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本文整理汇总了Python中keras.layers.GlobalAveragePooling1D方法的典型用法代码示例。如果您正苦于以下问题：Python layers.GlobalAveragePooling1D方法的具体用法？Python layers.GlobalAveragePooling1D怎么用？Python layers.GlobalAveragePooling1D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.GlobalAveragePooling1D方法的27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: fasttext_model# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import GlobalAveragePooling1D [as 别名]def fasttext_model(max_len=300, vocabulary_size=20000, embedding_dim=128, num_classes=4): model = Sequential() # embed layer by maps vocab index into emb dimensions model.add(Embedding(input_dim=vocabulary_size, output_dim=embedding_dim, input_length=max_len)) # pooling the embedding model.add(GlobalAveragePooling1D()) # output multi classification of num_classes model.add(Dense(num_classes, activation='softmax')) model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) model.summary() return model
示例2: bidLstm_simple# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import GlobalAveragePooling1D [as 别名]def bidLstm_simple(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 = Bidirectional(LSTM(recurrent_units, return_sequences=True, dropout=dropout_rate, recurrent_dropout=dropout_rate))(input_layer) 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 with attention layer
开发者ID:kermitt2，项目名称:delft，代码行数:27，代码来源:models.py
示例3: __init__# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import GlobalAveragePooling1D [as 别名]def __init__(self, nb_classes, nb_tokens, maxlen, nb_head=8, head_size=16, nb_transformer=2, embedding_dim=256, embeddings=None, embed_l2=1E-6, pos_embed=False, final_dropout_rate=0.15, embed_dropout_rate=0.15): self.nb_classes = nb_classes self.nb_tokens = nb_tokens self.maxlen = maxlen self.nb_head = nb_head self.head_size = head_size self.embedding_dim = embedding_dim self.nb_transformer = nb_transformer if embeddings is not None: self.token_embeddings = [embeddings] else: self.token_embeddings = None self.pos_embed = pos_embed self.final_dropout_rate = final_dropout_rate self.embed_dropout_rate = embed_dropout_rate self.pos_embed_layer = Position_Embedding(name='position_embedding') self.transformers = [Self_Attention( nb_head, head_size, name='self_attention_%d' % i) for i in range(nb_transformer)] self.pool = GlobalAveragePooling1D() self.invalid_params = {'pos_embed_layer', 'transformers', 'pool'}
示例4: cnn_spatial_multi# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import GlobalAveragePooling1D [as 别名]def cnn_spatial_multi(self): # spatial stream (frozen) cnn_spatial = self.cnn_spatial() if self.saved_spatial_weights is None: print("[ERROR] No saved_spatial_weights weights file!") else: cnn_spatial.load_weights(self.saved_spatial_weights) for layer in cnn_spatial.layers: layer.trainable = False # building inputs and output model = Sequential() model.add(TimeDistributed((cnn_spatial), input_shape=self.input_shape_spatial_multi)) model.add(GlobalAveragePooling1D()) return model # CNN model for the temporal stream with multiple inputs
示例5: cnn_temporal_multi# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import GlobalAveragePooling1D [as 别名]def cnn_temporal_multi(self): # spatial stream (frozen) cnn_temporal = self.cnn_temporal() if self.saved_temporal_weights is None: print("[ERROR] No saved_temporal_weights weights file!") else: cnn_temporal.load_weights(self.saved_temporal_weights) for layer in cnn_temporal.layers: layer.trainable = False # building inputs and output model = Sequential() model.add(TimeDistributed((cnn_temporal), input_shape=self.input_shape_temporal_multi)) model.add(GlobalAveragePooling1D()) return model # CNN model for the spatial stream
