def load_latest_checkpoint(self, model, optimizer=None):
if not self.continue_training:
raise RuntimeError(
f"Trying to load the latest checkpoint from logdir {self.logdir}, "
"but did not set `continue_training=true` in configuration.")
model, optimizer, iteration = load_latest_checkpoint(
self.logdir, model, optimizer)
return model, optimizer, iteration
def train():
model_type = FLAGS.model_type
run_desc = FLAGS.run_desc
run_desc_tl = FLAGS.run_desc_tl
data_dir = Path(FLAGS.data_dir)
checkpoints_dir = Path(FLAGS.checkpoints_dir) / model_type / run_desc
models_dir = Path(FLAGS.models_dir) / model_type / run_desc
results_dir = Path(FLAGS.results_dir) / model_type / run_desc
checkpoints_dir_tl = Path(FLAGS.checkpoints_dir) / model_type / run_desc_tl
models_dir_tl = Path(FLAGS.models_dir) / model_type / run_desc_tl
results_dir_tl = Path(FLAGS.results_dir) / model_type / run_desc_tl
learning_rate = FLAGS.learning_rate
batch_size_fn = FLAGS.batch_size
epoch_no = FLAGS.epoch
sent_hidden_dim = FLAGS.sent_hidden_dim
doc_hidden_dim = FLAGS.doc_hidden_dim
if not data_dir.exists():
raise ValueError('Data directory does not exist')
# create other directories if they do not exist
create_directories(checkpoints_dir_tl, models_dir_tl, results_dir_tl)
# load the data
print('Loading the data...')
# get the glove and elmo embedding
glove_dim = 0
elmo_dim = 0
GloVe_vectors = None
ELMo = None
if 'glove' in model_type:
GloVe_vectors = GloVe()
glove_dim = WORD_EMBED_DIM
print('Uploaded GloVe embeddings.')
if 'elmo' in model_type:
ELMo = Elmo(options_file=ELMO_OPTIONS_FILE,
weight_file=ELMO_WEIGHT_FILE,
num_output_representations=1,
requires_grad=False,
dropout=0).to(DEVICE)
elmo_dim = ELMO_EMBED_DIM
print('Uploaded Elmo embeddings.')
input_dim = glove_dim + elmo_dim
# get the fnn and snli data
keys = ['train', 'test', 'val']
FNN_DL_small = {}
for i in keys:
FNN_temp = FNNDataset(data_dir / ('FNN_small_' + i + '.pkl'),
GloVe_vectors, ELMo)
FNN_DL_temp = data.DataLoader(dataset=FNN_temp,
batch_size=batch_size_fn,
num_workers=0,
shuffle=True,
drop_last=True,
collate_fn=PadSortBatchFNN())
FNN_DL_small[i] = FNN_DL_temp
print('Uploaded FNN data.')
# initialize the model, according to the model type
print('Initializing the model for transfer learning...', end=' ')
model = HierarchicalAttentionNet(input_dim=input_dim,
sent_hidden_dim=sent_hidden_dim,
doc_hidden_dim=doc_hidden_dim,
num_classes=NUM_CLASSES_FN,
dropout=0).to(DEVICE)
print('Done!')
