双输出模型修改： res1 = Dense(trainY1.shape[1])(lstm_out) res2 = Dense(trainY2.shape[1])(lstm_out) #highway 使用Dense模拟AR自回归过程，为预测添加线性成份，同时使输出可以响应输入的尺度变化。 highway_window = config.highway_window #截取近3个窗口的时间维保留了所有的输入维度 z = Lambda(lambda k: k[:, -highway_window:, :])(input_data1) z = Lambda(lambda k: K.permute_dimensions(k, (0, 2, 1)))(z) z = Lambda(lambda k: K.reshape(k, (-1, highway_window*trainX1.shape[2])))(z) z = Dense(trainY1.shape[1])(z) z2 = Dense(trainY2.shape[1])(z) res1 = add([res1,z]) res1 = Activation('sigmoid')(res1) res2 = add([res2,z2]) res2 = Activation('sigmoid')(res2) model = Model(inputs=[input_data1,input_data2], outputs=[res1,res2]) return model

LSTM深度学习.zip_LSTM_MNIST_lstm 分类_lstm分类 keras_zip

例如，可以使用Sequential模型，然后添加LSTM层，接着是Dense层，最后是输出层，可能包含softmax激活函数以进行多分类。标签中的"lstm_分类_keras"强调了使用Keras进行LSTM的分类任务。Keras是TensorFlow的...

simple LSTM.zip_LSTM_LSTM python_lstm python code_lstm训练_python

2. **模型定义**：接下来，会定义LSTM模型。在Keras中，可以使用Sequential模型API来构建LSTM网络。一个基本的LSTM模型可能包括一个或多个LSTM层，接着是全连接（Dense）层以进行分类或回归。 python model = ...

lstm_out = BatchNormalization()(lstm_out) lstm_out = Dense(32, activation='relu')(lstm_out) lstm_out = BatchNormalization()(lstm_out) lstm_out = Dense(16, activation='tanh')(lstm_out) res1 = Dense(trainY1.shape[1])(lstm_out) res2 = Dense(trainY2.shape[1])(lstm_out) 追加了一个输出，写法正确吗

res2 = Dense(trainY2.shape[1])(lstm_out) res3 = Dense(trainY3.shape[1])(lstm_out) # 这里添加了一个新的输出 model = Model(inputs=[input_data1, input_data2], outputs=[res1, res2, res3]) # 将新的输出...

res1 = Dense(trainY1.shape[1])(lstm_out) 就等同于res1 = Dense(6)(lstm_out)

如果 trainY1.shape[1] 的值为 6，那么 Dense(trainY1.shape[1])(lstm_out) 确实等同于 Dense(6)(lstm_out)。但如果 trainY1.shape[1] 的值不是 6，那么这两者就不等同了。因此，为了避免混淆和错误，通常...

lstm_out = BatchNormalization()(lstm_out) lstm_out = Dense(32, activation='relu')(lstm_out) lstm_out = BatchNormalization()(lstm_out) lstm_out = Dense(16, activation='tanh')(lstm_out) res1 = Dense(trainY1.shape[1])(lstm_out) 这样可以吗，输出6个时间步的价格预测

res1 = Dense(6)(lstm_out) # 输出 6 个时间步的价格预测这样修改后，模型就可以输出 6 个时间步的价格预测了。同时需要注意调整损失函数和训练数据的格式，以考虑多个时间步的预测误差和目标值。

下面是单步预测模型，通过修改数据输出，对于多步输出结构，模型本身需要做什么修改？ res1 = Dense(trainY1.shape[1])(lstm_out) #res2 = Dense(trainY2.shape[1])(lstm_out) #highway 使用Dense模拟AR自回归过程，为预测添加线性成份，同时使输出可以响应输入的尺度变化。 highway_window = config.highway_window #截取近3个窗口的时间维保留了所有的输入维度 z = Lambda(lambda k: k[:, -highway_window:, :])(input_data1) z = Lambda(lambda k: K.permute_dimensions(k, (0, 2, 1)))(z) z = Lambda(lambda k: K.reshape(k, (-1, highway_window*trainX1.shape[2])))(z) z = Dense(trainY1.shape[1])(z) res1 = add([res1,z]) res1 = Activation('sigmoid')(res1) model = Model(inputs=[input_data1,input_data2], outputs=[res1])

对于多步输出结构，模型需要做以下修改： 1. 修改输出层的形状，以适应多个时间步的预测输出。可以使用一个包含多个 Dense 层的堆叠来实现多步输出。 2. 修改损失函数，以考虑多个时间步的预测误差。 3. 调整训练...

