modulenorm = p.grad.data.norm() AttributeError: 'NoneType' object has no attribute 'data' about pytorch_sentiment_rnn HOT 2 OPEN

sudo conda install anaconda
sudo conda update numpy

python train.py --batch-size 20 --rnn_type GRU --cuda --gpu 1 --lr 0.0001 --mdl RNN --clip_norm 1 --opt Adam
/scratch/sjn-p2/anaconda/anaconda2/lib/python2.7/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
Using TensorFlow backend.
There are 2 CUDA devices
Setting torch GPU to 1
Using device:1 
Stored Environment:['term_len', 'word_index', 'glove', 'max_len', 'train', 'dev', 'test', 'index_word']
Loaded environment
Creating Model...
Setting Pretrained Embeddings
Initialized GRU model
Starting training
Namespace(aggregation='mean', attention_width=5, batch_size=20, clip_norm=1, cuda=True, dataset='Restaurants', dev=1, dropout_prob=0.5, embedding_size=300, epochs=50, eval=1, gpu=1, hidden_layer_size=300, l2_reg=0.0, learn_rate=0.0001, log=1, maxlen=0, mode='term', model_type='RNN', opt='Adam', pretrained=1, rnn_direction='uni', rnn_layers=1, rnn_size=300, rnn_type='GRU', seed=1111, term_model='mean', toy=False, trainable=1)
========================================================================
/scratch2/debate_tweets/sentiment/pytorch_sentiment_rnn/models/rnn.py:51: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
  decoded = self.softmax(decoded)
Traceback (most recent call last):
  File "train.py", line 343, in <module>
    exp.train()
  File "train.py", line 326, in train
    loss = self.train_batch(i)
  File "train.py", line 303, in train_batch
    coeff = clip_gradient(self.mdl, self.args.clip_norm)
  File "train.py", line 35, in clip_gradient
    modulenorm = p.grad.data.norm()
AttributeError: 'NoneType' object has no attribute 'data'

def clip_gradient(model, clip):
    """Computes a gradient clipping coefficient based on gradient norm."""
    totalnorm = 0
    for p in model.parameters():
        if p.grad is not None:
                modulenorm = p.grad.data.norm()
                totalnorm += modulenorm ** 2
    totalnorm = math.sqrt(totalnorm)
    return min(1, clip / (totalnorm + 1e-6))

def train_batch(self, i):
        ''' Trains a regular RNN model
        '''
        sentence, targets, actual_batch = self.make_batch(self.train_set, i)
        if(sentence is None):
            return None
        hidden = self.mdl.init_hidden(actual_batch)
        hidden = repackage_hidden(hidden)
        self.mdl.zero_grad()
        output, hidden = self.mdl(sentence, hidden)
        loss = self.criterion(output, targets)
        loss.backward()
        if(self.args.clip_norm>0):
            coeff = clip_gradient(self.mdl, self.args.clip_norm)
            for p in self.mdl.parameters():
                if p.grad is not None:
                        p.grad.mul_(coeff)
        self.optimizer.step()
        return loss.data[0]

python train.py --batch-size 20 --rnn_type GRU --cuda --gpu 1 --lr 0.0001 --mdl RNN --clip_norm 1 --opt Adam
[Epoch 50] Train Loss=0.953654762131 T=0.51s
Test loss=0.90144520998
Output Distribution={2: 1120}
Accuracy=0.65

python train.py --batch-size 20 --rnn_type GRU --cuda --gpu 1 --lr 0.0001 --mdl TD-RNN --clip_norm 1 --opt Adam
[Epoch 50] Train Loss=0.64427837713 T=0.99s
Test loss=0.828059911728
Output Distribution={0: 165, 1: 138, 2: 817}
Accuracy=0.719642857143

[Epoch 50] Train Loss=0.680990989366
Test loss=0.810974478722
Output Distribution={0: 158, 1: 158, 2: 804}
Accuracy=0.733035714286