print_model_parameters(model)
print()
print('Working on: ', end='')
print(DEVICE)
# set the criterion and optimizer
# we weigh the loss: class [0] is real, class [1] is fake
#
loss_func_fn = nn.CrossEntropyLoss()
optimizer = optim.Adam(params=model.parameters(), lr=learning_rate)
# load the last checkpoint (if it exists)
results = {
'epoch': [],
'train_loss': [],
'train_accuracy': [],
'val_loss': [],
'val_accuracy': []
}
if epoch_no == '0':
model_path = models_dir / Path('HierarchicalAttentionNet_model.pt')
_, _, _ = load_latest_checkpoint(model_path, model, optimizer)
else:
checkpoint_path = checkpoints_dir / Path(
'HierarchicalAttentionNet_Adam_checkpoint_' + str(epoch_no) +
'_.pt')
_, _, _ = load_checkpoint(checkpoint_path, model, optimizer)
print(f'Starting transfer learning on the model extracted from {epoch_no}')
epoch = 0
for i in range(epoch, MAX_EPOCHS):
print(f'Epoch {i+1:0{len(str(MAX_EPOCHS))}}/{MAX_EPOCHS}:')
model.train()
# one epoch of training
train_loss_fn, train_acc_fn = train_epoch_fn(FNN_DL_small['train'],
model, optimizer,
loss_func_fn)
# one epoch of eval
model.eval()
val_loss_fn, val_acc_fn = eval_epoch_fn(FNN_DL_small['val'], model,
loss_func_fn)
results['epoch'].append(i)
results['train_loss'].append(train_loss_fn)
results['train_accuracy'].append(train_acc_fn)
results['val_loss'].append(val_loss_fn)
results['val_accuracy'].append(val_acc_fn)
#print(results)
best_accuracy = torch.tensor(val_acc_fn).max().item()
create_checkpoint(checkpoints_dir_tl, i, model, optimizer, results,
best_accuracy)
# save and plot the results
save_results(results_dir_tl, results, model)
save_model(models_dir_tl, model)
def train():
model_type = FLAGS.model_type
run_desc = FLAGS.run_desc
data_dir = Path(FLAGS.data_dir)
checkpoints_dir = Path(FLAGS.checkpoints_dir) / model_type / run_desc
models_dir = Path(FLAGS.models_dir) / model_type / run_desc
results_dir = Path(FLAGS.results_dir) / model_type / run_desc
learning_rate = LEARNING_RATE
sent_hidden_dim = FLAGS.sent_hidden_dim
doc_hidden_dim = FLAGS.doc_hidden_dim
if not data_dir.exists():
raise ValueError('Data directory does not exist')
# create other directories if they do not exist
create_directories(checkpoints_dir, models_dir, results_dir)
# load the data
print('Loading the data...')
# get the glove and elmo embedding
glove_dim = 0
elmo_dim = 0
GloVe_vectors = None
ELMo = None
if 'glove' in model_type:
GloVe_vectors = GloVe()
glove_dim = WORD_EMBED_DIM
print('Uploaded GloVe embeddings.')
if 'elmo' in model_type:
ELMo = Elmo(options_file=ELMO_OPTIONS_FILE,
weight_file=ELMO_WEIGHT_FILE,
num_output_representations=1,
requires_grad=False,
dropout=0).to(DEVICE)
elmo_dim = ELMO_EMBED_DIM
print('Uploaded Elmo embeddings.')
input_dim = glove_dim + elmo_dim
# get the fnn and snli data
FNN = {}
FNN_DL = {}
for path in ['train', 'val', 'test']:
FNN[path] = FNNDataset(data_dir / ('FNN_' + path + '.pkl'),
GloVe_vectors, ELMo)
FNN_DL[path] = data.DataLoader(dataset=FNN[path],
batch_size=BATCH_SIZE_FN,
num_workers=0,
shuffle=True,
drop_last=True,
collate_fn=PadSortBatchFNN())
print('Uploaded FNN data.')
fnn_train_sent_no = get_number_sentences(data_dir / 'FNN_train.pkl')
fnn_train_len = len(FNN['train'])
# initialize the model, according to the model type
print('Initializing the model...', end=' ')
model = HierarchicalAttentionNet(input_dim=input_dim,
sent_hidden_dim=sent_hidden_dim,
doc_hidden_dim=doc_hidden_dim,
num_classes=NUM_CLASSES_FN,
dropout=0).to(DEVICE)
print('Working on: ', end='')
print(DEVICE)
print('Done!')