示例6: create_model# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import GlobalAveragePooling1D [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) embedding = self.word_embedding.output def win_mean(x): res_list = [] for i in range(self.len_max-self.n_win+1): x_mean = tf.reduce_mean(x[:, i:i + self.n_win, :], axis=1) x_mean_dims = tf.expand_dims(x_mean, axis=-1) res_list.append(x_mean_dims) res_list = tf.concat(res_list, axis=-1) gg = tf.reduce_max(res_list, axis=-1) return gg if self.encode_type=="HIERARCHICAL": x = Lambda(win_mean, output_shape=(self.embed_size, ))(embedding) elif self.encode_type=="MAX": x = GlobalMaxPooling1D()(embedding) elif self.encode_type=="AVG": x = GlobalAveragePooling1D()(embedding) elif self.encode_type == "CONCAT": x_max = GlobalMaxPooling1D()(embedding) x_avg = GlobalAveragePooling1D()(embedding) x = Concatenate()([x_max, x_avg]) else: raise RuntimeError("encode_type must be 'MAX', 'AVG', 'CONCAT', 'HIERARCHICAL'") output = Dense(self.label, activation=self.activate_classify)(x) self.model = Model(inputs=self.word_embedding.input, outputs=output) self.model.summary(132)
开发者ID:yongzhuo，项目名称:Keras-TextClassification，代码行数:37，代码来源:graph.py
示例7: lstm# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import GlobalAveragePooling1D [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8: cnn3# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import GlobalAveragePooling1D [as 别名]def cnn3(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 = GRU(recurrent_units, return_sequences=True, dropout=dropout_rate, recurrent_dropout=dropout_rate)(input_layer) #x = Dropout(dropout_rate)(x) x = Conv1D(filters=recurrent_units, kernel_size=2, padding='same', activation='relu')(x) x = MaxPooling1D(pool_size=2)(x) x = Conv1D(filters=recurrent_units, kernel_size=2, padding='same', activation='relu')(x) x = MaxPooling1D(pool_size=2)(x) x = Conv1D(filters=recurrent_units, kernel_size=2, padding='same', activation='relu')(x) x = MaxPooling1D(pool_size=2)(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 = Dropout(dropout_rate)(x) x = Dense(dense_size, activation="relu")(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
开发者ID:kermitt2，项目名称:delft，代码行数:29，代码来源:models.py
示例9: gru# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import GlobalAveragePooling1D [as 别名]def gru(maxlen, embed_size, recurrent_units, dropout_rate, recurrent_dropout_rate, dense_size, nb_classes): #input_layer = Input(shape=(maxlen,)) input_layer = Input(shape=(maxlen, embed_size), ) #embedding_layer = Embedding(max_features, embed_size, # weights=[embedding_matrix], trainable=False)(input_layer) x = Bidirectional(GRU(recurrent_units, return_sequences=True, dropout=dropout_rate, recurrent_dropout=recurrent_dropout_rate))(input_layer) x = Dropout(dropout_rate)(x) x = Bidirectional(GRU(recurrent_units, return_sequences=True, dropout=dropout_rate, recurrent_dropout=recurrent_dropout_rate))(x) #x = AttentionWeightedAverage(maxlen)(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], axis=1) #x = Dense(dense_size, activation="relu")(x) #x = Dropout(dropout_rate)(x) x = Dense(dense_size, activation="relu")(x) output_layer = Dense(nb_classes, activation="sigmoid")(x) model = Model(inputs=input_layer, outputs=output_layer) model.summary() model.compile(loss='binary_crossentropy', optimizer=RMSprop(clipvalue=1, clipnorm=1), #optimizer='adam', metrics=['accuracy']) return model
