minqi / hnatt Goto Github PK

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
texts_input (InputLayer)     (None, 1, 20)             0         
_________________________________________________________________
time_distributed_1 (TimeDist (None, 1, 100)            99161300  
_________________________________________________________________
bidirectional_2 (Bidirection (None, 1, 100)            45300     
_________________________________________________________________
dense_transform_s (Dense)    (None, 1, 100)            10100     
_________________________________________________________________
sentence_attention (Attentio (None, 100)               100       
_________________________________________________________________
dense_1 (Dense)              (None, 25)                2525      
=================================================================
Total params: 99,219,325
Trainable params: 99,219,325
Non-trainable params: 0
_________________________________________________________________

# Predict Label
def predict(x):
    
    encoded_x = _encode_texts(x)
    print(encoded_x.shape)
    print(encoded_x)
    return model.predict(encoded_x)
    
raw_text = [['the food was really good']]
predict(raw_text)

(1, 1, 20)
[[[  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
     0.   1. 146.  31. 623. 138.]]]
---------------------------------------------------------------------------
InvalidArgumentError                      Traceback (most recent call last)
<ipython-input-100-f093b3ce957d> in <module>()
      8 
      9 raw_text = [['the food was really good']]
---> 10 predict(raw_text)
<ipython-input-100-f093b3ce957d> in predict(x)
      5     print(encoded_x.shape)
      6     print(encoded_x)
----> 7     return model.predict(encoded_x)
      8 
      9 raw_text = [['the food was really good']]

~/anaconda3/lib/python3.6/site-packages/keras/engine/training.py in predict(self, x, batch_size, verbose, steps)
   1170                                             batch_size=batch_size,
   1171                                             verbose=verbose,
-> 1172                                             steps=steps)
   1173 
   1174     def train_on_batch(self, x, y,

~/anaconda3/lib/python3.6/site-packages/keras/engine/training_arrays.py in predict_loop(model, f, ins, batch_size, verbose, steps)
    295                 ins_batch[i] = ins_batch[i].toarray()
    296 
--> 297             batch_outs = f(ins_batch)
    298             if not isinstance(batch_outs, list):
    299                 batch_outs = [batch_outs]

~/anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py in __call__(self, inputs)
   2659                 return self._legacy_call(inputs)
   2660 
-> 2661             return self._call(inputs)
   2662         else:
   2663             if py_any(is_tensor(x) for x in inputs):

~/anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py in _call(self, inputs)
   2629                                 symbol_vals,
   2630                                 session)
-> 2631         fetched = self._callable_fn(*array_vals)
   2632         return fetched[:len(self.outputs)]
   2633 

~/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py in __call__(self, *args)
   1452         else:
   1453           return tf_session.TF_DeprecatedSessionRunCallable(
-> 1454               self._session._session, self._handle, args, status, None)
   1455 
   1456     def __del__(self):

~/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py in __exit__(self, type_arg, value_arg, traceback_arg)
    517             None, None,
    518             compat.as_text(c_api.TF_Message(self.status.status)),
--> 519             c_api.TF_GetCode(self.status.status))
    520     # Delete the underlying status object from memory otherwise it stays alive
    521     # as there is a reference to status from this from the traceback due to

InvalidArgumentError: Inputs to operation bidirectional_2/while/Select_1 of type Select must have the same size and shape.  Input 0: [1,1000] != input 1: [1,50]
	 [[Node: bidirectional_2/while/Select_1 = Select[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"](bidirectional_2/while/Tile, bidirectional_2/while/add_6, bidirectional_2/while/Switch_3:1)]]