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自学教程:Python layers.CuDNNLSTM方法代码示例

51自学网 2020-12-01 11:09:10
  Keras
这篇教程Python layers.CuDNNLSTM方法代码示例写得很实用,希望能帮到您。

本文整理汇总了Python中keras.layers.CuDNNLSTM方法的典型用法代码示例。如果您正苦于以下问题:Python layers.CuDNNLSTM方法的具体用法?Python layers.CuDNNLSTM怎么用?Python layers.CuDNNLSTM使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在模块keras.layers的用法示例。

在下文中一共展示了layers.CuDNNLSTM方法的8个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于我们的系统推荐出更棒的Python代码示例。

示例1: create

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import CuDNNLSTM [as 别名]def create(inputtokens, vocabsize, units=16, dropout=0, embedding=32):        input_ = Input(shape=(inputtokens,), dtype='int32')        # Embedding layer        net = Embedding(input_dim=vocabsize, output_dim=embedding, input_length=inputtokens)(input_)        net = Dropout(dropout)(net)        # Bidirectional LSTM layer        net = BatchNormalization()(net)        net = Bidirectional(CuDNNLSTM(units))(net)        net = Dropout(dropout)(net)        # Output layer        net = Dense(vocabsize, activation='softmax')(net)        model = Model(inputs=input_, outputs=net)        # Make data-parallel        ngpus = len(get_available_gpus())        if ngpus > 1:            model = make_parallel(model, ngpus)        return model 
开发者ID:albarji,项目名称:neurowriter,代码行数:25,代码来源:models.py


示例2: nnet

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import CuDNNLSTM [as 别名]def nnet(inputs, keep_prob, num_classes):        """        # 适用于单导联的深度网络模型        :param inputs: keras tensor, 切片并堆叠后的单导联信号.        :param keep_prob: float, dropout-随机片段屏蔽概率.        :param num_classes: int, 目标类别数.        :return: keras tensor, 各类概率及全连接层前自动提取的特征.        """        branches = []        for i in range(int(inputs.shape[-1])):            ld = Lambda(Net.__slice, output_shape=(int(inputs.shape[1]), 1), arguments={'index': i})(inputs)            ld = Reshape((int(inputs.shape[1]), 1))(ld)            bch = Net.__backbone(ld)            branches.append(bch)        features = Concatenate(axis=1)(branches)        features = Dropout(keep_prob, [1, int(inputs.shape[-1]), 1])(features)        features = Bidirectional(CuDNNLSTM(1, return_sequences=True), merge_mode='concat')(features)        features = Flatten()(features)        net = Dense(units=num_classes, activation='softmax')(features)        return net, features 
开发者ID:Aiwiscal,项目名称:CPSC_Scheme,代码行数:22,代码来源:CPSC_model.py


示例3: build_and_compile_model

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import CuDNNLSTM [as 别名]def build_and_compile_model():    seq_input = Input(shape=(lookback_window, 1), name='seq_input', batch_shape=(1, lookback_window, 1))    x = CuDNNLSTM(LSTMunits, kernel_initializer='glorot_uniform', recurrent_initializer='glorot_uniform', return_sequences=True)(seq_input)    x = CuDNNLSTM(LSTMunits, kernel_initializer='glorot_uniform', recurrent_initializer='glorot_uniform', return_sequences=False)(x)    output_1 = Dense(1, activation='linear', name='output_1')(x)    weathernet = Model(inputs=seq_input, outputs=output_1)    weathernet.compile(optimizer=keras.optimizers.Adam(lr=1e-3), loss='mse')    weathernet.summary()    return weathernet#Load existing model 
开发者ID:produvia,项目名称:ai-platform,代码行数:14,代码来源:main.py


