hidden_size * 2, output_dim)
self.dropout = nn.Dropout(dropout)
def get_model(model):
"""
Get Model instance
"""
assert model in ['CHAR', 'WIDE', 'VDCNN', 'LSTM', 'MULTI_LSTM']
if model == 'CHAR': return Char_CNN(config, conv_layers, fc_layers)
elif model == 'WIDE': return Wide_CNN(config, wconv_layers)
elif model == 'VDCNN': return VDCNN2(config)
elif model == 'LSTM': return LSTM(config, fc_layers)
elif model == 'MULTI_LSTM':
return MULTI_LSTM(config, fc_layers, rnn_layers)
def initialize_model_and_trainer(model_properties, training_properties,
datasetloader, device):
logger.info("Model type is %s", training_properties["learner"])
if training_properties["learner"] == "text_cnn":
model = TextCnn(model_properties).to(device)
trainer = Trainer.trainer_factory("single_model_trainer",
training_properties, datasetloader,
device)
elif training_properties["learner"] == "gru":
model = GRU(model_properties).to(device)
trainer = Trainer.trainer_factory("single_model_trainer",
training_properties, datasetloader,
device)
elif training_properties["learner"] == "lstm":
model = LSTM(model_properties).to(device)
trainer = Trainer.trainer_factory("single_model_trainer",
training_properties, datasetloader,
device)
elif training_properties["learner"] == "char_cnn":
model = CharCNN(model_properties).to(device)
trainer = Trainer.trainer_factory("single_model_trainer",
training_properties, datasetloader,
device)
elif training_properties["learner"] == "vdcnn":
model = VDCNN(model_properties).to(device)
trainer = Trainer.trainer_factory("single_model_trainer",
training_properties, datasetloader,
device)
elif training_properties["learner"] == "conv_deconv_cnn":
model = ConvDeconvCNN(model_properties)
trainer = Trainer.trainer_factory("single_model_trainer",
training_properties, datasetloader,
device)
elif training_properties["learner"] == "transformer_google":
model = TransformerGoogle(model_properties).model.to(device)
trainer = Trainer.trainer_factory("single_model_trainer",
training_properties, datasetloader,
device)
elif training_properties["learner"] == "lstmcrf":
assert training_properties["task"] == "ner"
model = LSTMCRF(model_properties).to(device)
trainer = Trainer.trainer_factory("single_model_ner_trainer",
training_properties, datasetloader,
device)
else:
raise ValueError(
"Model is not defined! Available learner values are : 'text_cnn', 'char_cnn', 'vdcnn', 'gru', "
"'lstm', 'conv_deconv_cnn' and 'transformer_google'")
return model, trainer
def __init__(self, embedding_dim, output_dim, hidden_size, num_layers, bidirectional, dropout, pretrained_embeddings):
super(LSTMATT, self).__init__()
self.embedding = nn.Embedding.from_pretrained(
pretrained_embeddings, freeze=False)
self.rnn = LSTM(embedding_dim, hidden_size,
num_layers, bidirectional, dropout)
self.fc = nn.Linear(hidden_size * 2, output_dim)
self.dropout = nn.Dropout(dropout)
self.W_w = nn.Parameter(torch.Tensor(hidden_size * 2, hidden_size * 2))
self.u_w = nn.Parameter(torch.Tensor(hidden_size * 2, 1))
nn.init.uniform_(self.W_w, -0.1, 0.1)
nn.init.uniform_(self.u_w, -0.1, 0.1)
def __init__(self, word_dim, char_dim, output_dim, hidden_size, num_layers,
bidirectional, dropout, word_emb, char_emb, highway_layers):
super(TextRNNHighway, self).__init__()
self.char_embedding = nn.Embedding.from_pretrained(char_emb,
freeze=False)
self.word_embedding = nn.Embedding.from_pretrained(word_emb,
freeze=False)
self.text_embedding = Embedding(highway_layers, word_dim, char_dim)
self.rnn = LSTM(word_dim + char_dim, hidden_size, num_layers,
bidirectional, dropout)
self.fc = Linear(hidden_size * 2, output_dim)
self.dropout = nn.Dropout(dropout)
def initialize_model_and_trainer(model_properties, training_properties, datasetloader, device):
logger.info("Model type is %s", training_properties["learner"])
if training_properties["learner"] == "text_cnn":
model = TextCnn(model_properties).to(device)
trainer = Trainer.trainer_factory("single_model_trainer", training_properties, datasetloader.train_iter,
datasetloader.val_iter, datasetloader.test_iter, device)
elif training_properties["learner"] == "gru":