模型有这样的定义部分： lstm_out = BatchNormalization()(lstm_out) lstm_out = Dense(32, activation='relu')(lstm_out) lstm_out = BatchNormalization()(lstm_out) lstm_out = Dense(16, activation='tanh')(lstm_out) res = Dense(trainY.shape[1])(lstm_out) ，模型输出维度是大于16的，他前面全连接层定义的16是否有问题

在您提供的模型中，最后一个 Dense 层的输出维度是根据输入 trainY.shape[1] 自动识别的，因此不会对输出维度造成影响。而模型中的前两个 Dense 层的输出维度分别是 32 和 16，这些维度的选择需要根据具体的任务需求...

下面是时序预测单步单维输出定义，如果改为单步多维输出，需要修改模型定义吗： lstm_out = BatchNormalization()(lstm_out) lstm_out = Dense(16, activation='tanh')(lstm_out) res = Dense(trainY.shape[1])(lstm_out)

是的，如果需要改为单步多维...res = Dense(trainY.shape[1])(lstm_out) # trainY.shape[1] 为需要输出的维度这样就可以输出多维结果了。同时需要注意，根据具体的需求，可能需要对损失函数和评估指标进行修改。

我有一个单步模型，若改为多步预测，如何修改： input1 = Input(shape=(trainX1.shape[1], trainX1.shape[2])) conv1 = Conv1D(filters=128 , kernel_size=config.CK, strides=1, activation='relu') # for input1 # 多输入权重共享 conv2 = Conv1D(filters=128 , kernel_size=config.CK, strides=config.CK, activation='relu') # for input2 conv2.set_weights(conv1.get_weights()) # at least use same weight # CNN conv1out = conv1(input1) conv1out = Dropout(config.dropout)(conv1out) # RNN lstm1out = LSTM(config.lstm_batch_size)(conv1out) lstm1out = Dropout(config.dropout)(lstm1out) # Input2: long-term time series with period input2 = Input(shape=(trainX2.shape[1], trainX2.shape[2])) # CNN conv2out = conv2(input2) conv2out = Dropout(config.dropout)(conv2out) # RNN lstm2out = LSTM(config.lstm_batch_size)(conv2out) lstm2out = Dropout(config.dropout)(lstm2out) lstm_out = concatenate([lstm1out,lstm2out]) model.add(keras.layers.Embedding(input_dim=10000, output_dim=300, mask_zero=True)) model.add(keras.layers.Bidirectional(keras.layers.LSTM(units=128, return_sequences=True))) #model.add(SeqSelfAttention(attention_activation='sigmoid')) model.add(keras.layers.Dense(units=5)) #x = BatchNormalization() #x = Activation('relu')(x) res = Dense(trainY.shape[1])(lstm_out)

lstm_out = concatenate([lstm1out,lstm2out]) model.add(keras.layers.Embedding(input_dim=10000, output_dim=300, mask_zero=True)) model.add(keras.layers.Bidirectional(keras.layers.LSTM(units=128, return...

这是我模型最后阶段，你看如何加入GRACH ： res = Dense(trainY.shape[1])(lstm_out) #highway 使用Dense模拟AR自回归过程，为预测添加线性成份，同时使输出可以响应输入的尺度变化。 highway_window = config.highway_window #截取近3个窗口的时间维保留了所有的输入维度 z = Lambda(lambda k: k[:, -highway_window:, :])(input_data1) z = Lambda(lambda k: K.permute_dimensions(k, (0, 2, 1)))(z) z = Lambda(lambda k: K.reshape(k, (-1, highway_window*trainX1.shape[2])))(z) z = Dense(trainY.shape[1])(z) """ highway_window = config.highway_window z = input1[:, -highway_window:, :] z = Flatten()(K.permute_dimensions(z, (0, 2, 1))) z = Dense(trainY.shape[1])(z) """ res = add([res,z]) res = Activation('sigmoid')(res) model = Model(inputs=[input_data1,input_data2], outputs=res) return model

res = Dense(trainY.shape[1])(lstm_out) res = add([res, pred]) # 使用sigmoid激活函数输出最终结果 res = Activation('sigmoid')(res) # 创建模型 model = Model(inputs=[input_data1, input_data2], outputs=...

我的模型是这样的： def LSTNetAttention(trainX1,trainX2,trainY,config): # 输入数据 #inputs = tf.keras.layers.Input(shape=(seq_len, input_dim)) time_steps1 = trainX1.shape[1] input_dim1 = trainX1.shape[2] # 定义输入张量 input_data1 = Input(shape=(time_steps1, input_dim1)) #lstm1 = input_data1 .... concat = concatenate([lstm_out1,lstm_out2]) model.add(keras.layers.Embedding(input_dim=10000, output_dim=300, mask_zero=True)) model.add(keras.layers.Bidirectional(keras.layers.LSTM(units=128, return_sequences=True))) #model.add(SeqSelfAttention(attention_activation='sigmoid')) model.add(keras.layers.Dense(units=5)) model.add(keras.layers.Dense(units=多步预测步数)) # 添加多步预测输出层 highway_window = config.highway_window #截取近3个窗口的时间维保留了所有的输入维度 z = Lambda(lambda k: k[:, -highway_window:, :])(input_data1) z = Lambda(lambda k: K.permute_dimensions(k, (0, 2, 1)))(z) z = Lambda(lambda k: K.reshape(k, (-1, highway_window*trainX1.shape[2])))(z) z = Dense(trainY.shape[1])(z) res = add([res,z]) res = Activation('sigmoid')(res) model = Model(inputs=[input_data1,input_data2], outputs=res) return model，其中model.add形式是否有错误

concat = concatenate([lstm_out1, lstm_out2]) # 添加其他层 model = Model(inputs=[input_data1, input_data2], outputs=concat) model.add(Dense(units=5)) model.add(Dense(units=config.prediction_steps...

请把下面单步预测改变多步预测： concat = concatenate([lstm_out1,lstm_out2]) # 增加一个TimeDistributed层，以便对每个时间步进行相同的处理 td = TimeDistributed(Dense(128, activation='relu'))(concat) td = TimeDistributed(Dropout(0.2))(td) lstm_out = LSTM(64, return_sequences=False)(td) # 加入LSTM层 lstm_out = Dense(32, activation='relu')(lstm_out) lstm_out = Dense(16, activation='tanh')(lstm_out) res = Dense(trainY.shape[1])(lstm_out) #highway 使用Dense模拟AR自回归过程，为预测添加线性成份，同时使输出可以响应输入的尺度变化。 highway_window = config.highway_window #截取近3个窗口的时间维保留了所有的输入维度 z = Lambda(lambda k: k[:, -highway_window:, :])(input_data1) z = Lambda(lambda k: K.permute_dimensions(k, (0, 2, 1)))(z) z = Lambda(lambda k: K.reshape(k, (-1, highway_window*trainX1.shape[2])))(z) z = Dense(trainY.shape[1])(z) """ highway_window = config.highway_window z = input1[:, -highway_window:, :] z = Flatten()(K.permute_dimensions(z, (0, 2, 1))) z = Dense(trainY.shape[1])(z) """ res = add([res,z]) res = Activation('sigmoid')(res) model = Model(inputs=[input_data1,input_data2], outputs=res) return model

res = Dense(trainY.shape[1])(lstm_out) #highway 使用Dense模拟AR自回归过程，为预测添加线性成份，同时使输出可以响应输入的尺度变化。 highway_window = config.highway_window # 截取近3个窗口的时间维保留...

请检查这个多步预测模型定义是否有错误： concat = concatenate([lstm_out1,lstm_out2]) """ # 增加一个TimeDistributed层，以便对每个时间步进行相同的处理 td = TimeDistributed(Dense(128, activation='relu'))(concat) td = TimeDistributed(Dropout(0.2))(td) lstm_out = LSTM(64, return_sequences=True)(td) # 加入LSTM层 lstm_out = Dense(32, activation='relu')(lstm_out) lstm_out = Dense(16, activation='tanh')(lstm_out) res = Dense(trainY.shape[1])(lstm_out) """ #highway 使用Dense模拟AR自回归过程，为预测添加线性成份，同时使输出可以响应输入的尺度变化。 highway_window = config.highway_window #截取近3个窗口的时间维保留了所有的输入维度 z = Lambda(lambda k: k[:, -highway_window:, :])(input_data1) z = Lambda(lambda k: K.permute_dimensions(k, (0, 2, 1)))(z) z = Lambda(lambda k: K.reshape(k, (-1, highway_window*trainX1.shape[2])))(z) z = Dense(trainY.shape[1])(z) res = add([concat,z]) res = Activation('sigmoid')(res) model = Model(inputs=[input_data1,input_data2], outputs=res) # 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=128, return_sequences=True))) #model.add(SeqSelfAttention(attention_activation='sigmoid')) model.add(keras.layers.Dense(units=5)) model.add(keras.layers.Dense(units=8)) # 添加多步预测输出层 return model

