print('\nNumber of xTestData pairs: ', len(xTestData))
print('\nNumber of yTrainData pairs: ', len(yTrainData))
print('\nNumber of yTestData pairs: ', len(yTestData))
#%% Model build and fit
model = CNN()
history = model.fit(xTrainData, yTrainData,
batch_size = 16,
epochs = 20,
validation_split= 0.25,
verbose = 2,
)
# model_test
testEvaluate = model.evaluate(xTestData, yTestData, verbose=0)
print("loss: " + str(testEvaluate[0]) + "\t accuracy: " + str(testEvaluate[1]))
#%% Save weights
model.save("my_h5_model.h5")
model.save_weights("covid19_weights.h5")
#%% Load weights
model.load_weights("my_h5_model.h5")
#%% Plotting
print(history.history.keys())
#Accuracy and Loss
accuracy = history.history['accuracy']
def main():
"""
Main Function
"""
args = Config(yaml_file='./config.yaml')
args.build()
args.Print()
if not (args.do_train or args.do_val or args.do_infer):
raise ValueError("For args `do_train`, `do_val` and `do_infer`, at "
"least one of them must be True.")
place = set_device("gpu" if args.use_cuda else "cpu")
fluid.enable_dygraph(place)
processor = EmoTectProcessor(data_dir=args.data_dir,
vocab_path=args.vocab_path,
random_seed=args.random_seed)
num_labels = args.num_labels
if args.model_type == 'cnn_net':
model = CNN(args.vocab_size, args.max_seq_len)
elif args.model_type == 'bow_net':
model = BOW(args.vocab_size, args.max_seq_len)
elif args.model_type == 'lstm_net':
model = LSTM(args.vocab_size, args.max_seq_len)
elif args.model_type == 'gru_net':
model = GRU(args.vocab_size, args.max_seq_len)
elif args.model_type == 'bigru_net':
model = BiGRU(args.vocab_size, args.batch_size, args.max_seq_len)
else:
raise ValueError("Unknown model type!")
inputs = [Input([None, args.max_seq_len], 'int64', name='doc')]
optimizer = None
labels = None
if args.do_train:
train_data_generator = processor.data_generator(
batch_size=args.batch_size,
places=place,
phase='train',
epoch=args.epoch,
padding_size=args.max_seq_len)
num_train_examples = processor.get_num_examples(phase="train")
max_train_steps = args.epoch * num_train_examples // args.batch_size + 1
print("Num train examples: %d" % num_train_examples)
print("Max train steps: %d" % max_train_steps)
labels = [Input([None, 1], 'int64', name='label')]
optimizer = fluid.optimizer.Adagrad(learning_rate=args.lr,
parameter_list=model.parameters())
test_data_generator = None
if args.do_val:
test_data_generator = processor.data_generator(
batch_size=args.batch_size,
phase='dev',
epoch=1,
places=place,
padding_size=args.max_seq_len)
elif args.do_val:
test_data_generator = processor.data_generator(
batch_size=args.batch_size,
phase='test',
epoch=1,
places=place,
padding_size=args.max_seq_len)
elif args.do_infer:
infer_data_generator = processor.data_generator(
batch_size=args.batch_size,
phase='infer',
epoch=1,
places=place,
padding_size=args.max_seq_len)
model.prepare(optimizer,
CrossEntropy(),
Accuracy(topk=(1, )),
inputs,
labels,
device=place)
if args.do_train:
if args.init_checkpoint:
model.load(args.init_checkpoint)
elif args.do_val or args.do_infer:
if not args.init_checkpoint:
raise ValueError("args 'init_checkpoint' should be set if"
"only doing validation or infer!")
model.load(args.init_checkpoint, reset_optimizer=True)
if args.do_train:
model.fit(train_data=train_data_generator,
eval_data=test_data_generator,
batch_size=args.batch_size,
epochs=args.epoch,
save_dir=args.checkpoints,
eval_freq=args.eval_freq,
save_freq=args.save_freq)
elif args.do_val:
eval_result = model.evaluate(eval_data=test_data_generator,
batch_size=args.batch_size)
print("Final eval result: acc: {:.4f}, loss: {:.4f}".format(
eval_result['acc'], eval_result['loss'][0]))
elif args.do_infer:
preds = model.predict(test_data=infer_data_generator)
preds = np.array(preds[0]).reshape((-1, args.num_labels))
if args.output_dir:
with open(os.path.join(args.output_dir, 'predictions.json'),
'w') as w:
for p in range(len(preds)):
label = np.argmax(preds[p])
result = json.dumps({
'index': p,
'label': label,
'probs': preds[p].tolist()
})
w.write(result + '\n')
print('Predictions saved at ' +
os.path.join(args.output_dir, 'predictions.json'))