model = GRU(model_properties).to(device)
trainer = Trainer.trainer_factory("single_model_trainer", training_properties, datasetloader.train_iter,
datasetloader.val_iter, datasetloader.test_iter, device)
elif training_properties["learner"] == "lstm":
model = LSTM(model_properties).to(device)
trainer = Trainer.trainer_factory("single_model_trainer", training_properties, datasetloader.train_iter,
datasetloader.val_iter, datasetloader.test_iter, device)
elif training_properties["learner"] == "char_cnn":
model = CharCNN(model_properties).to(device)
trainer = Trainer.trainer_factory("single_model_trainer", training_properties, datasetloader.train_iter,
datasetloader.val_iter, datasetloader.test_iter, device)
elif training_properties["learner"] == "vdcnn":
model = VDCNN(model_properties).to(device)
trainer = Trainer.trainer_factory("single_model_trainer", training_properties, datasetloader.train_iter,
datasetloader.val_iter, datasetloader.test_iter, device)
elif training_properties["learner"] == "conv_deconv_cnn":
convDeconveCNN = ConvDeconvCNN(model_properties)
encoderCNN = convDeconveCNN.encoder.to(device)
decoderCNN = convDeconveCNN.decoder.to(device)
classifier = convDeconveCNN.classifier.to(device)
trainer = Trainer.trainer_factory("multiple_model_trainer", training_properties, datasetloader.train_iter,
datasetloader.val_iter, datasetloader.test_iter, device)
model = [encoderCNN, decoderCNN, classifier]
elif training_properties["learner"] == "transformer_google":
model = TransformerGoogle(model_properties).model.to(device)
trainer = Trainer.trainer_factory("single_model_trainer", training_properties, datasetloader.train_iter,
datasetloader.val_iter, datasetloader.test_iter, device)
elif training_properties["learner"] == "crf":
model = ConditionalRandomField().to(device)
else:
raise ValueError("Model is not defined! Available learner values are : 'text_cnn', 'char_cnn', 'vdcnn', 'gru', "
"'lstm', 'conv_deconv_cnn' and 'transformer_google'")
return model, trainer
def __init__(self, word_dim, char_dim, output_dim, hidden_size, num_layers,
bidirectional, dropout, word_emb, char_emb, highway_layers):
super(LSTMATTHighway, self).__init__()
self.char_embedding = nn.Embedding.from_pretrained(char_emb,
freeze=False)
self.word_embedding = nn.Embedding.from_pretrained(word_emb,
freeze=False)
self.text_embedding = Embedding(highway_layers, word_dim, char_dim)
self.rnn = LSTM(word_dim + char_dim, hidden_size, num_layers,
bidirectional, dropout)
self.fc = nn.Linear(hidden_size * 2, output_dim)
self.dropout = nn.Dropout(dropout)
self.W_w = nn.Parameter(torch.Tensor(hidden_size * 2, hidden_size * 2))
self.u_w = nn.Parameter(torch.Tensor(hidden_size * 2, 1))
nn.init.uniform_(self.W_w, -0.1, 0.1)
nn.init.uniform_(self.u_w, -0.1, 0.1)
sess = tf.Session(config=config)
tf.keras.backend.set_session(sess)
# # save np.load
# np_load_old = np.load
# # modify the default parameters of np.load
# np.load = lambda *a,**k: np_load_old(*a, allow_pickle=True, **k)
num_words = 20000
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=num_words)
epoch = 5
batch_size = 64
word_to_index = imdb.get_word_index()
word_to_index = {key:(value+3) for key,value in word_to_index.items()}
word_to_index["<PAD>"] = 0
word_to_index["<START>"] = 1
word_to_index["<UNK>"] = 2
index_to_word = {value:key for key,value in word_to_index.items()}
def print_sentence(id_list):
print(' '.join([index_to_word[id] for id in id_list if id != 0]))
print("Train-set size: ", len(x_train))
print("Test-set size: ", len(x_test))
x_train, x_test, max_tokens = utils.pad_sequences(train_sequences=x_train, test_sequences=x_test)
lstm = LSTM(num_words, max_tokens)
lstm.build()
lstm.train(epochs=epoch, x_train=x_train, y_train=y_train, batch_size=batch_size)
def main():
parser = argparse.ArgumentParser()
# parameters
parser.add_argument("--epoch",
default=100,
type=int,
help="the number of epoches needed to train")
parser.add_argument("--lr",
default=2e-5,
type=float,
help="the learning rate")
parser.add_argument("--classifier",
default='lstm',
type=str,
help="the classifier, such as LSTM, CNN ...")
