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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"]()]]
The text was updated successfully, but these errors were encountered:
When trying to set the "pretrained_char" as True, the is a tensor reshape size conflict.
Error is from model.py line 76, below. How can the reshape dimensions be adjusted?
Error:
The text was updated successfully, but these errors were encountered: