ValueError: Unknown layer:name

这个错误通常表示在模型中引用了一个不存在的层名。

可能有几种原因导致这个错误：

1. 拼写错误：请检查你的代码是否正确地拼写了层名。

2. 模型结构已更改：如果你修改了模型的结构，例如删除或更改了某些层，那么保存的模型权重将不再与新模型相对应，因此在加载模型时可能会出现这个错误。

3. 加载错误：如果你正在加载一个预训练的模型，那么这个错误可能是由于加载的模型与你的代码或环境不兼容引起的。请检查你的代码、环境和模型文件是否正确。

要解决这个错误，你可以检查以上可能的原因并尝试调整你的代码或模型结构来解决这个问题。

相关问题

def create_LSTM_model(): # instantiate the model model = Sequential() model.add(Input(shape=(X_train.shape[1], X_train.shape[2]))) model.add(Reshape((X_train.shape[1], 1, X_train.shape[2], 1))) # cnn1d Layers model.add(ConvLSTM2D(filters=64, kernel_size=(1,3), activation='relu', padding='same', return_sequences=True)) model.add(Flatten()) model.add(Dropout(0.5)) model.add(RepeatVector(1)) # 添加lstm层 model.add(LSTM(64, activation = 'relu', return_sequences=True)) model.add(Dropout(0.5)) #添加注意力层 model.add(LSTM(64, activation = 'relu', return_sequences=False)) # 添加dropout model.add(Dropout(0.5)) model.add(Dense(128)) # 输出层 model.add(Dense(1, name='Output')) # 编译模型 model.compile(optimizer='adam', loss='mse', metrics=['mae']) return model # lstm network model = create_LSTM_model() # summary print(model.summary())修改该代码，解决ValueError: in user code: File "C:\Users\gaozhiyuan\anaconda3\lib\site-packages\keras\engine\training.py", line 1284, in train_function * return step_function(self, iterator) File "C:\Users\gaozhiyuan\anaconda3\lib\site-packages\keras\engine\training.py", line 1268, in step_function ** outputs = model.distribute_strategy.run(run_step, args=(data,)) File "C:\Users\gaozhiyuan\anaconda3\lib\site-packages\keras\engine\training.py", line 1249, in run_step ** outputs = model.train_step(data) File "C:\Users\gaozhiyuan\anaconda3\lib\site-packages\keras\engine\training.py", line 1050, in train_step y_pred = self(x, training=True) File "C:\Users\gaozhiyuan\anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 70, in error_handler raise e.with_traceback(filtered_tb) from None File "C:\Users\gaozhiyuan\anaconda3\lib\site-packages\keras\layers\reshaping\reshape.py", line 118, in _fix_unknown_dimension raise ValueError(msg) ValueError: Exception encountered when calling layer 'reshape_51' (type Reshape). total size of new array must be unchanged, input_shape = [10, 1, 1, 5], output_shape = [10, 1, 1, 1] Call arguments received by layer 'reshape_51' (type Reshape): • inputs=tf.Tensor(shape=(None, 10, 1, 1, 5), dtype=float32)问题

该代码中的问题在于 Reshape 层的输出形状与后续层的输入形状不一致，导致了 ValueError。具体来说，Reshape 层将输入形状从 (batch_size, time_steps, features) 转换为了 (batch_size, time_steps, 1, features, 1)，然后经过 ConvLSTM2D 层后又经过了 Flatten 层，这个操作将 5 维的张量打平成了 3 维的张量，但是 RepeatVector 层却要求输入是 3 维的。因此，我们需要修改 Reshape 层的输出形状，使其与后续层的输入形状一致。具体来说，可以将 Reshape 层的输出形状修改为 (batch_size, time_steps, features)，即删除 Reshape 层中的两个 1，代码如下所示：

def create_LSTM_model():
    # instantiate the model
    model = Sequential()
    model.add(Input(shape=(X_train.shape[1], X_train.shape[2])))
    model.add(Reshape((X_train.shape[1], X_train.shape[2], 1)))
    # cnn1d Layers
    model.add(ConvLSTM2D(filters=64, kernel_size=(1,3), activation='relu', padding='same', return_sequences=True))
    model.add(Flatten())
    model.add(Dropout(0.5))
    model.add(RepeatVector(1))
    # 添加lstm层
    model.add(LSTM(64, activation='relu', return_sequences=True))
    model.add(Dropout(0.5))
    #添加注意力层
    model.add(LSTM(64, activation='relu', return_sequences=False))
    # 添加dropout
    model.add(Dropout(0.5))
    model.add(Dense(128))
    # 输出层
    model.add(Dense(1, name='Output'))
    # 编译模型
    model.compile(optimizer='adam', loss='mse', metrics=['mae'])
    return model