开发者ID:kermitt2，项目名称:delft，代码行数:31，代码来源:models.py
示例10: gru_simple# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import GlobalAveragePooling1D [as 别名]def gru_simple(maxlen, embed_size, recurrent_units, dropout_rate, recurrent_dropout_rate, dense_size, nb_classes): #input_layer = Input(shape=(maxlen,)) input_layer = Input(shape=(maxlen, embed_size), ) #embedding_layer = Embedding(max_features, embed_size, # weights=[embedding_matrix], trainable=False)(input_layer) x = Bidirectional(GRU(recurrent_units, return_sequences=True, dropout=dropout_rate, recurrent_dropout=dropout_rate))(input_layer) #x = AttentionWeightedAverage(maxlen)(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], axis=1) #x = Dense(dense_size, activation="relu")(x) #x = Dropout(dropout_rate)(x) x = Dense(dense_size, activation="relu")(x) output_layer = Dense(nb_classes, activation="sigmoid")(x) model = Model(inputs=input_layer, outputs=output_layer) model.summary() model.compile(loss='binary_crossentropy', optimizer=RMSprop(clipvalue=1, clipnorm=1), #optimizer='adam', metrics=['accuracy']) return model# bid GRU + bid LSTM
开发者ID:kermitt2，项目名称:delft，代码行数:31，代码来源:models.py
示例11: mix1# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import GlobalAveragePooling1D [as 别名]def mix1(maxlen, embed_size, recurrent_units, dropout_rate, recurrent_dropout_rate, dense_size, nb_classes): #input_layer = Input(shape=(maxlen,)) input_layer = Input(shape=(maxlen, embed_size), ) #embedding_layer = Embedding(max_features, embed_size, # weights=[embedding_matrix], trainable=False)(input_layer) x = Bidirectional(GRU(recurrent_units, return_sequences=True, dropout=dropout_rate, recurrent_dropout=recurrent_dropout_rate))(input_layer) x = Dropout(dropout_rate)(x) x = Bidirectional(LSTM(recurrent_units, return_sequences=True, dropout=dropout_rate, recurrent_dropout=recurrent_dropout_rate))(x) x_a = GlobalMaxPool1D()(x) x_b = GlobalAveragePooling1D()(x) x = concatenate([x_a,x_b]) x = Dense(dense_size, activation="relu")(x) output_layer = Dense(nb_classes, activation="sigmoid")(x) model = Model(inputs=input_layer, outputs=output_layer) model.summary() model.compile(loss='binary_crossentropy', optimizer=RMSprop(clipvalue=1, clipnorm=1), #optimizer='adam', metrics=['accuracy']) return model# DPCNN
开发者ID:kermitt2，项目名称:delft，代码行数:30，代码来源:models.py
示例12: build_model_avt_cnn# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import GlobalAveragePooling1D [as 别名]def build_model_avt_cnn(self): #########text-cnn######### # bert embedding bert_inputs, bert_output = KerasBertEmbedding().bert_encode() # text cnn bert_output_emmbed = SpatialDropout1D(rate=self.keep_prob)(bert_output) concat_x = [] concat_y = [] concat_z = [] for index, filter_size in enumerate(self.filters): conv = Conv1D(name='TextCNN_Conv1D_{}'.format(index), filters=int(self.embedding_dim/2), kernel_size=self.filters[index], padding='valid', kernel_initializer='normal', activation='relu')(bert_output_emmbed) x = GlobalMaxPooling1D(name='TextCNN_MaxPooling1D_{}'.format(index))(conv) y = GlobalAveragePooling1D(name='TextCNN_AveragePooling1D_{}'.format(index))(conv) z = AttentionWeightedAverage(name='TextCNN_Annention_{}'.format(index))(conv) concat_x.append(x) concat_y.append(y) concat_z.append(z) merge_x = Concatenate(axis=1)(concat_x) merge_y = Concatenate(axis=1)(concat_y) merge_z = Concatenate(axis=1)(concat_z) merge_xyz = Concatenate(axis=1)([merge_x, merge_y, merge_z]) x = Dropout(self.keep_prob)(merge_xyz) # 最后就是softmax dense_layer = Dense(self.label, activation=self.activation)(x) output_layers = [dense_layer] self.model = Model(bert_inputs, output_layers)