它包含了LSTM层、Dense层、Dropout层、TimeDistributed层和Lambda层等，同时使用了concatenate和add函数。此外，它还添加了Embedding层、Bidirectional层、Dense层和多步预测输出层。需要注意的是，该模型的实现可能...

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)

在 LSTNetAttention 函数中，首先分别定义了两个输入层 input1 和 input2，并对它们分别应用 LSTM 和自注意力机制。然后将两个自注意力机制得到的输出合并，再经过一层 LSTM 和一层全连接层，最终输出模型预测...

具体到我的模型，请看看怎样修改： lstm_out = Dense(16, activation='tanh')(lstm_out) res = Dense(trainY.shape[1])(lstm_out) #highway 使用Dense模拟AR自回归过程，为预测添加线性成份，同时使输出可以响应输入的尺度变化。 highway_window = config.highway_window #截取近3个窗口的时间维保留了所有的输入维度 z = Lambda(lambda k: k[:, -highway_window:, :])(input_data1) z = Lambda(lambda k: K.permute_dimensions(k, (0, 2, 1)))(z) z = Lambda(lambda k: K.reshape(k, (-1, highway_window*trainX1.shape[2])))(z) z = Dense(trainY.shape[1])(z) res = add([res,z]) res = Activation('sigmoid')(res) model = Model(inputs=[input_data1,input_data2], outputs=res)

output1 = Dense(trainY.shape[1], activation='sigmoid', name='output1')(lstm_out) loss1 = 'binary_crossentropy' # 新增第二个输出层及相应的损失函数 highway_window = config.highway_window z = Lambda...

看看这个结构算不算seq2seq : concat = concatenate([lstm_out1,lstm_out2]) conv_out = Conv1D(filters=64, kernel_size=5, activation='relu')(concat) # 添加一个卷积层 # 添加MaxPooling1D池化层, 是否加这层,需要测试，适合于强烈的局部特征 #conv_out = MaxPooling1D(pool_size=2)(conv_out) conv_out = Dropout(0.2)(conv_out) # 添加Dropout层 # 增加一个TimeDistributed层，以便对每个时间步进行相同的处理 td = TimeDistributed(Dense(128, activation='relu'))(conv_out) td = TimeDistributed(Dropout(0.2))(td) #lstm_out = LSTM(64, return_sequences=False)(td) # 加入LSTM层 lstm_out = Bidirectional(LSTM(64, return_sequences=False))(td) # 替换为Bidirectional层 # 在LSTM层之后添加Dense层和BatchNormalization层 """ BatchNormalization可以进一步提高模型的稳定性并加速收敛。BatchNormalization通过标准化每个特征的均值和方差，可以使得模型更加鲁棒，同时也能够防止梯度消失和梯度爆炸问题。因此，即使数据已经规范化了，仍然建议在模型中使用BatchNormalization。 Batch Normalization不会增加模型的参数数量，但是会增加模型的计算量 """ lstm_out = BatchNormalization()(lstm_out) lstm_out = Dense(32, activation='relu')(lstm_out) lstm_out = BatchNormalization()(lstm_out) lstm_out = Dense(16, activation='tanh')(lstm_out) res1 = Dense(trainY1.shape[1])(lstm_out)

其中，LSTM 层和双向 LSTM 层可以提取输入序列的时序特征，Conv1D 层和 Dropout 层可以增强模型的泛化能力和抗噪性，Dense 层和 BatchNormalization 层可以进一步提高模型的准确性和稳定性。总之，这个结构不是 ...