print_model_parameters(model)
print()
# set the criterion and optimizer
# we weigh the loss: class [0] is real, class [1] is fake
#
real_ratio, fake_ratio = get_class_balance(data_dir / 'FNN_train.pkl')
weights = [(1.0 - real_ratio), (1.0 - fake_ratio)]
print(weights)
class_weights = torch.FloatTensor(weights).to(DEVICE)
loss_func_fn = nn.CrossEntropyLoss(weight=class_weights)
optimizer = optim.Adam(params=model.parameters(), lr=LEARNING_RATE)
# load the last checkpoint (if it exists)
results = {
'epoch': [],
'train_loss': [],
'train_accuracy': [],
'val_loss': [],
'val_accuracy': []
}
epoch, results, best_accuracy = load_latest_checkpoint(
checkpoints_dir, model, optimizer)
if epoch == 0:
print(f'Starting training at epoch {epoch + 1}...')
else:
print(f'Resuming training from epoch {epoch + 1}...')
for i in range(epoch, MAX_EPOCHS):
print(f'Epoch {i+1:0{len(str(MAX_EPOCHS))}}/{MAX_EPOCHS}:')
model.train()
# one epoch of training
train_loss_fn, train_acc_fn = train_epoch_fn(FNN_DL['train'], model,
optimizer, loss_func_fn)
# one epoch of eval
model.eval()
val_loss_fn, val_acc_fn = eval_epoch_fn(FNN_DL['val'], model,
loss_func_fn)
results['epoch'].append(i)
results['train_loss'].append(train_loss_fn)
results['train_accuracy'].append(train_acc_fn)
results['val_loss'].append(val_loss_fn)
results['val_accuracy'].append(val_acc_fn)
#print(results)
best_accuracy = torch.tensor(val_acc_fn).max().item()
create_checkpoint(checkpoints_dir, i, model, optimizer, results,
best_accuracy)
if (i + 1) % 4 == 0 and i != 0:
learning_rate = learning_rate / 2
optimizer = optim.Adam(params=model.parameters(), lr=learning_rate)
# save and plot the results
save_results(results_dir, results, model)
save_model(models_dir, model)
def train():
model_type = FLAGS.model_type
run_desc = FLAGS.run_desc
data_dir = Path(FLAGS.data_dir)
checkpoints_dir = Path(FLAGS.checkpoints_dir) / model_type / run_desc
models_dir = Path(FLAGS.models_dir) / model_type / run_desc
results_dir = Path(FLAGS.results_dir) / model_type / run_desc
#data_percentage = FLAGS.data_percentage
if model_type == 'STL':
only_fn = True
else:
only_fn = False
GloVe_vectors = GloVe()
ELMo = Elmo(options_file=ELMO_OPTIONS_FILE,
weight_file=ELMO_WEIGHT_FILE,
num_output_representations=1,
requires_grad=False,
dropout=0).to(DEVICE)
FNN_test = FNNDataset(DATA_DIR_DEFAULT / ('FNN_test.pkl'), GloVe_vectors,
ELMo)
FNN_DL_test = data.DataLoader(dataset=FNN_test,
batch_size=BATCH_SIZE_FN,
num_workers=0,
shuffle=True,
drop_last=True,
collate_fn=PadSortBatch())
input_dim = 300 + 1024
NUM_CLASSES_NLI = None
model = HierarchicalAttentionNet(input_dim=input_dim,
hidden_dim=WORD_HIDDEN_DIM,
num_classes_task_fn=NUM_CLASSES_FN,
embedding=None,
num_classes_task_nli=NUM_CLASSES_NLI,
dropout=0).to(DEVICE)
optimizer = optim.Adam(params=model.parameters(), lr=LEARNING_RATE)
#model.load_state_dict(torch.load(CHECKPOINTS_DIR_DEFAULT / 'HierarchicalAttentionNet_model.pt'))
_, _, _ = load_latest_checkpoint(CHECKPOINTS_DIR_DEFAULT, model, optimizer)
model.eval()
loss_func_fn = nn.CrossEntropyLoss()
y_pred = []
