model = LSTNetAttention(trainX1,trainX2,trainY,config) File D:\lstnet_keras-master\lstm\LSTNet_Interface_a38.py:451 in LSTNetAttention context_vector1 = Dot(axes=(2, 1))([attention_weights1, input1]) File ~\anaconda4\envs\tensorflow\lib\site-packages\tensorflow\python\keras\engine\base_layer.py:897 in __call__ self._maybe_build(inputs) File ~\anaconda4\envs\tensorflow\lib\site-packages\tensorflow\python\keras\engine\base_layer.py:2416 in _maybe_build self.build(input_shapes) # pylint:disable=not-callable File ~\anaconda4\envs\tensorflow\lib\site-packages\tensorflow\python\keras\utils\tf_utils.py:316 in wrapper output_shape = fn(instance, input_shape) File ~\anaconda4\envs\tensorflow\lib\site-packages\tensorflow\python\keras\layers\merge.py:674 in build raise ValueError('Dimension incompatibility ' ValueError: Dimension incompatibility 1 != 48. Layer shapes: (None, 48, 1), (None, 48, 21). Chosen axes: 2, 1
时间: 2023-06-28 19:06:18 浏览: 177
这个错误是由于您的模型在进行 Dot 操作时,矩阵的维度不匹配导致的。具体来说,您传递给 Dot 层的两个输入张量的形状分别为 (batch_size, 48, 1) 和 (batch_size, 48, 21),而您指定的 axes 参数为 (2, 1)。这意味着您要对第一个张量的第二个维度和第二个张量的第一个维度进行点积操作,但是这两个维度的大小不同,分别为 1 和 48,所以会抛出 ValueError 异常。
您需要检查一下您的代码,特别是输入张量的构建部分,确保它们的形状与您的模型定义相匹配。如果您需要更多帮助,请提供更多代码和上下文信息。
相关问题
(64849, 21) (64849, 1) trainX Y shape is: (64599, 48, 21) (64599, 60, 21) (64599, 1) Traceback (most recent call last): File ~\anaconda4\envs\tensorflow\lib\site-packages\spyder_kernels\py3compat.py:356 in compat_exec exec(code, globals, locals) File d:\lstnet_keras-master\train_batch_a1.py:148 model,normalize = startTrainMult_1H(data,name,config) File D:\lstnet_keras-master\lstm\LSTNet_Interface_a38.py:570 in startTrainMult_1H model = trainModel(trainX1, trainX2 , trainY, config) File D:\lstnet_keras-master\lstm\LSTNet_Interface_a38.py:500 in trainModel model = LSTNetAttention(trainX1,trainX2,trainY,config) File D:\lstnet_keras-master\lstm\LSTNet_Interface_a38.py:443 in LSTNetAttention lstm1 = LSTM(64, return_sequences=True)(input1) File ~\anaconda4\envs\tensorflow\lib\site-packages\keras\layers\recurrent.py:541 in __call__ return super(RNN, self).__call__(inputs, **kwargs) File ~\anaconda4\envs\tensorflow\lib\site-packages\keras\backend\tensorflow_backend.py:75 in symbolic_fn_wrapper return func(*args, **kwargs) File ~\anaconda4\envs\tensorflow\lib\site-packages\keras\engine\base_layer.py:475 in __call__ previous_mask = _collect_previous_mask(inputs) File ~\anaconda4\envs\tensorflow\lib\site-packages\keras\engine\base_layer.py:1441 in _collect_previous_mask mask = node.output_masks[tensor_index] AttributeError: 'Node' object has no attribute 'output_masks'
这个错误是由于 Keras 版本的问题导致的。在旧版本的 Keras 中,LSTM 层的参数是 return_sequences 和 return_state,而在新版本的 Keras 中,LSTM 层的参数是 return_sequences、return_state 和 return_attention。你可以检查一下你使用的 Keras 版本是否与代码中使用的版本相同。如果不同,你需要修改代码以适应你的 Keras 版本。另外,你可以尝试在 LSTM 层中添加 `return_sequences=True` 参数来解决这个问题。
trainX Y shape is: (4755, 48, 23) (4755, 60, 23) (4755, 5, 1) Hx= 1 Traceback (most recent call last): File ~\anaconda4\envs\tensorflow\lib\site-packages\spyder_kernels\py3compat.py:356 in compat_exec exec(code, globals, locals) File d:\lstnet_keras-master\train_batch_a391.py:172 model,normalize = startTrainMult_1H(df,name,config) File D:\lstnet_keras-master\lstm\LSTNet_Interface_a391.py:417 in startTrainMult_1H model = trainModel(trainX1, trainX2 , trainY, config) File D:\lstnet_keras-master\lstm\LSTNet_Interface_a391.py:340 in trainModel model = LSTNetAttention(trainX1,trainX2,trainY,config) File D:\lstnet_keras-master\lstm\LSTNet_Interface_a391.py:282 in LSTNetAttention bidirectional = Bidirectional(LSTM(units=32, return_sequences=True))(embedding) File ~\anaconda4\envs\tensorflow\lib\site-packages\keras\layers\wrappers.py:437 in __call__ return super(Bidirectional, self).__call__(inputs, **kwargs) File ~\anaconda4\envs\tensorflow\lib\site-packages\keras\backend\tensorflow_backend.py:75 in symbolic_fn_wrapper return func(*args, **kwargs) File ~\anaconda4\envs\tensorflow\lib\site-packages\keras\engine\base_layer.py:446 in __call__ self.assert_input_compatibility(inputs) File ~\anaconda4\envs\tensorflow\lib\site-packages\keras\engine\base_layer.py:338 in assert_input_compatibility raise ValueError('Input ' + str(input_index) + ValueError: Input 0 is incompatible with layer bidirectional_1: expected ndim=3, found ndim=4
根据你提供的信息,这个错误是在使用 Keras 框架中进行训练时出现的。错误提示中指出了问题的源头是在 Bidirectional 层的第一个输入,它期望一个三维张量作为输入,但实际上输入的是一个四维张量。这可能是因为你的输入数据的维度不正确导致的。
你可以检查一下你的输入数据的维度是否正确。确保你的输入数据的维度是 (batch_size, time_steps, features),其中 batch_size 表示每个 batch 中的样本数,time_steps 表示序列的时间步数,features 表示每个时间步中的特征数。如果你的输入数据维度正确,你可以尝试将它们转换为正确的维度,例如使用 reshape() 函数,以便它们可以被正确地输入到模型中进行训练。
阅读全文