def LSTNetAttention(trainX1,trainX2,trainY,config): # 输入数据 #inputs = tf.keras.layers.Input(shape=(seq_len, input_dim)) input1 = Input(shape=(trainX1.shape[1], trainX1.shape[2],)) # 定义attention权重 attention_weights1 = TimeDistributed(Dense(1))(input1) attention_weights1 = Activation('softmax')(attention_weights1) # 将attention权重应用于输入数据 context_vector1 = Dot(axes=(2, 1))([attention_weights1, input1]) # 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],)) # 定义attention权重 attention_weights2 = TimeDistributed(Dense(1))(input2) attention_weights2 = Activation('softmax')(attention_weights2) # 将attention权重应用于输入数据 context_vector2 = Dot(axes=(2, 1))([attention_weights2, input2]) merged_output = concatenate([context_vector1,context_vector2]) 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) 有没有错误

2. 对于 TimeDistributed(Dense(1))，可以考虑加入 kernel_regularizer 和 bias_regularizer 参数进行正则化，以防止过拟合。 3. 在使用 concatenate 合并两个输入时，可以加入 axis 参数明确指定合并的...

如何合在一起是不是应该这样：# 添加线性成分 res = Dense(trainY.shape[1])(lstm_out) res = add([res, pred]) # 使用sigmoid激活函数输出最终结果 res = Activation('sigmoid')(res) # 创建模型 model = Model(inputs=[input_data1, input_data2], outputs=res) # 创建GARCH模型 am = arch.arch_model(res, vol='GARCH', p=1, q=1) # 训练GARCH模型 res = am.fit() # 预测波动率 volatility = res.forecast(horizon=1).variance.iloc[-1].values # 将预测结果和波动率相乘，得到最终的预测结果 pred = model.predict([input_data1,input_data2]) pred = pred * np.sqrt(volatility) # 返回模型 return model, am

然后，使用Model类将输入和输出定义为input_data1、input_data2和res，并创建一个模型。接下来，使用arch.arch_model函数创建一个GARCH模型，将res作为参数传递给该函数。然后，使用fit方法训练GARCH模型并使用...

请修改如下代码： def LSTNetAttention(trainX1, trainX2, trainY, config): # 定义输入层 input_data1 = Input(shape=(trainX1.shape[1], trainX1.shape[2])) input_data2 = Input(shape=(trainX2.shape[1], trainX2.shape[2])) # 定义 LSTM 模型 ... # 使用 GARCH 模型预测波动率 am = arch.arch_model(trainY, vol='GARCH', p=1, q=1) res = am.fit() volatility = res.forecast(horizon=1).variance.iloc[-1].values # 将预测结果和波动率相乘，得到最终的预测结果 pred = model.predict([trainX1, trainX2]) pred = pred * np.sqrt(volatility) # 将预测结果作为一个新的特征加入到模型中 new_feature = pred.reshape(-1, 1) # 重塑成 (batch_size, 1) 的形状 concat = concatenate([lstm_out1, lstm_out2, new_feature]) res = Activation('sigmoid')(concat) # 定义模型并返回 model = Model(inputs=[input_data1, input_data2], outputs=res) return model

attention = Dense(units=1, activation='tanh')(res_out2) attention = Lambda(lambda x: np.exp(x) / np.sum(np.exp(x), axis=1, keepdims=True))(attention) attention = Multiply()([res_out1, attention]) ...

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simple LSTM.zip_LSTM_LSTM python_lstm python code_lstm训练_python

res1 = Dense(trainY1.shape[1])(lstm_out) 就等同于res1 = Dense(6)(lstm_out)

lstm_out = BatchNormalization()(lstm_out) lstm_out = Dense(32, activation='relu')(lstm_out) lstm_out = BatchNormalization()(lstm_out) lstm_out = Dense(16, activation='tanh')(lstm_out) res1 = Dense(trainY1.shape[1])(lstm_out) 这样可以吗，输出6个时间步的价格预测

下面是时序预测单步单维输出定义，如果改为单步多维输出， 需要修改模型定义吗 ： lstm_out = BatchNormalization()(lstm_out) lstm_out = Dense(16, activation='tanh')(lstm_out) res = Dense(trainY.shape[1])(lstm_out)

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下面是时序预测单步单维输出定义，如果改为单步多维输出，需要修改模型定义吗： lstm_out = BatchNormalization()(lstm_out) lstm_out = Dense(16, activation='tanh')(lstm_out) res = Dense(trainY.shape[1])(lstm_out)