if m.bias is not None:
nn.init.constant_(m.bias, 0)
if __name__ == '__main__':
# seed = 1
model_fn = lambda: FastText(1024, 1024)
model_name = 'fasttext_char'
train_data = np.load('../../data/char_train_input.npy')
train_label = np.load('../../data/label.npy')
test_data = np.load('../../data/char_test_input.npy')
# hold_out_test(model_fn, model_name, train_data, train_label, test_data, lr=1e-4)
cross_validation_bagging(model_fn,
model_name,
train_data,
train_label,
test_data,
lr=1e-4,
patience=20,
seed=2)
# seed = 1
# model_fn = lambda: Fast_Attention_Text(1024, 1024)
# model_name = 'fast_attention_text'
# train_data = np.load('../../data/train_input.npy')
# train_label = np.load('../../data/label.npy')
# test_data = np.load('../../data/test_input.npy')
# hold_out_test(model_fn, model_name, train_data, train_label, test_data)
# cross_validation_bagging(model_fn, model_name, train_data, train_label, test_data, batch_size=32, lr=5e-4,
# seed=seed)
elif isinstance(m, nn.Linear):
nn.init.xavier_normal_(m.weight)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
if __name__ == '__main__':
# seed = 1
# model_fn = lambda: FastText(1024, 1024)
# model_name = 'new_fasttext'
# train_data = np.load('../../data/train_input.npy')
# train_label = np.load('../../data/label.npy')
# test_data = np.load('../../data/test_input.npy')
# cross_validation_bagging(model_fn, model_name, train_data, train_label, test_data, seed=seed)
seed = 1
model_fn = lambda: Fast_Attention_Text(1024, 1024)
model_name = 'fast_attention_text'
train_data = np.load('../../data/train_input.npy')
train_label = np.load('../../data/label.npy')
test_data = np.load('../../data/test_input.npy')
# hold_out_test(model_fn, model_name, train_data, train_label, test_data)
cross_validation_bagging(model_fn,
model_name,
train_data,
train_label,
test_data,
batch_size=32,
lr=5e-4,
seed=seed)
return out
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out')
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.xavier_uniform_(m.weight)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LSTM):
for name, param in m.named_parameters():
if 'weight' in name:
nn.init.orthogonal_(param)
elif 'bias' in name:
nn.init.constant_(param, 0.0)
if __name__ == '__main__':
model_fn = lambda: Pooled_BiLSTM(40, 128)
model_name = 'pooled_bilstm'
train_data = np.load('../../data/train_input.npy')
train_label = np.load('../../data/label.npy')
test_data = np.load('../../data/test_input.npy')
cross_validation_bagging(model_fn, model_name, train_data, train_label,
test_data)