LSTM及其衍生网络代码

from keras.layers.core import * from keras.layers.recurrent import LSTM from keras.models import * from keras.layers import merge, TimeDistributed, Conv1D, Bidirectional class Generate_model(): def __init__(self): self.model = Sequential() def generate_lstm_model(self,n_input, n_out, n_features): self.model = Sequential() self.model.add(LSTM(64, activation='relu', input_shape=(n_input, n_features))) self.model.add(Dropout(0.1)) self.model.add(Dense(n_out)) self.model.summary() # 模型编译 self.model.compile(loss="mse", optimizer='adam') return self.model def generate_seq2seq_model(self,n_input, n_out, n_features): self.model = Sequential() self.model.add(LSTM(128,input_shape=(n_input, n_features))) self.model.add(Dense(10, activation="relu")) # 使用 "RepeatVector" 将 Encoder 的输出(最后一个 time step)复制 N 份作为 Decoder 的 N 次输入 self.model.add(RepeatVector(1))#此为步长 # Decoder(第二个 LSTM) self.model.add(LSTM(128,return_sequences=True)) # TimeDistributed 是为了保证 Dense 和 Decoder 之间的一致 self.model.add(TimeDistributed(Dense(units=n_out, activation="linear"))) self.model.add(Flatten())#扁平层将（None,1,8)变为（None,1*8) self.model.summary() self.model.compile(loss="mse", optimizer='adam') return self.model # ------------------------------------------------------------------------------------------------------# # 注意力模块，主要是实现对step维度的注意力机制 # 在这里大家可能会疑惑，为什么需要先Permute再进行注意力机制的施加。 # 这是因为，如果我们直接进行全连接的话，我们的最后一维是特征维度，这个时候，我们每个step的特征是分开的， # 此时进行全连接的话，得出来注意力权值每一个step之间是不存在特征交换的，自然也就不准确了。 # 所以在这里我们需要首先将step维度转到最后一维，然后再进行全连接，根据每一个step的特征获得注意力机制的权值。 def attention_block(self,inputs,time_step): # batch_size, time_steps, lstm_units -> batch_size, lstm_units, time_steps a = Permute((2, 1))(inputs) # batch_size, lstm_units, time_steps -> batch_size, lstm_units, time_steps a = Dense(time_step, activation='softmax')(a)#和步长有关 # batch_size, lstm_units, time_steps -> batch_size, time_steps, lstm_units a_probs = Permute((2, 1), name='attention_vec')(a) # 相当于获得每一个step中，每个特征的权重 output_attention_mul = merge([inputs, a_probs], name='attention_mul', mode='mul') return output_attention_mul def generate_attention_model(self, n_input, n_out, n_features): inputs = Input(shape=(n_input, n_features,)) # (batch_size, time_steps, input_dim) -> (batch_size, input_dim, lstm_units) lstm_out = LSTM(128, return_sequences=True)(inputs) attention_mul = self.attention_block(lstm_out,n_input) # (batch_size, input_dim, lstm_units) -> (batch_size, input_dim*lstm_units) dropout=Dropout(0.8)(attention_mul) flatten = Flatten()(dropout) output = Dense(n_out, activation='sigmoid')(flatten) model = Model(inputs=[inputs], outputs=output) model.summary() model.compile(loss="mse", optimizer='adam') return model def generate_seq2seq_attention_model(self, n_input, n_out, n_features): inputs = Input(shape=(n_input, n_features,)) lstm_out1 = LSTM(128, return_sequences=True)(inputs) attention_mul = self.attention_block(lstm_out1, n_input) # (batch_size, input_dim, lstm_units) -> (batch_size, input_dim*lstm_units) attention_mul = Flatten()(attention_mul) output1 = Dense(n_out, activation='sigmoid')(attention_mul) repeatVector=RepeatVector(1)(output1) lstm_out2 = LSTM(128, return_sequences=True)(repeatVector) output2=TimeDistributed(Dense(n_out))(lstm_out2) flatten=Flatten()(output2) model = Model(inputs=[inputs], outputs=flatten) model.summary() model.compile(loss="mse", optimizer='adam') return model def cnn_lstm_attention_model(self, n_input, n_out, n_features): inputs = Input(shape=(n_input, n_features)) x = Conv1D(filters=64, kernel_size=1, activation='relu')(inputs) # , padding = 'same' x = Dropout(0.3)(x) # lstm_out = Bidirectional(LSTM(lstm_units, activation='relu'), name='bilstm')(x) # 对于GPU可以使用CuDNNLSTM lstm_out = Bidirectional(LSTM(128, return_sequences=True))(x) lstm_out = Dropout(0.3)(lstm_out) attention_mul = self.attention_block(lstm_out, n_input) attention_mul = Flatten()(attention_mul) output = Dense(n_out, activation='sigmoid')(attention_mul) model = Model(inputs=[inputs], outputs=output) model.summary() model.compile(loss="mse", optimizer='adam') return model