parser.add_argument("--hidden_size",
default=64,
type=int,
help="the hidden size")
parser.add_argument("--output_size",
default=1,
type=int,
help="the output label size")
parser.add_argument("--early_stopping",
default=15,
type=int,
help="Tolerance for early stopping (# of epochs).")
parser.add_argument("--load_model",
default=None,
type=str,
help="load pretrained model for testing")
parser.add_argument('--n_models', nargs='+')
args = parser.parse_args()
path = 'D:/MyDocument/Project/CarSalesPrediction/'
T = 12
data = dataset.read_data(path)
df, feature_list, y_list = dataset.get_feature(data, T)
train_x, train_y, valid_x, valid_y, test_x, model2index = dataset.get_Xy(
df, T, feature_list, y_list)
X_train, X_val, X_test = dataset.normalization(train_x, valid_x, test_x)
input_size = X_train.shape[2]
print("feature_dim: ", input_size)
model = LSTM(args.output_size, [args.hidden_size], input_size)
X_train = torch.from_numpy(X_train.astype(np.float32))
y_train = torch.from_numpy(train_y.astype(np.float32))
X_val = torch.from_numpy(X_val.astype(np.float32))
y_val = torch.from_numpy(valid_y.astype(np.float32))
X_test = torch.from_numpy(X_test.astype(np.float32))
y_mean = []
train_y = []
valid_y = []
for step in range(4):
yy = y_train[:, step].view(-1, 1)
train_y.append(yy)
mean_y = yy.mean()
y_mean.append(mean_y)
valid_y.append(y_val[:, step])
# print (X_val)
# print(X_val)
if args.load_model:
evaluation_public = pd.read_csv(
path + 'test2_dataset/evaluation_public.csv')[[
'id', 'regMonth', 'forecastVolum'
]]
id_list = evaluation_public['id'].values
evaluation_result = DataFrame({'id': id_list})
# forecastVolum = []
for ii, model_name in enumerate(args.n_models):
print(model_name)
model.load_state_dict(
torch.load(args.load_model + '/' + model_name))
model.eval()
val_prediction = model(X_val)
score = get_score(
np.expm1(val_prediction.detach().numpy() + y_mean[ii].numpy()),
np.expm1(valid_y[ii].numpy()), model2index)
logger.info('Valid Score:%.4f', score)
test_prediction = model(X_test)
test_prediction = np.expm1(test_prediction.detach().numpy() +
y_mean[ii].numpy()).reshape(
-1).tolist()
evaluation_public.loc[(evaluation_public.regMonth == ii + 1),
'forecastVolum'] = test_prediction
# forecastVolum.extend(test_prediction)
del model
model = LSTM(args.output_size, [args.hidden_size], input_size)
# evaluation_result['forecastVolum'] = forecastVolum
evaluation_public[['id', 'forecastVolum']].round().astype(int).to_csv(
'evaluation_public.csv', index=False)
exit()
for i in range(4):
train_model(model, X_train, train_y[i], y_mean[i], X_val, valid_y[i],
args.epoch, model2index, args.lr, i)
args.embed_num = len(text_field.vocab)
args.class_num = len(
label_field.vocab) - 1 # 4, positive, negative, neutral, conflict
args.text_field = text_field
args.cuda = (not args.no_cuda) and torch.cuda.is_available()
del args.no_cuda
print("\nParameters:")
for attr, value in sorted(args.__dict__.items()):
print("\t{}={}".format(attr.upper(), value))
models = [
TD_LSTM(args),
TC_LSTM(args),
LSTM(args),
AE_LSTM(args),
ATAE_LSTM(args),
IAN(args),
RAM(args)
]
models_name = [
"TD_LSTM", "TC_LSTM", "LSTM", "AE_LSTM", "ATAE_LSTM", "IAN_LSTM", "RAM"
]
if args.cuda:
torch.cuda.set_device(args.device)
for i in range(len(models)):
models[i] = models[i].cuda()
# train and test for all models
log_file = open(save_path + model_name + "_test_logs.txt", 'a+')
# prepare test loader for the test set
test_file = args.data_path + args.test_file
test_data = ArticlesDataset(csv_file=test_file, vocab=vocab, label2id=label2id)
test_loader = DataLoader(test_data, batch_size=args.batch_size, shuffle=False)
scores_dict = {'f1': [], 'recall': [], 'precision': [], 'confidence': []}
for run_num in range(args.num_runs):
model_run_name = model_name + "_run" + str(run_num + 1)
print("-" * 10, "Run", run_num + 1, "-" * 10)
print("Model name:", model_run_name)
print("Loading model from", save_path + model_run_name + ".pt")