y_true = []
for step, batch in enumerate(FNN_DL_test):
articles, article_dims, labels = batch
out = model(batch=articles, batch_dims=article_dims, task='FN')
y_pred.append(out.argmax(dim=1).to(DEVICE).item())
y_true.append(labels.to(DEVICE).item())
if step % 100 == 0 and step != 0:
print(
sklearn.metrics.precision_recall_fscore_support(y_true,
y_pred,
average=None))
print(
sklearn.metrics.precision_recall_fscore_support(y_true,
y_pred,
average='micro'))
print(
sklearn.metrics.precision_recall_fscore_support(y_true,
y_pred,
average='macro'))
def train():
model_type = FLAGS.model_type
run_desc = FLAGS.run_desc
data_dir = Path(FLAGS.data_dir)
checkpoints_dir = Path(FLAGS.checkpoints_dir) / model_type / run_desc
models_dir = Path(FLAGS.models_dir) / model_type / run_desc
results_dir = Path(FLAGS.results_dir) / model_type / run_desc
#data_percentage = FLAGS.data_percentage
if model_type == 'STL':
only_fn = True
else:
only_fn = False
# check if data directory exists
if not data_dir.exists():
raise ValueError('Data directory does not exist')
# create other directories if they do not exist
create_directories(checkpoints_dir, models_dir, results_dir)
# load the data
print('Loading the data...')
# get the glove and elmo embeddings
GloVe_vectors = GloVe()
print('Uploaded GloVe embeddings.')
# ELMo = Elmo(
# options_file=ELMO_OPTIONS_FILE,
# weight_file=ELMO_WEIGHT_FILE,
# num_output_representations=1,
# requires_grad=False,
# dropout=0).to(DEVICE)
# print('Uploaded Elmo embeddings.')
# get the fnn and snli data
FNN = {}
FNN_DL = {}
for path in ['train', 'val', 'test']:
FNN[path] = FNNDataset(data_dir / ('FNN_' + path + '.pkl'),
GloVe_vectors)
FNN_DL[path] = data.DataLoader(dataset=FNN[path],
batch_size=BATCH_SIZE_FN,
num_workers=0,
shuffle=True,
drop_last=True,
collate_fn=PadSortBatch())
print('Uploaded FNN data.')
if not only_fn:
SNLI = {}
SNLI_DL = {}
for path in ['train', 'val', 'test']:
SNLI[path] = SNLIDataset(data_dir / ('SNLI_' + path + '.pkl'),
GloVe_vectors)
SNLI_DL[path] = data.DataLoader(dataset=SNLI[path],
batch_size=BATCH_SIZE_NLI,
num_workers=0,
shuffle=True,
drop_last=True,
collate_fn=PadSortBatchSNLI())
print('Uploaded SNLI data.')
snli_train_sent_no = len(SNLI['train']) * 2
snli_train_len = len(SNLI['train'])
fnn_train_sent_no = get_number_sentences(data_dir / 'FNN_train.pkl')
fnn_train_len = len(FNN['train'])
# initialize the model, according to the model type
print('Initializing the model...', end=' ')
if model_type == 'MTL':
NUM_CLASSES_NLI = 3
print("Loading an MTL HAN model.")
elif model_type == 'STL':
NUM_CLASSES_NLI = None
print("Loading an STL HAN model.")
elif model_type == 'Transfer':
print("Nothing for now.")
if ELMO_EMBED_DIM is not None:
# input_dim = WORD_EMBED_DIM + ELMO_EMBED_DIM
input_dim = WORD_EMBED_DIM
else:
input_dim = WORD_EMBED_DIM
model = HierarchicalAttentionNet(input_dim=input_dim,
hidden_dim=WORD_HIDDEN_DIM,
num_classes_task_fn=NUM_CLASSES_FN,
embedding=None,
num_classes_task_nli=NUM_CLASSES_NLI,
dropout=0).to(DEVICE)
print('Working on: ', end='')
print(DEVICE)
print('Done!')
