File "D:\lstnet_keras-master\Train_Batch_a1.py", line 11, in <module> tf.config.experimental.set_memory_growth(physical_devices[0], True) IndexError: list index out of range

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...

File d:\lstnet_keras-master\lstm\lstnet_interface_a38.py:3 import tensorflow.keras as keras ModuleNotFoundError: No module named 'tensorflow.keras'

这个错误提示是因为当前代码运行环境中没有安装 tensorflow.keras 模块。你可以使用以下命令来安装此模块： pip install tensorflow 如果你已经安装了 tensorflow，可以尝试升级到最新版本，也许这个...

File D:\lstnet_keras-master\lstm\LSTNet_Interface_a38.py:100 in init assert d_model % self.num_heads == 0 AssertionError

这个错误通常是因为在使用 Transformer 模型时，d_model（即模型中嵌入向量的维度）不能被 num_heads（即注意力头的数量）整除。这是因为注意力头需要平均分配嵌入向量的维度，从而确保每个头可以学习到不同的...

File d:\lstnet_keras-master\lstm\未命名5.py:11 from tensorflow.contrib import layers ModuleNotFoundError: No module named 'tensorflow.contrib'

这个错误提示是因为新版本的 TensorFlow 不再支持 tensorflow.contrib 模块。如果你使用的是旧版本的 TensorFlow...例如，layers 模块可以使用 tf.keras.layers 替代，具体的修改方法需要根据代码具体情况而定。

File D:\lstnet_keras-master\lstm\LSTNet_Interface_a391.py:286 in LSTNetAttention model.add(keras.layers.Embedding(input_dim=10000, output_dim=300, mask_zero=True)) AttributeError: 'Model' object has no attribute 'add'

这个错误是因为你在一个已经构建的 Keras 模型对象上尝试添加一个新的 Keras 层，但是模型对象并没有 add() 方法。这可能是因为你使用了 Model() 来创建模型对象，而不是使用 Sequential()。在 Model() 中，...

File "C:\lstnet_keras-master\lstm\LSTNet_Interface_a373.py", line 225, in LSTNetAttention input_data1 = residual_block(input_data1, filters=128, kernel_size=3) TypeError: residual_block() got an unexpected keyword argument 'kernel_size'

from tensorflow.keras.layers import Conv2D, BatchNormalization, Add, Activation, Lambda def residual_block(x, filters, kernel_size=3): # 定义第一个卷积层 conv1 = Conv2D(filters, (kernel_size, ...

File "C:\lstnet_keras-master\lstm\LSTNet_Interface_a36.py", line 176, in attention_3d_block output_attention_mul = Multiply([inputs, a_probs]) NameError: name 'Multiply' is not defined

这是一个名称错误，看起来...请确保已经导入了Keras中的Multiply模块，可以使用以下代码导入： from keras.layers import Multiply 如果仍然存在问题，请检查你的Keras版本是否正确，或者尝试重新安装Keras。

File "C:\lstnet_keras-master\lstm\LSTNet_Interface_a36.py", line 173, in attention_3d_block a_probs = Permute((1, 2), name=namevec)(a) NameError: name 'Permute' is not defined

这个错误是因为代码中没有导入 Permute。你需要在代码文件的开头添加以下导入语句： python ...如果你已经导入了 keras.layers，那么你可以直接使用 Permute，否则你需要先安装 Keras 库。

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

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...

d:\lstnet_keras-master\load_1h_signal_a373.py:268: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead. forestdata = forestdata.append(forestdata.iloc[-1,:],ignore_index = True) !next pandas.errors.InvalidIndexError: (slice(None, None, None), slice(-10, None, None))

这是一个警告和错误信息，其中FutureWarning表示这个方法在未来版本中将被移除，而InvalidIndexError表示使用了无效的索引。具体地，第268行的代码使用了过时的DataFrame.append()方法，而且在使用时还传递了一个...

concat = concatenate([lstm_out1,lstm_out2]) model = Model(inputs=[input_data1,input_data2], outputs=concat) # 添加其他层 #model = Model(inputs=[input_data1, input_data2], outputs=concat) model.add(keras.layers.Embedding(input_dim=10000, output_dim=300, mask_zero=True)) model.add(keras.layers.Bidirectional(keras.layers.LSTM(units=32, return_sequences=True))) #model.add(SeqSelfAttention(attention_activation='sigmoid')) model.add(keras.layers.Dense(units=32)) model.add(keras.layers.Dense(units=5)) # 添加多步预测输出层 return model 产生如下错误： File D:\lstnet_keras-master\lstm\LSTNet_Interface_a391.py:281 in LSTNetAttention model.add(keras.layers.Embedding(input_dim=10000, output_dim=300, mask_zero=True)) AttributeError: 'Model' object has no attribute 'add'

from keras.layers import concatenate, Embedding, Bidirectional, LSTM, Dense def LSTNetAttention(): # 定义第一个输入 input_data1 = Input(shape=(None,), dtype="int32") lstm_out1 = Bidirectional...

