ValueError Traceback (most recent call last)
Cell In[35], line 4
2 accuracy=[0]*20
3 for epoch in range(epochs):
----> 4 train_loop(lin_model,train_dataset)
5 loss[epoch],accuracy[epoch]=test_loop(lin_model,test_dataset,loss_fn)
6 print(f"epoch: {epoch+1}, loss: {loss[epoch]}, accuracy: {accuracy[epoch]}")
Cell In[32], line 6, in train_loop(model, dataset)
4 # loss_total=0
5 for batch,(data,label) in enumerate(dataset.create_tuple_iterator()):
----> 6 loss=train_step(data,label)
Cell In[30], line 2, in train_step(data, label)
1 def train_step(data,label):
----> 2 (loss,_),grads=grad_fn(data,label)
3 optimizer(grads)
4 return loss
File D:\10_The_Programs\4_The_Codes\00_virtual_environment\DeepLearning\Lib\site-packages\mindspore\ops\composite\base.py:638, in _Grad.__call__.<locals>.after_grad(*args, **kwargs)
637 def after_grad(*args, **kwargs):
--> 638 return grad_(fn_, weights)(*args, **kwargs)
File D:\10_The_Programs\4_The_Codes\00_virtual_environment\DeepLearning\Lib\site-packages\mindspore\common\api.py:187, in _wrap_func.<locals>.wrapper(*arg, **kwargs)
185 @wraps(fn)
186 def wrapper(*arg, **kwargs):
--> 187 results = fn(*arg, **kwargs)
188 return _convert_python_data(results)
File D:\10_The_Programs\4_The_Codes\00_virtual_environment\DeepLearning\Lib\site-packages\mindspore\ops\composite\base.py:610, in _Grad.__call__.<locals>.after_grad(*args, **kwargs)
608 @_wrap_func
609 def after_grad(*args, **kwargs):
--> 610 run_args, res = self._pynative_forward_run(fn, grad_, weights, *args, **kwargs)
611 if self.has_aux:
612 out = _pynative_executor.grad_aux(fn, grad_, weights, grad_position, *run_args)
File D:\10_The_Programs\4_The_Codes\00_virtual_environment\DeepLearning\Lib\site-packages\mindspore\ops\composite\base.py:671, in _Grad._pynative_forward_run(self, fn, grad, weights, *args, **kwargs)
669 _pynative_executor.set_grad_flag(True)
670 _pynative_executor.new_graph(fn, *args, **kwargs)
--> 671 outputs = fn(*args, **kwargs)
672 _pynative_executor.end_graph(fn, outputs, *args, **kwargs)
673 run_forward = True
File D:\10_The_Programs\4_The_Codes\00_virtual_environment\DeepLearning\Lib\site-packages\mindspore\ops\composite\base.py:578, in _Grad.__call__.<locals>.aux_fn(*args, **kwargs)
577 def aux_fn(*args, **kwargs):
--> 578 outputs = fn(*args, **kwargs)
579 if not isinstance(outputs, tuple) or len(outputs) < 2:
580 raise ValueError("When has_aux is True, origin fn requires more than one outputs.")
Cell In[28], line 2, in forward_fn(data, label)
1 def forward_fn(data,label):
----> 2 logits=lin_model(data)
3 loss=loss_fn(logits,label)
4 return loss,logits
File D:\10_The_Programs\4_The_Codes\00_virtual_environment\DeepLearning\Lib\site-packages\mindspore\nn\cell.py:1355, in Cell.__call__(self, *args, **kwargs)
1352 if not (self.requires_grad or self._dynamic_shape_inputs or self.mixed_precision_type):
1353 if not (self._forward_pre_hook or self._forward_hook or self._backward_pre_hook or self._backward_hook or
1354 self._shard_fn or self._recompute_cell or (self.has_bprop and _pynative_executor.requires_grad())):
-> 1355 return self.construct(*args, **kwargs)
1357 return self._run_construct(*args, **kwargs)
1359 return self._complex_call(*args, **kwargs)
Cell In[25], line 10, in linear_net.construct(self, x)
8 def construct(self,x):
9 x=self.flatten(x)
---> 10 x=self.dense1(x)
11 x=self.dense2(x)
12 return x
File D:\10_The_Programs\4_The_Codes\00_virtual_environment\DeepLearning\Lib\site-packages\mindspore\nn\cell.py:1355, in Cell.__call__(self, *args, **kwargs)
1352 if not (self.requires_grad or self._dynamic_shape_inputs or self.mixed_precision_type):
1353 if not (self._forward_pre_hook or self._forward_hook or self._backward_pre_hook or self._backward_hook or
1354 self._shard_fn or self._recompute_cell or (self.has_bprop and _pynative_executor.requires_grad())):
-> 1355 return self.construct(*args, **kwargs)
1357 return self._run_construct(*args, **kwargs)
1359 return self._complex_call(*args, **kwargs)
File D:\10_The_Programs\4_The_Codes\00_virtual_environment\DeepLearning\Lib\site-packages\mindspore\nn\layer\basic.py:744, in Dense.construct(self, x)
742 x = self.matmul(x, self.weight)
743 if self.has_bias:
--> 744 x = self.bias_add(x, self.bias)
745 if self.activation_flag:
746 x = self.activation(x)
File D:\10_The_Programs\4_The_Codes\00_virtual_environment\DeepLearning\Lib\site-packages\mindspore\ops\auto_generate\gen_ops_prim.py:3859, in BiasAdd.__call__(self, input_x, bias)
3858 def __call__(self, input_x, bias):
-> 3859 return super().__call__(input_x, bias, self.data_format)
File D:\10_The_Programs\4_The_Codes\00_virtual_environment\DeepLearning\Lib\site-packages\mindspore\ops\primitive.py:413, in Primitive.__call__(self, *args)
411 if should_elim:
412 return output
--> 413 return _run_op(self, self.name, args)
File D:\10_The_Programs\4_The_Codes\00_virtual_environment\DeepLearning\Lib\site-packages\mindspore\ops\primitive.py:1022, in _run_op(obj, op_name, args)
1020 def _run_op(obj, op_name, args):
1021 """Single op execution function supported by ge in PyNative mode."""
-> 1022 res = _pynative_executor.run_op_async(obj, op_name, args)
1023 # Add for jit context.
1024 if jit_context():
File D:\10_The_Programs\4_The_Codes\00_virtual_environment\DeepLearning\Lib\site-packages\mindspore\common\api.py:1638, in _PyNativeExecutor.run_op_async(self, *args)
1628 def run_op_async(self, *args):
1629 """
1630 Run single op async.
1631
(...) 1636 StubNode, result of run op.
1637 """
-> 1638 return self._executor.run_op_async(*args)
ValueError: For 'MatMul' the input dimensions must be equal, but got 'x1_col': 784 and 'x2_row': 100.
----------------------------------------------------
- C++ Call Stack: (For framework developers)
----------------------------------------------------
kins/agent-working-dir/workspace/executor0/mindspore/mindspore/ops/infer/ops_func_impl//matmul.cc:200 mindspore::ops::MatMulFuncImpl::InferShape
我的神经网络训练时出现这种情况是什么意思？我用的库是Mindspore？
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本文提供了一个优快云博客的链接示例，尽管具体内容未直接给出，但可以推测这是一篇关于编程技术的文章。
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