print_model_parameters(model)
print()
# set the criterion and optimizer
# we weigh the loss: class [0] is real, class [1] is fake
#
real_ratio, fake_ratio = get_class_balance(data_dir / 'FNN_train.pkl')
weights = [(1.0 - real_ratio), (1.0 - fake_ratio)]
print(weights)
class_weights = torch.FloatTensor(weights).to(DEVICE)
loss_func_fn = nn.CrossEntropyLoss(weight=class_weights)
if not only_fn:
loss_func_nli = nn.CrossEntropyLoss()
temperature = 2
optimizer = optim.Adam(params=model.parameters(), lr=LEARNING_RATE)
# load the last checkpoint (if it exists)
epoch, results, best_accuracy = load_latest_checkpoint(
checkpoints_dir, model, optimizer)
results_fn = {
'epoch': [],
'train_loss': [],
'train_accuracy': [],
'val_loss': [],
'val_accuracy': []
}
results_nli = {
'epoch': [],
'train_loss': [],
'train_accuracy': [],
'val_loss': [],
'val_accuracy': []
}
results = {'fn': results_fn, 'nli': results_nli}
if epoch == 0:
print(f'Starting training at epoch {epoch + 1}...')
else:
print(f'Resuming training from epoch {epoch + 1}...')
for i in range(epoch, MAX_EPOCHS):
print(f'Epoch {i+1:0{len(str(MAX_EPOCHS))}}/{MAX_EPOCHS}:')
model.train()
# one epoch of training
if only_fn:
train_loss_fn, train_acc_fn = train_epoch_fn(
FNN_DL['train'], model, optimizer, loss_func_fn)
elif model_type == 'MTL':
model.train()
train_loss_fn = []
train_acc_fn = []
loss_fn_weight_gradnorm = 1
train_loss_nli = []
train_acc_nli = []
loss_nli_weight_gradnorm = 1
#define by sentence number
#loss_fn_weight_dataset = 1 - fnn_train_sent_no / (fnn_train_sent_no + snli_train_sent_no)
#loss_nli_weight_dataset = 1 - snli_train_sent_no / (fnn_train_sent_no + snli_train_sent_no)
loss_fn_weight_dataset = 1 - fnn_train_len / (fnn_train_len +
snli_train_len)
loss_nli_weight_dataset = 1 - snli_train_len / (fnn_train_len +
snli_train_len)
chance_fn = 1000 * (fnn_train_len / BATCH_SIZE_FN) / (
(fnn_train_len / BATCH_SIZE_FN) +
(snli_train_len / BATCH_SIZE_NLI))
iterator_fnn = enumerate(FNN_DL['train'])
iterator_snli = enumerate(SNLI_DL['train'])
done_fnn, done_snli = False, False
step_fnn = 0
step_snli = 0
print(
f'Train set length, FNN: {fnn_train_len}. Train set length, SNLI: {snli_train_len}.'
)
print(
f'Training set to batch size ratio for Fake News Detection is {fnn_train_len / BATCH_SIZE_FN}.'
)
print(
f'Training set to batch size ratio for Language Inference is {snli_train_len / BATCH_SIZE_NLI}.'