示例4: build_and_compile_model

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import CuDNNLSTM [as 别名]def build_and_compile_model():    seq_input = Input(shape=(lookback_window, 1), name='seq_input', batch_shape=(1, lookback_window, 1))    x = CuDNNLSTM(LSTMunits, kernel_initializer='glorot_uniform', recurrent_initializer='glorot_uniform', return_sequences=True)(seq_input)    x = CuDNNLSTM(LSTMunits, kernel_initializer='glorot_uniform', recurrent_initializer='glorot_uniform', return_sequences=False)(x)    output_1 = Dense(1, activation='linear', name='output_1')(x)    weathernet = Model(inputs=seq_input, outputs=output_1)    weathernet.compile(optimizer=keras.optimizers.Adam(lr=1e-3), loss='mse')    weathernet.summary()    return weathernet# Predict 
开发者ID:produvia,项目名称:ai-platform,代码行数:14,代码来源:train_weathernet.py


示例5: create_model

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import CuDNNLSTM [as 别名]def create_model(self, hyper_parameters):        """            构建神经网络        :param hyper_parameters:json,  hyper parameters of network        :return: tensor, moedl        """        super().create_model(hyper_parameters)        x = self.word_embedding.output        # x = Reshape((self.len_max, self.embed_size, 1))(embedding)        if self.rnn_type=="LSTM":                layer_cell = LSTM        elif self.rnn_type=="GRU":                layer_cell = GRU        elif self.rnn_type=="CuDNNLSTM":                layer_cell = CuDNNLSTM        elif self.rnn_type=="CuDNNGRU":                layer_cell = CuDNNGRU        else:            layer_cell = GRU        # Bi-LSTM        for nrl in range(self.num_rnn_layers):            x = Bidirectional(layer_cell(units=self.rnn_units,                                         return_sequences=True,                                         activation='relu',                                         kernel_regularizer=regularizers.l2(0.32 * 0.1),                                         recurrent_regularizer=regularizers.l2(0.32)                                         ))(x)            x = Dropout(self.dropout)(x)        x = Flatten()(x)        # 最后就是softmax        dense_layer = Dense(self.label, activation=self.activate_classify)(x)        output = [dense_layer]        self.model = Model(self.word_embedding.input, output)        self.model.summary(120) 
开发者ID:yongzhuo,项目名称:Keras-TextClassification,代码行数:37,代码来源:graph.py


示例6: lstm

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import CuDNNLSTM [as 别名]def lstm(maxlen, embed_size, recurrent_units, dropout_rate, recurrent_dropout_rate, dense_size, nb_classes):    #inp = Input(shape=(maxlen, ))    input_layer = Input(shape=(maxlen, embed_size), )    #x = Embedding(max_features, embed_size, weights=[embedding_matrix],    #              trainable=False)(inp)    x = LSTM(recurrent_units, return_sequences=True, dropout=dropout_rate,                           recurrent_dropout=dropout_rate)(input_layer)    #x = CuDNNLSTM(recurrent_units, return_sequences=True)(x)    x = Dropout(dropout_rate)(x)    x_a = GlobalMaxPool1D()(x)    x_b = GlobalAveragePooling1D()(x)    #x_c = AttentionWeightedAverage()(x)    #x_a = MaxPooling1D(pool_size=2)(x)    #x_b = AveragePooling1D(pool_size=2)(x)    x = concatenate([x_a,x_b])    x = Dense(dense_size, activation="relu")(x)    x = Dropout(dropout_rate)(x)    x = Dense(nb_classes, activation="sigmoid")(x)    model = Model(inputs=input_layer, outputs=x)    model.summary()    model.compile(loss='binary_crossentropy',                 optimizer='adam',                 metrics=['accuracy'])    return model# bidirectional LSTM 
开发者ID:kermitt2,项目名称:delft,代码行数:29,代码来源:models.py