best_model = LSTM(lstm_args=lstm_args, mlp_args=mlp_args).to(device)
optimizer = torch.optim.Adam(best_model.parameters(), lr=0.005)
load_checkpoint(save_path + model_run_name + ".pt", best_model, optimizer,
device, log_file)
results = evaluate(best_model, test_loader)
scores_dict['f1'].append(results['f1'])
scores_dict['recall'].append(results['recall'])
scores_dict['precision'].append(results['precision'])
# if args.save_confidence is True:
# scores_dict['confidence'].append(results['confidence'])
# scores_dict['labels'].append(results['labels'])
# scores_dict['content'].append(results['content'])
# sentence_encodings = results['sentence_encodings']
hidden1 = 512
drop_p = 0.0
# Train with Skeleton+LSTM
if __name__ == '__main__':
# Load data
transform = None # TODO
train_set = CSL_Skeleton(data_path=data_path, label_path=label_path, frames=sample_duration,
num_classes=num_classes, selected_joints=selected_joints, train=True, transform=transform)
val_set = CSL_Skeleton(data_path=data_path, label_path=label_path, frames=sample_duration,
num_classes=num_classes, selected_joints=selected_joints, train=False, transform=transform)
logger.info("Dataset samples: {}".format(len(train_set)+len(val_set)))
train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True)
val_loader = DataLoader(val_set, batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True)
# Create model
model = LSTM(lstm_input_size=lstm_input_size, lstm_hidden_size=lstm_hidden_size, lstm_num_layers=lstm_num_layers,
num_classes=num_classes, hidden1=hidden1, drop_p=drop_p).to(device)
# Run the model parallelly
if torch.cuda.device_count() > 1:
logger.info("Using {} GPUs".format(torch.cuda.device_count()))
model = nn.DataParallel(model)
# Create loss criterion & optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
# Start training
logger.info("Training Started".center(60, '#'))
for epoch in range(epochs):
# Train the model
train_epoch(model, criterion, optimizer, train_loader, device, epoch, logger, log_interval, writer)
# Validate the model
def main(args):
best_er1 = 0
# Check if CUDA is enabled
args.cuda = not args.no_cuda and torch.cuda.is_available()
# Load data
root = args.datasetPath
files = []
train_ids = []
test_ids = []
if os.path.isfile('file_list.txt'):
print("File exists")
else:
print("File does not exist")
print("Prepare files")
files = [
f for f in os.listdir(root)
if os.path.isfile(os.path.join(root, f))
]
idx = np.random.permutation(len(files))
idx = idx.tolist()
files = [files[i] for i in idx[:]]
with open('file_list.txt', "w") as myfile:
for f in files:
myfile.write("%s\n" % f)
file2 = open("file_list.txt")
files = [line[:-1] for line in file2]
file2.close()
chunk = int(len(files) / 10)
train_ids = []
test_ids = []
print(len(files))
for i in range(10):
if i == int(args.fold):
test_ids = files[i * chunk:i * chunk + chunk]
print("test: " + str(i * chunk) + ":" + str(i * chunk + chunk))
continue
train_ids += files[i * chunk:i * chunk + chunk]
print("train: " + str(i * chunk) + ":" + str(i * chunk + chunk))
all_data = utils.Qm9(
root,
files,
edge_transform=datasets.qm9_edges,
e_representation="raw_distance",
)
data_train = utils.Qm9(
root,
train_ids,
edge_transform=datasets.qm9_edges,
e_representation="raw_distance",
)
data_test = utils.Qm9(
root,
test_ids,
edge_transform=datasets.qm9_edges,
e_representation="raw_distance",
)
# Define model and optimizer
print("Define model")
# Select one graph
g_tuple, l = data_train[1]
g, h_t, e = g_tuple
print("\tStatistics")
stat_dict = datasets.get_graph_stats(all_data,
["target_mean", "target_std"])
# Identify atoms in all files
data_train.set_target_transform(lambda x: datasets.normalize_data(
x, stat_dict["target_mean"], stat_dict["target_std"]))
data_test.set_target_transform(lambda x: datasets.normalize_data(
x, stat_dict["target_mean"], stat_dict["target_std"]))
# Data Loader
train_loader = torch.utils.data.DataLoader(
data_train,
