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本文整理汇总了Python中keras.layers.Conv1D方法的典型用法代码示例。如果您正苦于以下问题：Python layers.Conv1D方法的具体用法？Python layers.Conv1D怎么用？Python layers.Conv1D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.Conv1D方法的29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_model# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Conv1D [as 别名]def create_model(time_window_size, metric): model = Sequential() model.add(Conv1D(filters=256, kernel_size=5, padding='same', activation='relu', input_shape=(time_window_size, 1))) model.add(MaxPooling1D(pool_size=4)) model.add(LSTM(64)) model.add(Dense(units=time_window_size, activation='linear')) model.compile(optimizer='adam', loss='mean_squared_error', metrics=[metric]) # model.compile(optimizer='adam', loss='mean_squared_error', metrics=[metric]) # model.compile(optimizer="sgd", loss="mse", metrics=[metric]) print(model.summary()) return model
开发者ID:chen0040，项目名称:keras-anomaly-detection，代码行数:20，代码来源:recurrent.py
示例2: CausalCNN# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Conv1D [as 别名]def CausalCNN(n_filters, lr, decay, loss, seq_len, input_features, strides_len, kernel_size, dilation_rates): inputs = Input(shape=(seq_len, input_features), name='input_layer') x=inputs for dilation_rate in dilation_rates: x = Conv1D(filters=n_filters, kernel_size=kernel_size, padding='causal', dilation_rate=dilation_rate, activation='linear')(x) x = BatchNormalization()(x) x = Activation('relu')(x) #x = Dense(7, activation='relu', name='dense_layer')(x) outputs = Dense(3, activation='sigmoid', name='output_layer')(x) causalcnn = Model(inputs, outputs=[outputs]) return causalcnn
开发者ID:BruceBinBoxing，项目名称:Deep_Learning_Weather_Forecasting，代码行数:23，代码来源:weather_model.py
示例3: create_model# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Conv1D [as 别名]def create_model(): inputs = Input(shape=(length,), dtype='int32', name='inputs') embedding_1 = Embedding(len(vocab), EMBED_DIM, input_length=length, mask_zero=True)(inputs) bilstm = Bidirectional(LSTM(EMBED_DIM // 2, return_sequences=True))(embedding_1) bilstm_dropout = Dropout(DROPOUT_RATE)(bilstm) embedding_2 = Embedding(len(vocab), EMBED_DIM, input_length=length)(inputs) con = Conv1D(filters=FILTERS, kernel_size=2 * HALF_WIN_SIZE + 1, padding='same')(embedding_2) con_d = Dropout(DROPOUT_RATE)(con) dense_con = TimeDistributed(Dense(DENSE_DIM))(con_d) rnn_cnn = concatenate([bilstm_dropout, dense_con], axis=2) dense = TimeDistributed(Dense(len(chunk_tags)))(rnn_cnn) crf = CRF(len(chunk_tags), sparse_target=True) crf_output = crf(dense) model = Model(input=[inputs], output=[crf_output]) model.compile(loss=crf.loss_function, optimizer=Adam(), metrics=[crf.accuracy]) return model
示例4: ann_model# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Conv1D [as 别名]def ann_model(input_shape): inp = Input(shape=input_shape, name='mfcc_in') model = inp model = Conv1D(filters=12, kernel_size=(3), activation='relu')(model) model = Conv1D(filters=12, kernel_size=(3), activation='relu')(model) model = Flatten()(model) model = Dense(56)(model) model = Activation('relu')(model) model = BatchNormalization()(model) model = Dropout(0.2)(model) model = Dense(28)(model) model = Activation('relu')(model) model = BatchNormalization()(model) model = Dense(1)(model) model = Activation('sigmoid')(model) model = Model(inp, model) return model
示例5: DiscriminatorConv# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Conv1D [as 别名]def DiscriminatorConv(V, E, filter_sizes, num_filters, dropout): ''' Another Discriminator model, currently unused because keras don't support masking for Conv1D and it does huge influence on training. # Arguments: V: int, Vocabrary size E: int, Embedding size filter_sizes: list of int, list of each Conv1D filter sizes num_filters: list of int, list of each Conv1D num of filters dropout: float # Returns: discriminator: keras model input: word ids, shape = (B, T) output: probability of true data or not, shape = (B, 1) ''' input = Input(shape=(None,), dtype='int32', name='Input') # (B, T) out = Embedding(V, E, name='Embedding')(input) # (B, T, E) out = VariousConv1D(out, filter_sizes, num_filters) out = Highway(out, num_layers=1) out = Dropout(dropout, name='Dropout')(out) out = Dense(1, activation='sigmoid', name='FC')(out) discriminator = Model(input, out) return discriminator