示例13: Archi_3CONV64C_1FC256_GAP_f3fd# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import GlobalAveragePooling1D [as 别名]def Archi_3CONV64C_1FC256_GAP_f3fd(X, nbclasses): #-- get the input sizes m, L, depth = X.shape input_shape = (L,depth) #-- parameters of the architecture l2_rate = 1.e-6 dropout_rate = 0.5 nb_conv = 3 nb_fc= 1 nbunits_conv = 640 #-- will be double nbunits_fc = 256 #-- will be double # Define the input placeholder. X_input = Input(input_shape) #-- nb_conv CONV layers X = X_input for add in range(nb_conv): X = conv_bn_relu_drop(X, nbunits=nbunits_conv, kernel_size=3, kernel_regularizer=l2(l2_rate), dropout_rate=dropout_rate) #-- Flatten + 1 FC layers X = GlobalAveragePooling1D()(X) for add in range(nb_fc): X = fc_bn_relu_drop(X, nbunits=nbunits_fc, kernel_regularizer=l2(l2_rate), dropout_rate=dropout_rate) #-- SOFTMAX layer out = softmax(X, nbclasses, kernel_regularizer=l2(l2_rate)) # Create model. return Model(inputs = X_input, outputs = out, name='Archi_3CONV64C_1FC256_GAP_f3fd') #-----------------------------------------------------------------------
示例14: Archi_3CONV64C_1FC256_GAP_f5fd# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import GlobalAveragePooling1D [as 别名]def Archi_3CONV64C_1FC256_GAP_f5fd(X, nbclasses): #-- get the input sizes m, L, depth = X.shape input_shape = (L,depth) #-- parameters of the architecture l2_rate = 1.e-6 dropout_rate = 0.5 nb_conv = 3 nb_fc= 1 nbunits_conv = 512 #-- will be double nbunits_fc = 256 #-- will be double # Define the input placeholder. X_input = Input(input_shape) #-- nb_conv CONV layers X = X_input for add in range(nb_conv): X = conv_bn_relu_drop(X, nbunits=nbunits_conv, kernel_size=5, kernel_regularizer=l2(l2_rate), dropout_rate=dropout_rate) #-- Flatten + 1 FC layers X = GlobalAveragePooling1D()(X) for add in range(nb_fc): X = fc_bn_relu_drop(X, nbunits=nbunits_fc, kernel_regularizer=l2(l2_rate), dropout_rate=dropout_rate) #-- SOFTMAX layer out = softmax(X, nbclasses, kernel_regularizer=l2(l2_rate)) # Create model. return Model(inputs = X_input, outputs = out, name='Archi_3CONV64C_1FC256_GAP_f5fd') #-----------------------------------------------------------------------
示例15: Archi_3CONV64C_1FC256_GAP_f9fd# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import GlobalAveragePooling1D [as 别名]def Archi_3CONV64C_1FC256_GAP_f9fd(X, nbclasses): #-- get the input sizes m, L, depth = X.shape input_shape = (L,depth) #-- parameters of the architecture l2_rate = 1.e-6 dropout_rate = 0.5 nb_conv = 3 nb_fc= 1 nbunits_conv = 384 #-- will be double nbunits_fc = 256 #-- will be double # Define the input placeholder. X_input = Input(input_shape) #-- nb_conv CONV layers X = X_input for add in range(nb_conv): X = conv_bn_relu_drop(X, nbunits=nbunits_conv, kernel_size=9, kernel_regularizer=l2(l2_rate), dropout_rate=dropout_rate) #-- Flatten + 1 FC layers X = GlobalAveragePooling1D()(X) for add in range(nb_fc): X = fc_bn_relu_drop(X, nbunits=nbunits_fc, kernel_regularizer=l2(l2_rate), dropout_rate=dropout_rate) #-- SOFTMAX layer out = softmax(X, nbclasses, kernel_regularizer=l2(l2_rate)) # Create model. return Model(inputs = X_input, outputs = out, name='Archi_3CONV64C_1FC256_GAP_f9fd') #-----------------------------------------------------------------------