)
while not (done_fnn and done_snli):
if len(train_loss_fn) > 1 and len(train_loss_nli) > 1:
# computes loss weights based on the loss from the previous iterations
loss_fn_ratio = train_loss_fn[len(train_loss_fn) -
1] / train_loss_fn[
len(train_loss_fn) - 2]
loss_nli_ratio = train_loss_nli[
len(train_acc_nli) -
1] / train_loss_nli[len(train_loss_nli) - 2]
loss_fn_exp = math.exp(loss_fn_ratio / temperature)
loss_nli_exp = math.exp(loss_nli_ratio / temperature)
loss_fn_weight_gradnorm = loss_fn_exp / (loss_fn_exp +
loss_nli_exp)
loss_nli_weight_gradnorm = loss_nli_exp / (loss_fn_exp +
loss_nli_exp)
loss_fn_weight = math.exp(
loss_fn_weight_dataset * loss_fn_weight_gradnorm) / (
math.exp(loss_fn_weight_dataset *
loss_fn_weight_gradnorm) +
math.exp(loss_nli_weight_dataset *
loss_nli_weight_gradnorm))
loss_nli_weight = math.exp(
loss_nli_weight_dataset * loss_nli_weight_gradnorm) / (
math.exp(loss_fn_weight_dataset *
loss_fn_weight_gradnorm) +
math.exp(loss_nli_weight_dataset *
loss_nli_weight_gradnorm))
else:
loss_fn_weight = loss_fn_weight_dataset
loss_nli_weight = loss_nli_weight_dataset
# define the total loss function
#loss_func = loss_func_fn + loss_func_nli
# is this needed?
if np.random.randint(0, 1000) < chance_fn:
try:
step_fnn, batch_fnn = next(iterator_fnn)
except StopIteration:
done_fnn = True
else:
try:
batch_loss_fn, batch_acc_fn = train_batch_fn(
batch_fnn, model, optimizer, loss_func_fn,
loss_fn_weight)
train_loss_fn.append(batch_loss_fn)
train_acc_fn.append(batch_acc_fn)
except:
print('Error in batch')
else:
try:
step_snli, batch_snli = next(iterator_snli)
except StopIteration:
done_snli = True
else:
try:
batch_loss_nli, batch_acc_nli = train_batch_nli(
batch_snli, model, optimizer, loss_func_nli,
loss_nli_weight)
train_loss_nli.append(batch_loss_nli)
train_acc_nli.append(batch_acc_nli)
except:
print('Error in batch')
print(f'FNN batch {step_fnn}')
print(f'SNLI batch {step_snli}')
if step_fnn % 50 == 0 and step_fnn != 0:
print(f'Processed {step_fnn} FNN batches.')
print(f'Accuracy: {train_acc_fn[len(train_acc_fn)-1]}.')
print(
f'Weight for loss for NLI is {loss_nli_weight}, for loss for FN is {loss_fn_weight}.'
)
if step_snli % 50 == 0 and step_snli != 0:
print(f'Processed {step_snli} SNLIbatches.')
print(f'Accuracy: {train_acc_nli[len(train_acc_nli)-1]}.')
print(
f'Weight for loss for NLI is {loss_nli_weight}, for loss for FN is {loss_fn_weight}.'
)
# one epoch of eval
model.eval()
val_loss_fn, val_acc_fn = eval_epoch_fn(FNN_DL['val'], model,
loss_func_fn)
tasks = ['fn']
if model_type == 'MTL':
val_loss_nli, val_acc_nli = eval_epoch_nli(SNLI_DL['val'], model,
loss_func_nli)
tasks.append('nli')
for task in tasks:
results[task]['epoch'].append(i)
if task == 'fn':
temp_train_loss = train_loss_fn
temp_val_loss = val_loss_fn
temp_train_acc = train_acc_fn
temp_val_acc = val_acc_fn
elif task == 'nli':
temp_train_loss = train_loss_nli
temp_val_loss = val_loss_nli
temp_train_acc = train_acc_nli
temp_val_acc = val_acc_nli
results[task]['train_loss'].append(temp_train_loss)
results[task]['train_accuracy'].append(temp_train_acc)
results[task]['val_loss'].append(temp_val_loss)
results[task]['val_accuracy'].append(temp_val_acc)
print(results)
best_accuracy = torch.tensor(temp_val_acc).max().item()
create_checkpoint(checkpoints_dir, epoch, model, optimizer, results,
best_accuracy)
# save and plot the results
save_results(results_dir, results, model)
save_model(models_dir, model)
plot_results(results_dir, results, model)
def test():
model_type = FLAGS.model_type
run_desc = FLAGS.run_desc
data_dir = Path(FLAGS.data_dir)
checkpoints_dir = Path(FLAGS.checkpoints_dir) / model_type / run_desc
models_dir = Path(FLAGS.models_dir) / model_type / run_desc
results_dir = Path(FLAGS.results_dir) / model_type / run_desc
learning_rate = LEARNING_RATE
epoch_no = FLAGS.epoch
sent_hidden_dim = FLAGS.sent_hidden_dim
doc_hidden_dim = FLAGS.doc_hidden_dim
if not data_dir.exists():
raise ValueError('Data directory does not exist')
# create other directories if they do not exist
create_directories(checkpoints_dir, models_dir, results_dir)
# load the data
print('Loading the data...')
