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。

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。

def dp(dist_mat): N, M = dist_mat.shape cost_mat = np.zeros((N + 1, M + 1)) for i in range(1, N + 1): cost_mat[i, 0] = np.inf for i in range(1, M + 1): cost_mat[0, i] = np.inf traceback_mat = np.zeros((N, M)) for i in range(N): for j in range(M): penalty = [ cost_mat[i, j], cost_mat[i, j + 1], cost_mat[i + 1, j]] i_penalty = np.argmin(penalty) cost_mat[i + 1, j + 1] = dist_mat[i, j] + penalty[i_penalty] traceback_mat[i, j] = i_penalty i = N - 1 j = M - 1 path = [(i, j)] while i > 0 or j > 0: tb_type = traceback_mat[i, j] if tb_type == 0: i = i - 1 j = j - 1 elif tb_type == 1: i = i - 1 elif tb_type == 2: j = j - 1 path.append((i, j)) cost_mat = cost_mat[1:, 1:] return (path[::-1], cost_mat)解释一下

这是一个动态规划算法，用于求解两个序列之间的最小编辑距离。其中，dist_mat是一个N*M的矩阵，表示两个序列之间的距离矩阵。cost_mat是一个(N+1)*(M+1)的矩阵，用于存储最小编辑距离。traceback_mat是一个N*M的矩阵，用于记录路径。算法的核心是通过递推的方式，计算出cost_mat和traceback_mat。最后，通过回溯traceback_mat，可以得到最小编辑距离的路径和最小编辑距离的值。