in run_epoch(self, new_model, verbose)
176 break
177 tree = self.train_data[step]
--> 178 logits = self.inference(tree)
179 labels = [l for l in tree.labels if l!=2]
180 loss = self.loss(logits, labels)
E:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\util\dispatch.py in wrapper(*args, *kwargs)
178 """Call target, and fall back on dispatchers if there is a TypeError."""
179 try:
--> 180 return target(args, **kwargs)
181 except (TypeError, ValueError):
182 # Note: convert_to_eager_tensor currently raises a ValueError, not a
I got this error when I run the code:
ValueError Traceback (most recent call last) in 271 272 if name == "main": --> 273 test_RNN()
in test_RNN() 259 model = RNN_Model(config) 260 start_time = time.time() --> 261 stats = model.train(verbose=True) 262 print('Training time: {}'.format(time.time() - start_time)) 263
in train(self, verbose) 216 print('epoch %d'%epoch) 217 if epoch==0: --> 218 train_acc, val_acc, loss_history, val_loss = self.run_epoch(new_model=True) 219 else: 220 train_acc, val_acc, loss_history, val_loss = self.run_epoch()
in run_epoch(self, new_model, verbose) 176 break 177 tree = self.train_data[step] --> 178 logits = self.inference(tree) 179 labels = [l for l in tree.labels if l!=2] 180 loss = self.loss(logits, labels)
in inference(self, tree, predict_only_root) 38 39 def inference(self, tree, predict_only_root=False): ---> 40 node_tensors = self.add_model(tree.root) 41 if predict_only_root: 42 node_tensors = node_tensors[tree.root][0]
in add_model(self, node) 81 curr_node_tensor = [curr_node_vec, curr_node_mat] 82 else: ---> 83 node_tensors.update(self.add_model(node.left)) 84 node_tensors.update(self.add_model(node.right)) 85 tmp = tf.concat(0,
in add_model(self, node) 82 else: 83 node_tensors.update(self.add_model(node.left)) ---> 84 node_tensors.update(self.add_model(node.right)) 85 tmp = tf.concat(0, 86 [tf.matmul(
in add_model(self, node) 81 curr_node_tensor = [curr_node_vec, curr_node_mat] 82 else: ---> 83 node_tensors.update(self.add_model(node.left)) 84 node_tensors.update(self.add_model(node.right)) 85 tmp = tf.concat(0,
in add_model(self, node) 82 else: 83 node_tensors.update(self.add_model(node.left)) ---> 84 node_tensors.update(self.add_model(node.right)) 85 tmp = tf.concat(0, 86 [tf.matmul(
in add_model(self, node) 81 curr_node_tensor = [curr_node_vec, curr_node_mat] 82 else: ---> 83 node_tensors.update(self.add_model(node.left)) 84 node_tensors.update(self.add_model(node.right)) 85 tmp = tf.concat(0,
in add_model(self, node) 90 tf.matmul( 91 node_tensors[node.left][1], ---> 92 node_tensors[node.right][0] 93 ) 94 ])
E:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\util\dispatch.py in wrapper(*args, *kwargs) 178 """Call target, and fall back on dispatchers if there is a TypeError.""" 179 try: --> 180 return target(args, **kwargs) 181 except (TypeError, ValueError): 182 # Note: convert_to_eager_tensor currently raises a ValueError, not a
E:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\ops\array_ops.py in concat(values, axis, name) 1252 axis, name="concat_dim", 1253 dtype=dtypes.int32).get_shape().assert_is_compatible_with( -> 1254 tensor_shape.scalar()) 1255 return identity(values[0], name=scope) 1256 return gen_array_ops.concat_v2(values=values, axis=axis, name=name)
E:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\framework\tensor_shape.py in assert_is_compatible_with(self, other) 1021 """ 1022 if not self.is_compatible_with(other): -> 1023 raise ValueError("Shapes %s and %s are incompatible" % (self, other)) 1024 1025 def most_specific_compatible_shape(self, other):
ValueError: Shapes (2, 35, 1) and () are incompatible
### I ran your script on Jupyter and python 3. Could you tell me how to fix it?