Pre-loaded glove char vectors have mismatched tensor shapes

[alphamupsiomega]

When trying to set the "pretrained_char" as True, the is a tensor reshape size conflict.

glove_char_file = os.path.join('data/glove', "glove.840B.300d-char.txt")
flags.DEFINE_string("glove_char_file", glove_char_file, "Glove character embedding source file")
flags.DEFINE_boolean("pretrained_char", True, "Whether to use pretrained character embedding")

Error is from model.py line 76, below. How can the reshape dimensions be adjusted?

Error:

Traceback (most recent call last):
  File "config.py", line 152, in <module>
    tf.app.run()
  File "/home/my/anaconda3/lib/python3.6/site-packages/tensorflow/python/platform/app.py", line 126, in run
    _sys.exit(main(argv))
  File "config.py", line 133, in main
    train(config)
  File "QANet/main.py", line 95, in train
    handle: train_handle, model.dropout: config.dropout})
  File "/home/my/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 905, in run
    run_metadata_ptr)
  File "/home/my/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1140, in _run
    feed_dict_tensor, options, run_metadata)
  File "/home/my/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1321, in _do_run
    run_metadata)
  File "/home/my/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1340, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Input to reshape is a tensor with 2445312 values, but the requested shape has 11462400
	 [[Node: Input_Embedding_Layer/Reshape = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/device:GPU:0"](Input_Embedding_Layer/embedding_lookup, Input_Embedding_Layer/Reshape/shape)]]
	 [[Node: Identity/_4743 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_52979_Identity", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]

Caused by op 'Input_Embedding_Layer/Reshape', defined at:
  File "config.py", line 152, in <module>
    tf.app.run()
  File "/home/my/anaconda3/lib/python3.6/site-packages/tensorflow/python/platform/app.py", line 126, in run
    _sys.exit(main(argv))
  File "config.py", line 133, in main
    train(config)
  File "QANet/main.py", line 72, in train
    model = Model(config, iterator, word_mat, char_mat, graph = g)
  File "QANet/model.py", line 60, in __init__
    self.forward()
  File "QANet/model.py", line 76, in forward
    ch_emb = tf.reshape(tf.nn.embedding_lookup(self.char_mat, self.ch), [N * PL, CL, dc]) # 32*1000?, 16, 64 = 32768000.  Input to reshape is a tensor with 34099200 values, but the requested shape has 7274496.
  File "/home/my/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/gen_array_ops.py", line 5782, in reshape
    "Reshape", tensor=tensor, shape=shape, name=name)
  File "/home/my/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
    op_def=op_def)
  File "/home/my/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 3290, in create_op
    op_def=op_def)
  File "/home/my/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1654, in __init__
    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access

InvalidArgumentError (see above for traceback): Input to reshape is a tensor with 2445312 values, but the requested shape has 11462400
	 [[Node: Input_Embedding_Layer/Reshape = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/device:GPU:0"](Input_Embedding_Layer/embedding_lookup, Input_Embedding_Layer/Reshape/shape)]]
	 [[Node: Identity/_4743 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_52979_Identity", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]

[alphamupsiomega]

The same issue also arises just when modifying char_dim.

[ghost]

If you want to use your own char embeddings you should change the dimension of the mebeddings here https://github.com/minsangkim142/QANet/blob/f0c79cc93dc1dfdad2bc8abb712a53d078814a56/config.py#L82
And you have to re-run python config.py --mode prepro

[alphamupsiomega]

Thanks!