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What versions of python,theano,keras and tensorflow did you use? I am facing issues with tensorflow.

Originally posted by @nahos in #13 (comment)

I have tried to reproduce the result, by got QWK much less than that in the paper. Here is my log for prompt 1, fold_0:

log.txt

Did i do something wrong?

[INFO] (nea.w2vEmbReader) Loading embeddings from: data/embeddings.w2v.txt
Traceback (most recent call last):
File "train_nea.py", line 160, in
model = create_model(args, train_y.mean(axis=0), overal_maxlen, vocab)
File "/home/zetao/nea-master/nea/models.py", line 133, in create_model
emb_reader = EmbReader(args.emb_path, emb_dim=args.emb_dim)
File "/home/zetao/nea-master/nea/w2vEmbReader.py", line 47, in init
assert self.emb_dim == emb_dim, 'The embeddings dimension does not match with the requested dimension'
AssertionError: The embeddings dimension does not match with the requested dimension

Hi,

When I try to run the code using the default parameters;

python train_nea.py -tr data/fold_0/train.tsv -tu data/fold_0/dev.tsv -ts data/fold_0/test.tsv -p 1 -o output_dir

I get the following error:

nea/nea/my_layers.py", line 59, in call
    return K.cast(x.sum(axis=1) / mask.sum(axis=1, keepdims=True), K.floatx())
AttributeError: 'Tensor' object has no attribute 'sum'

Any thoughts ?

Thanks !

hello ~ I got a problem as the following

�[94m[INFO]�[0m (nea.models) Building a REGRESSION model with POOLING
Traceback (most recent call last):
  File "train_nea.py", line 160, in <module>
    model = create_model(args, train_y.mean(axis=0), overal_maxlen, vocab)
  File "/data1/wbxu/AES/nea-master/nea/models.py", line 63, in create_model
    model.add(RNN(args.rnn_dim, return_sequences=True, dropout_W=dropout_W, dropout_U=dropout_U))
  File "/home/wbxu/anaconda3/envs/nea-master/lib/python2.7/site-packages/keras/models.py", line 308, in add
    output_tensor = layer(self.outputs[0])
  File "/home/wbxu/anaconda3/envs/nea-master/lib/python2.7/site-packages/keras/engine/topology.py", line 487, in __call__
    self.build(input_shapes[0])
  File "/home/wbxu/anaconda3/envs/nea-master/lib/python2.7/site-packages/keras/layers/recurrent.py", line 710, in build
    self.W = K.concatenate([self.W_i, self.W_f, self.W_c, self.W_o])
  File "/home/wbxu/anaconda3/envs/nea-master/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py", line 716, in concatenate
    return tf.concat(axis, [to_dense(x) for x in tensors])
  File "/home/wbxu/anaconda3/envs/nea-master/lib/python2.7/site-packages/tensorflow/python/ops/array_ops.py", line 1043, in concat
    dtype=dtypes.int32).get_shape(
  File "/home/wbxu/anaconda3/envs/nea-master/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 676, in convert_to_tensor
    as_ref=False)
  File "/home/wbxu/anaconda3/envs/nea-master/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 741, in internal_convert_to_tensor
    ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
  File "/home/wbxu/anaconda3/envs/nea-master/lib/python2.7/site-packages/tensorflow/python/framework/constant_op.py", line 113, in _constant_tensor_conversion_function
    return constant(v, dtype=dtype, name=name)
  File "/home/wbxu/anaconda3/envs/nea-master/lib/python2.7/site-packages/tensorflow/python/framework/constant_op.py", line 102, in constant
    tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape, verify_shape=verify_shape))
  File "/home/wbxu/anaconda3/envs/nea-master/lib/python2.7/site-packages/tensorflow/python/framework/tensor_util.py", line 374, in make_tensor_proto
    _AssertCompatible(values, dtype)
  File "/home/wbxu/anaconda3/envs/nea-master/lib/python2.7/site-packages/tensorflow/python/framework/tensor_util.py", line 302, in _AssertCompatible
    (dtype.name, repr(mismatch), type(mismatch).__name__))
TypeError: Expected int32, got <tf.Variable 'lstm_1_W_i:0' shape=(50, 300) dtype=float32_ref> of type 'Variable' instead.