# lstm network
model = create_LSTM_model()

# summary
print(model.summary())

这样修改后，Reshape 层的输出形状就与后续层的输入形状一致了，可以避免上述 ValueError 的问题。

使用模型进行预测... WARNING:tensorflow:Model was constructed with shape (None, 3989, 10) for input KerasTensor(type_spec=TensorSpec(shape=(None, 3989, 10), dtype=tf.float32, name='dense_input'), name='dense_input', description="created by layer 'dense_input'"), but it was called on an input with incompatible shape (None, 10). 1/1 [==============================] - 0s 36ms/step --------------------------------------------------------------------------- ValueError Traceback (most recent call last) Cell In[20], line 14 11 predicted = model.predict(unknown, verbose=1) 13 # 将预测结果保存到新的 CSV 文件中 ---> 14 result = pd.DataFrame(predicted, columns=['prediction']) 15 result.to_csv('predicted_result.csv', index=False) 16 print("输入的数据为: ") File ~\AppData\Roaming\Python\Python39\site-packages\pandas\core\frame.py:757, in DataFrame.__init__(self, data, index, columns, dtype, copy) 746 mgr = dict_to_mgr( 747 # error: Item "ndarray" of "Union[ndarray, Series, Index]" has no 748 # attribute "name" (...) 754 copy=_copy, 755 ) 756 else: --> 757 mgr = ndarray_to_mgr( 758 data, 759 index, 760 columns, 761 dtype=dtype, 762 copy=copy, 763 typ=manager, 764 ) 766 # For data is list-like, or Iterable (will consume into list) 767 elif is_list_like(data): File ~\AppData\Roaming\Python\Python39\site-packages\pandas\core\internals\construction.py:337, in ndarray_to_mgr(values, index, columns, dtype, copy, typ) 332 # _prep_ndarraylike ensures that values.ndim == 2 at this point 333 index, columns = _get_axes( 334 values.shape[0], values.shape[1], index=index, columns=columns 335 ) --> 337 _check_values_indices_shape_match(values, index, columns) 339 if typ == "array": 340 if issubclass(values.dtype.type, str): File ~\AppData\Roaming\Python\Python39\site-packages\pandas\core\internals\construction.py:408, in _check_values_indices_shape_match(values, index, columns) 406 passed = values.shape 407 implied = (len(index), len(columns)) --> 408 raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}") ValueError: Shape of passed values is (1, 3), indices imply (1, 1)该怎么修改代码

根据错误提示，可以看出预测数据的形状不匹配。模型的输入形状是 `(None, 3989, 10)`，而传入的数据的形状是 `(1, 10)`，因此需要对数据进行相应的处理。

假设你要预测的数据只有一组，可以通过以下代码进行处理：

import pandas as pd
import numpy as np
from tensorflow.keras.models import load_model

# 加载模型
print("使用模型进行预测...")
filepath = "./best_model2222.h5"
model = load_model(filepath)

# 读取 CSV 文件
data = pd.read_csv('shixiongshuju.csv')
# 将数据转换为 NumPy 数组
unknown = data.values.astype(np.float32)

# 将预测数据的形状转换为 (1, 3989, 10)
unknown = np.expand_dims(unknown[0], axis=0)

# 进行预测
predicted = model.predict(unknown, verbose=1)

# 将预测结果保存到新的 CSV 文件中
result = pd.DataFrame(predicted, columns=['prediction'])
result.to_csv('predicted_result.csv', index=False)
print("预测结果已保存到 predicted_result.csv 文件中。")

这段代码将会把预测结果保存在名为 `predicted_result.csv` 的文件中。请注意，代码中的列名 `prediction` 可以根据实际情况进行修改。