batch_size=args.batch_size,
shuffle=True,
collate_fn=datasets.collate_g,
num_workers=args.prefetch,
pin_memory=True,
)
test_loader = torch.utils.data.DataLoader(
data_test,
batch_size=args.batch_size,
collate_fn=datasets.collate_g,
num_workers=args.prefetch,
pin_memory=True,
)
print("\tCreate model")
in_n = [len(h_t[0]), len(list(e.values())[0])]
hidden_state_size = 73
message_size = 73
n_layers = 3
l_target = len(l)
type = "regression"
if args.model == "MPNNv2":
model = MPNNv2(in_n, [5, 15, 15], [10, 20, 20], l_target, type=type)
elif args.model == "MPNNv3":
model = MPNNv3([1, 2, 3, 4],
in_n, [5, 15, 15],
30,
l_target,
type=type)
elif args.model == "LSTM":
model = LSTM(in_n,
hidden_state_size,
message_size,
n_layers,
l_target,
type=type)
else:
model = MPNN(in_n,
hidden_state_size,
message_size,
n_layers,
l_target,
type=type)
del in_n, hidden_state_size, message_size, n_layers, l_target, type
print("Optimizer")
optimizer = optim.Adam(model.parameters(), lr=args.lr)
criterion = nn.MSELoss()
evaluation = lambda output, target: torch.mean(
torch.abs(output - target) / torch.abs(target))
print("Logger")
logger = Logger(args.logPath)
lr_step = (args.lr - args.lr * args.lr_decay) / (
args.epochs * args.schedule[1] - args.epochs * args.schedule[0])
# get the best checkpoint if available without training
if args.resume:
checkpoint_dir = args.resume
best_model_file = os.path.join(checkpoint_dir, "model_best.pth")
if not os.path.isdir(checkpoint_dir):
os.makedirs(checkpoint_dir)
if os.path.isfile(best_model_file):
print("=> loading best model '{}'".format(best_model_file))
checkpoint = torch.load(best_model_file)
args.start_epoch = checkpoint["epoch"]
best_acc1 = checkpoint["best_er1"]
model.load_state_dict(checkpoint["state_dict"])
optimizer.load_state_dict(checkpoint["optimizer"])
print("=> loaded best model '{}' (epoch {})".format(
best_model_file, checkpoint["epoch"]))
else:
print("=> no best model found at '{}'".format(best_model_file))
print("Check cuda")
if args.cuda:
print("\t* Cuda")
model = model.cuda()
criterion = criterion.cuda()
# Epoch for loop
for epoch in range(0, args.epochs):
if (epoch > args.epochs * args.schedule[0]
and epoch < args.epochs * args.schedule[1]):
args.lr -= lr_step
for param_group in optimizer.param_groups:
param_group["lr"] = args.lr
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, evaluation,
logger)
# evaluate on test set
#er1 = validate(valid_loader, model, criterion, evaluation, logger)
#is_best = er1 > best_er1
#best_er1 = min(er1, best_er1)
is_best = True
best_er1 = 1
datasets.save_checkpoint(
{
"epoch": epoch + 1,
"state_dict": model.state_dict(),
"best_er1": best_er1,
"optimizer": optimizer.state_dict(),
},
is_best=is_best,
directory=args.resume,
)
# Logger step
logger.log_value("learning_rate", args.lr).step()
# get the best checkpoint and test it with test set
if args.resume:
checkpoint_dir = args.resume
best_model_file = os.path.join(checkpoint_dir, "model_best.pth")
if not os.path.isdir(checkpoint_dir):
os.makedirs(checkpoint_dir)
if os.path.isfile(best_model_file):
print("=> loading best model '{}'".format(best_model_file))
checkpoint = torch.load(best_model_file)
args.start_epoch = checkpoint["epoch"]
best_acc1 = checkpoint["best_er1"]
model.load_state_dict(checkpoint["state_dict"])
if args.cuda:
model.cuda()
optimizer.load_state_dict(checkpoint["optimizer"])
print("=> loaded best model '{}' (epoch {})".format(
best_model_file, checkpoint["epoch"]))
else:
print("=> no best model found at '{}'".format(best_model_file))
# For testing
validate(test_loader, model, criterion, evaluation)
#method = "train"
method = "read"
cnn = CNN()
cnn.data(train_data, train_labels, test_data, test_labels)
cnn.createModel()
cnn.findBestWeights(method)
cnn.testModel()
print()
#method = "train"
method = "read"
lstm = LSTM()
lstm.data(train_data, train_labels, test_data, test_labels)
lstm.createModel()
lstm.findBestWeights(method)
lstm.testModel()