my env is

keras 1.1.0
tensorflow 1.6.0
teano 0.8.2
nltk 3.0.0

File "/home/deepamparmar/Music/nea-master/nea/models.py", line 132, in create_model
emb_reader = EmbReader(args.emb_path, emb_dim=args.emb_dim)
File "/home/deepamparmar/Music/nea-master/nea/w2vEmbReader.py", line 46, in init
assert self.emb_dim == emb_dim, 'The embeddings dimension does not match with the requested dimension'
AssertionError: The embeddings dimension does not match with the requested dimension

when runing train_nea.py -tr /data/fold_1/train.tsv -tu /data/fold_1/dev.tsv -ts /data/fold_1/test.tsv -p 0 -o /output_dir I get an error:

Traceback (most recent call last):
  File "/train_nea.py", line 63, in <module>
    raise NotImplementedError
NotImplementedError

when I run it for an individual prompt it works fine. Any Ideas how to solve this ?

When I run "python train_nea.py -tr data/fold_0/train.tsv -tu data/fold_0/dev.tsv -ts data/fold_0/test.tsv -p 1 -o output"

I got an error like this:

/Users/yanshengjia/Desktop/aes/nea/nea/models.py:63: UserWarning: Update your LSTM call to the Keras 2 API: LSTM(300, return_sequences=True, dropout=0.5, recurrent_dropout=0.1)
model.add(RNN(args.rnn_dim, return_sequences=True, dropout_W=dropout_W, dropout_U=dropout_U))
/Users/yanshengjia/anaconda/lib/python2.7/site-packages/keras/engine/topology.py:621: UserWarning: Class nea.my_layers.MeanOverTime defines get_output_shape_for but does not override compute_output_shape. If this is a Keras 1 layer, please implement compute_output_shape to support Keras 2.
output_shape = self.compute_output_shape(input_shape)
Traceback (most recent call last):
File "train_nea.py", line 160, in
model = create_model(args, train_y.mean(axis=0), overal_maxlen, vocab)
File "/Users/yanshengjia/Desktop/aes/nea/nea/models.py", line 73, in create_model
model.layers[-1].b.set_value(bias_value)
AttributeError: 'Dense' object has no attribute 'b'

What versions of keras and theano did you use?

I often find the most difficult part of getting a code like this to run is specifying all the unique versions of everything. I wonder if we could do some sort of container in the future (like docker)...

Really looking forward to trying it all out.

Loading a trained model requires the custom objects which were defined in the my_layers.py. Any tips for loading them with the model archs?

example code:
from keras.models import model_from_json

with open('model_arch.json', 'r') as f: model = model_from_json(file.read(f), custom_objects={'MeanOverTime':MeanOverTime}

Some documentation on the issue in others:
loading custom objects in keras

When I tried to run the codebase it returns such error.

I guess it's probably because of the updated Keras 2 API, but could you try to build it again?

Thanks a lot ;)

When I run
python preprocess_asap.py -i training_set_rel3.tsv

There are the errors:
Traceback (most recent call last):
File "preprocess_asap.py", line 44, in
dataset = collect_dataset(args.input_file)
File "preprocess_asap.py", line 34, in collect_dataset
for line in f:
File "/home/wyn/anaconda3/envs/py3NLP/lib/python3.5/codecs.py", line 321, in decode
(result, consumed) = self._buffer_decode(data, self.errors, final)
UnicodeDecodeError: 'utf-8' codec can't decode byte 0x92 in position 1539: invalid start byte

I don't know why do I get it?

For help!