# get the glove and elmo embedding
glove_dim = 0
elmo_dim = 0
GloVe_vectors = None
ELMo = None
if 'glove' in model_type:
GloVe_vectors = GloVe()
glove_dim = WORD_EMBED_DIM
print('Uploaded GloVe embeddings.')
if 'elmo' in model_type:
ELMo = Elmo(options_file=ELMO_OPTIONS_FILE,
weight_file=ELMO_WEIGHT_FILE,
num_output_representations=1,
requires_grad=False,
dropout=0).to(DEVICE)
elmo_dim = ELMO_EMBED_DIM
print('Uploaded Elmo embeddings.')
input_dim = glove_dim + elmo_dim
# get the fnn and snli data
FNN_test = FNNDataset(data_dir / ('FNN_test.pkl'), GloVe_vectors, ELMo)
FNN_DL_test = data.DataLoader(dataset=FNN_test,
batch_size=BATCH_SIZE_FN,
num_workers=0,
shuffle=True,
drop_last=True,
collate_fn=PadSortBatchFNN())
print('Uploaded FNN data.')
print('Initializing the model...', end=' ')
model = HierarchicalAttentionNet(input_dim=input_dim,
sent_hidden_dim=sent_hidden_dim,
doc_hidden_dim=doc_hidden_dim,
num_classes=NUM_CLASSES_FN,
dropout=0).to(DEVICE)
print('Working on: ', end='')
print(DEVICE)
print('Done!')
print_model_parameters(model)
print()
optimizer = optim.Adam(params=model.parameters(), lr=LEARNING_RATE)
#model.load_state_dict(torch.load(CHECKPOINTS_DIR_DEFAULT / 'HierarchicalAttentionNet_model.pt'))
if epoch_no == '0':
model_path = models_dir / Path('HierarchicalAttentionNet_model.pt')
_, _, _ = load_latest_checkpoint(model_path, model, optimizer)
else:
checkpoint_path = checkpoints_dir / Path(
'HierarchicalAttentionNet_Adam_checkpoint_' + str(epoch_no) +
'_.pt')
_, _, _ = load_checkpoint(checkpoint_path, model, optimizer)
model.eval()
loss_func_fn = nn.CrossEntropyLoss()
y_pred = []
y_true = []
for step, batch in enumerate(FNN_DL_test):
articles, article_dims, labels = batch
out = model(batch=articles, batch_dims=article_dims)
y_pred.append(out.argmax(dim=1).to(DEVICE).item())
y_true.append(labels.to(DEVICE).item())
#if step % 100 == 0 and step != 0:
#print(sklearn.metrics.precision_recall_fscore_support(y_true, y_pred, average=None))
print(
sklearn.metrics.precision_recall_fscore_support(y_true,
y_pred,
average='micro'))
print(
sklearn.metrics.precision_recall_fscore_support(y_true,
y_pred,
average='macro'))
print(
sklearn.metrics.precision_recall_fscore_support(y_true,
y_pred,
average=None))