示例7: create_model

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import CuDNNLSTM [as 别名]def create_model(self, hyper_parameters):        """            构建神经网络        :param hyper_parameters:json,  hyper parameters of network        :return: tensor, moedl        """        super().create_model(hyper_parameters)        x = self.word_embedding.output        embedding_output_spatial = SpatialDropout1D(self.dropout_spatial)(x)        if self.rnn_units=="LSTM":                layer_cell = LSTM        elif self.rnn_units=="GRU":                layer_cell = GRU        elif self.rnn_units=="CuDNNLSTM":                layer_cell = CuDNNLSTM        elif self.rnn_units=="CuDNNGRU":                layer_cell = CuDNNGRU        else:            layer_cell = GRU        # CNN        convs = []        for kernel_size in self.filters:            conv = Conv1D(self.filters_num,                            kernel_size=kernel_size,                            strides=1,                            padding='SAME',                            kernel_regularizer=regularizers.l2(self.l2),                            bias_regularizer=regularizers.l2(self.l2),                            )(embedding_output_spatial)            convs.append(conv)        x = Concatenate(axis=1)(convs)        # Bi-LSTM, 论文中使用的是LSTM        x = Bidirectional(layer_cell(units=self.rnn_units,                                     return_sequences=True,                                     activation='relu',                                     kernel_regularizer=regularizers.l2(self.l2),                                     recurrent_regularizer=regularizers.l2(self.l2)                                     ))(x)        x = Dropout(self.dropout)(x)        x = Flatten()(x)        # 最后就是softmax        dense_layer = Dense(self.label, activation=self.activate_classify)(x)        output = [dense_layer]        self.model = Model(self.word_embedding.input, output)        self.model.summary(120) 
开发者ID:yongzhuo,项目名称:Keras-TextClassification,代码行数:48,代码来源:graph.py


示例8: create_model

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import CuDNNLSTM [as 别名]def create_model(self, hyper_parameters):        """            构建神经网络, a bit like RCNN, R        :param hyper_parameters:json,  hyper parameters of network        :return: tensor, moedl        """        super().create_model(hyper_parameters)        x = self.word_embedding.output        x = Activation('tanh')(x)        # entire embedding channels are dropped out instead of the        # normal Keras embedding dropout, which drops all channels for entire words        # many of the datasets contain so few words that losing one or more words can alter the emotions completely        x = SpatialDropout1D(self.dropout_spatial)(x)        if self.rnn_units=="LSTM":                layer_cell = LSTM        elif self.rnn_units=="GRU":                layer_cell = GRU        elif self.rnn_units=="CuDNNLSTM":                layer_cell = CuDNNLSTM        elif self.rnn_units=="CuDNNGRU":                layer_cell = CuDNNGRU        else:            layer_cell = GRU        # skip-connection from embedding to output eases gradient-flow and allows access to lower-level features        # ordering of the way the merge is done is important for consistency with the pretrained model        lstm_0_output = Bidirectional(layer_cell(units=self.rnn_units,                                                 return_sequences=True,                                                 activation='relu',                                                 kernel_regularizer=regularizers.l2(self.l2),                                                 recurrent_regularizer=regularizers.l2(self.l2)                                                 ), name="bi_lstm_0")(x)        lstm_1_output = Bidirectional(layer_cell(units=self.rnn_units,                                                 return_sequences=True,                                                 activation='relu',                                                 kernel_regularizer=regularizers.l2(self.l2),                                                 recurrent_regularizer=regularizers.l2(self.l2)                                                 ), name="bi_lstm_1")(lstm_0_output)        x = concatenate([lstm_1_output, lstm_0_output, x])        # if return_attention is True in AttentionWeightedAverage, an additional tensor        # representing the weight at each timestep is returned        weights = None        x = AttentionWeightedAverage(name='attlayer', return_attention=self.return_attention)(x)        if self.return_attention:            x, weights = x        x = Dropout(self.dropout)(x)        # x = Flatten()(x)        # 最后就是softmax        dense_layer = Dense(self.label, activation=self.activate_classify)(x)        output = [dense_layer]        self.model = Model(self.word_embedding.input, output)        self.model.summary(120) 
开发者ID:yongzhuo,项目名称:Keras-TextClassification,代码行数:59,代码来源:graph.py


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