LSTNet-Gluon:在Apache MXNet Gluon中使用LSTNet进行时间序列预测

Apache MXNet Gluon中的LSTNet实现该存储库包含本文的实现： ://arxiv.org/abs/1703.07015，基于该存储库中作者的原始PyTorch实现： : 请参考该论文以获取有关网络体系结构的背景知识。运行脚本要获取命令行...

请详细解释下面这段代码：作者：BINGO Hong 链接：https://zhuanlan.zhihu.com/p/61795416 来源：知乎著作权归作者所有。商业转载请联系作者获得授权，非商业转载请注明出处。 def make_model(self): x = Input(shape=(self.P, self.m)) # CNN，普通卷积，无casual-dilation c = Conv1D(self.hidC, self.Ck, activation='relu')(x) c = Dropout(self.dropout)(c) # RNN, 普通RNN r = GRU(self.hidR)(c) r = Lambda(lambda k: K.reshape(k, (-1, self.hidR)))(r) r = Dropout(self.dropout)(r) # skip-RNN，以skip为周期的RNN，需要对数据进行变换 if self.skip > 0: # c: batch_sizestepsfilters, steps=P-Ck s = Lambda(lambda k: k[:, int(-self.ptself.skip):, :])(c) s = Lambda(lambda k: K.reshape(k, (-1, self.pt, self.skip, self.hidC)))(s) s = Lambda(lambda k: K.permute_dimensions(k, (0,2,1,3)))(s) # 这里设置时间步长为周期数目self.pt，时序关系以周期间隔递进，输入维度为self.hidC s = Lambda(lambda k: K.reshape(k, (-1, self.pt, self.hidC)))(s) s = GRU(self.hidS)(s) s = Lambda(lambda k: K.reshape(k, (-1, self.skipself.hidS)))(s) s = Dropout(self.dropout)(s) # 合并RNN及Skip-RNN r = concatenate([r,s]) res = Dense(self.m)(r) # highway，模型线性AR if self.hw > 0: z = Lambda(lambda k: k[:, -self.hw:, :])(x) z = Lambda(lambda k: K.permute_dimensions(k, (0,2,1)))(z) # hw设置以7天(self.hw=7)的值做为特征，利用Dense求预测量 z = Lambda(lambda k: K.reshape(k, (-1, self.hw)))(z) z = Dense(1)(z) z = Lambda(lambda k: K.reshape(k, (-1, self.m)))(z) res = add([res, z]) if self.output != 'no': res = Activation(self.output)(res) model = Model(inputs=x, outputs=res) model.compile(optimizer=Adam(lr=self.lr, clipnorm=self.clip), loss=self.loss) # print(model.summary()) # plot_model(model, to_file="LSTNet_model.png", show_shapes=True) return model

11. 如果self.output不等于'no'，则对res应用激活函数self.output。 12. 最后，构建了一个模型(Model)，输入为x，输出为res。使用Adam优化器、指定学习率和梯度裁剪阈值，并使用self.loss作为损失函数进行模型编译...

ModuleNotFoundError: No module named 'algorithms.train_LSTNet'

根据提供的引用内容，问题是关于ModuleNotFoundError: No module named 'algorithms.train_LSTNet'的。根据引用[1]和引用[2]的信息，这个错误通常是由于缺少相应的模块或库导致的。在引用[3]中提到，pymoo库的版本是...