开发者ID:tyo-yo，项目名称:SeqGAN，代码行数:26，代码来源:models.py
示例6: VariousConv1D# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Conv1D [as 别名]def VariousConv1D(x, filter_sizes, num_filters, name_prefix=''): ''' Layer wrapper function for various filter sizes Conv1Ds # Arguments: x: tensor, shape = (B, T, E) filter_sizes: list of int, list of each Conv1D filter sizes num_filters: list of int, list of each Conv1D num of filters name_prefix: str, layer name prefix # Returns: out: tensor, shape = (B, sum(num_filters)) ''' conv_outputs = [] for filter_size, n_filter in zip(filter_sizes, num_filters): conv_name = '{}VariousConv1D/Conv1D/filter_size_{}'.format(name_prefix, filter_size) pooling_name = '{}VariousConv1D/MaxPooling/filter_size_{}'.format(name_prefix, filter_size) conv_out = Conv1D(n_filter, filter_size, name=conv_name)(x) # (B, time_steps, n_filter) conv_out = GlobalMaxPooling1D(name=pooling_name)(conv_out) # (B, n_filter) conv_outputs.append(conv_out) concatenate_name = '{}VariousConv1D/Concatenate'.format(name_prefix) out = Concatenate(name=concatenate_name)(conv_outputs) return out
开发者ID:tyo-yo，项目名称:SeqGAN，代码行数:23，代码来源:models.py
示例7: construct_model# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Conv1D [as 别名]def construct_model(classe_nums): model = Sequential() model.add( Conv1D(filters=256, kernel_size=3, strides=1, activation='relu', input_shape=(99, 40), name='block1_conv1')) model.add(MaxPool1D(pool_size=2, name='block1_pool1')) model.add(BatchNormalization(momentum=0.9, epsilon=1e-5, axis=1)) model.add(Conv1D(filters=256, kernel_size=3, strides=1, activation='relu', name='block1_conv2')) model.add(MaxPool1D(pool_size=2, name='block1_pool2')) model.add(Flatten(name='block1_flat1')) model.add(Dropout(0.5, name='block1_drop1')) model.add(Dense(512, activation='relu', name='block2_dense2')) model.add(MaxoutDense(512, nb_feature=4, name="block2_maxout2")) model.add(Dropout(0.5, name='block2_drop2')) model.add(Dense(512, activation='relu', name='block2_dense3', kernel_regularizer=l2(1e-4))) model.add(MaxoutDense(512, nb_feature=4, name="block2_maxout3")) model.add(Dense(classe_nums, activation='softmax', name="predict")) # plot_model(model, to_file='model_struct.png', show_shapes=True, show_layer_names=False) model.summary()
示例8: shortcut_pool# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Conv1D [as 别名]def shortcut_pool(inputs, output, filters=256, pool_type='max', shortcut=True): """ ResNet(shortcut连接|skip连接|residual连接), 这里是用shortcut连接. 恒等映射, block+f(block) 再加上 downsampling实现 参考: https://github.com/zonetrooper32/VDCNN/blob/keras_version/vdcnn.py :param inputs: tensor :param output: tensor :param filters: int :param pool_type: str, 'max'、'k-max' or 'conv' or other :param shortcut: boolean :return: tensor """ if shortcut: conv_2 = Conv1D(filters=filters, kernel_size=1, strides=2, padding='SAME')(inputs) conv_2 = BatchNormalization()(conv_2) output = downsampling(output, pool_type=pool_type) out = Add()([output, conv_2]) else: out = ReLU(inputs) out = downsampling(out, pool_type=pool_type) if pool_type is not None: # filters翻倍 out = Conv1D(filters=filters*2, kernel_size=1, strides=1, padding='SAME')(out) out = BatchNormalization()(out) return out
开发者ID:yongzhuo，项目名称:Keras-TextClassification，代码行数:27，代码来源:graph.py
示例9: downsampling# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Conv1D [as 别名]def downsampling(inputs, pool_type='max'): """ In addition, downsampling with stride 2 essentially doubles the effective coverage (i.e., coverage in the original document) of the convolution kernel; therefore, after going through downsampling L times, associations among words within a distance in the order of 2L can be represented. Thus, deep pyramid CNN is computationally ef
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