class SelfAttention(Layer): def init(self, output_dim, kwargs): self.output_dim = output_dim super(SelfAttention, self).init(kwargs) def build(self, input_shape): self.W = self.add_weight(name='W', shape=(input_shape[-1], self.output_dim), initializer='uniform', trainable=True) self.b = self.add_weight(name='b', shape=(self.output_dim,), initializer='zeros', trainable=True) self.u = self.add_weight(name='u', shape=(self.output_dim, 1), initializer='uniform', trainable=True) super(SelfAttention, self).build(input_shape) def call(self, x): uit = K.tanh(K.bias_add(K.dot(x, self.W), self.b)) ait = K.softmax(K.squeeze(K.dot(uit, self.u), axis=-1)) weighted_input = x * K.expand_dims(ait) return K.sum(weighted_input, axis=1) def compute_output_shape(self, input_shape): return (input_shape[0], self.output_dim) def LSTNetAttention(trainX1,trainX2,trainY,config): # 输入数据 input1 = Input(shape=(trainX1.shape[1], trainX1.shape[2])) # 定义输入层 # 定义attention权重 # Add LSTM layer lstm1 = LSTM(64, return_sequences=True)(input1) # Add Self-Attention layer Self_Attention1 = SelfAttention(64)(lstm1) # 应用注意力机制到第二个输入 # Input2: long-term time series with period #input2 = Input(shape=(trainX2.shape[1], trainX2.shape[2],)) input2 = Input(shape=(trainX2.shape[1], trainX2.shape[2])) # Add LSTM layer lstm2 = LSTM(64, return_sequences=True)(input2) # Add Self-Attention layer Self_Attention2 = SelfAttention(64)(lstm2) merged_output = concatenate([Self_Attention1,Self_Attention2]) lstm_out = LSTM(64, return_sequences=False)(merged_output) # 加入LSTM层 lstm_out = Dense(32, activation='relu')(lstm_out) res = Dense(trainY.shape[1])(lstm_out)

这段代码定义了一个带有两个输入的 LSTNet 模型，并在其中添加了自注意力机制。其中，第一个输入是一个时间序列数据，经过 LSTM 后进行自注意力机制处理；第二个输入是一个长期时间序列数据，也经过 LSTM 后进行自...

File "D:\lstnet_keras-master\Train_Batch_a1.py", line 11, in <module> tf.config.experimental.set_memory_growth(physical_devices[0], True) IndexError: list index out of range

File d:\lstnet_keras-master\train_batch_a36.py:49 from sklearn import metrics ModuleNotFoundError: No module named 'sklearn'

相关推荐

File "D:\lstnet_keras-master\Train_Batch_a1.py", line 11, in <module> tf.config.experimental.set_memory_growth(physical_devices[0], True) IndexError: list index out of range

File d:\lstnet_keras-master\train_batch_a36.py:49 from sklearn import metrics ModuleNotFoundError: No module named 'sklearn'

相关推荐

模态分解与注意力机制结合的短期负荷预测LSTNet-Attn模型

Apache MXNet Gluon中的LSTNet时间序列预测模型实现

Transformer在时间序列预测的发展与代表论文

File d:\lstnet_keras-master\lstm\lstnet_interface_a38.py:3 import tensorflow.keras as keras ModuleNotFoundError: No module named 'tensorflow.keras'

File D:\lstnet_keras-master\lstm\LSTNet_Interface_a38.py:100 in __init__ assert d_model % self.num_heads == 0 AssertionError

File d:\lstnet_keras-master\lstm\未命名5.py:11 from tensorflow.contrib import layers ModuleNotFoundError: No module named 'tensorflow.contrib'

File D:\lstnet_keras-master\lstm\LSTNet_Interface_a391.py:286 in LSTNetAttention model.add(keras.layers.Embedding(input_dim=10000, output_dim=300, mask_zero=True)) AttributeError: 'Model' object has no attribute 'add'

File "C:\lstnet_keras-master\lstm\LSTNet_Interface_a373.py", line 225, in LSTNetAttention input_data1 = residual_block(input_data1, filters=128, kernel_size=3) TypeError: residual_block() got an unexpected keyword argument 'kernel_size'

File "C:\lstnet_keras-master\lstm\LSTNet_Interface_a36.py", line 176, in attention_3d_block output_attention_mul = Multiply([inputs, a_probs]) NameError: name 'Multiply' is not defined

File "C:\lstnet_keras-master\lstm\LSTNet_Interface_a36.py", line 173, in attention_3d_block a_probs = Permute((1, 2), name=namevec)(a) NameError: name 'Permute' is not defined

LSTNet-Gluon:在Apache MXNet Gluon中使用LSTNet进行时间序列预测

ModuleNotFoundError: No module named 'algorithms.train_LSTNet'

最新推荐

教师节主题班会.pptx

学生网络安全教育主题班会.pptx

正整数数组验证库：确保值符合正整数规则

管理建模和仿真的文件

【损失函数与随机梯度下降】：探索学习率对损失函数的影响，实现高效模型训练

在ADS软件中，如何选择并优化低噪声放大器的直流工作点以实现最佳性能？

系统移植工具集：镜像、工具链及其他必备软件包

"互动学习：行动中的多样性与论文攻读经历"

【损失函数与批量梯度下降】：分析批量大小对损失函数影响，优化模型学习路径

在设计高性能模拟电路时，如何根据应用需求选择合适的运算放大器，并评估供电对电路性能的影响？

File D:\lstnet_keras-master\lstm\LSTNet_Interface_a38.py:100 in init assert d_model % self.num_heads == 0 AssertionError