示例16: Archi_3CONV64C_1FC256_GAP_f17fd# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import GlobalAveragePooling1D [as 别名]def Archi_3CONV64C_1FC256_GAP_f17fd(X, nbclasses): #-- get the input sizes m, L, depth = X.shape input_shape = (L,depth) #-- parameters of the architecture l2_rate = 1.e-6 dropout_rate = 0.5 nb_conv = 3 nb_fc= 1 nbunits_conv = 256 #-- will be double nbunits_fc = 256 #-- will be double # Define the input placeholder. X_input = Input(input_shape) #-- nb_conv CONV layers X = X_input for add in range(nb_conv): X = conv_bn_relu_drop(X, nbunits=nbunits_conv, kernel_size=17, kernel_regularizer=l2(l2_rate), dropout_rate=dropout_rate) #-- Flatten + 1 FC layers X = GlobalAveragePooling1D()(X) for add in range(nb_fc): X = fc_bn_relu_drop(X, nbunits=nbunits_fc, kernel_regularizer=l2(l2_rate), dropout_rate=dropout_rate) #-- SOFTMAX layer out = softmax(X, nbclasses, kernel_regularizer=l2(l2_rate)) # Create model. return Model(inputs = X_input, outputs = out, name='Archi_3CONV64C_1FC256_GAP_f17fd') #-----------------------------------------------------------------------
示例17: Archi_3CONV64C_1FC256_GAP_f33fd# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import GlobalAveragePooling1D [as 别名]def Archi_3CONV64C_1FC256_GAP_f33fd(X, nbclasses): #-- get the input sizes m, L, depth = X.shape input_shape = (L,depth) #-- parameters of the architecture l2_rate = 1.e-6 dropout_rate = 0.5 nb_conv = 3 nb_fc= 1 nbunits_conv = 192 #-- will be double nbunits_fc = 256 #-- will be double # Define the input placeholder. X_input = Input(input_shape) #-- nb_conv CONV layers X = X_input for add in range(nb_conv): X = conv_bn_relu_drop(X, nbunits=nbunits_conv, kernel_size=33, kernel_regularizer=l2(l2_rate), dropout_rate=dropout_rate) #-- Flatten + 1 FC layers X = GlobalAveragePooling1D()(X) for add in range(nb_fc): X = fc_bn_relu_drop(X, nbunits=nbunits_fc, kernel_regularizer=l2(l2_rate), dropout_rate=dropout_rate) #-- SOFTMAX layer out = softmax(X, nbclasses, kernel_regularizer=l2(l2_rate)) # Create model. return Model(inputs = X_input, outputs = out, name='Archi_3CONV64C_1FC256_GAP_f33fd') #-----------------------------------------------------------------------
示例18: Archi_3CONV2MP_1FC256_GAP_f17_9_5fd# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import GlobalAveragePooling1D [as 别名]def Archi_3CONV2MP_1FC256_GAP_f17_9_5fd(X, nbclasses): #-- get the input sizes m, L, depth = X.shape input_shape = (L,depth) #-- parameters of the architecture l2_rate = 1.e-6 dropout_rate = 0.5 nb_conv = 3 nb_fc= 1 nbunits_fc = 256 #-- will be double # Define the input placeholder. X_input = Input(input_shape) #-- nb_conv CONV layers X = conv_bn_relu(X_input, nbunits=256, kernel_size=17, kernel_regularizer=l2(l2_rate), padding='same') X = MaxPooling1D(pool_size=2, strides=2, padding='valid')(X) X = Dropout(dropout_rate)(X) X = conv_bn_relu(X, nbunits=512, kernel_size=9, kernel_regularizer=l2(l2_rate), padding='same') X = MaxPooling1D(pool_size=2, strides=2, padding='valid')(X) X = Dropout(dropout_rate)(X) X = conv_bn_relu(X, nbunits=512, kernel_size=5, kernel_regularizer=l2(l2_rate), padding='same') X = MaxPooling1D(pool_size=2, strides=2, padding='valid')(X) X = Dropout(dropout_rate)(X) #-- Flatten + 1 FC layers X = GlobalAveragePooling1D()(X) for add in range(nb_fc): X = fc_bn_relu_drop(X, nbunits=nbunits_fc, kernel_regularizer=l2(l2_rate), dropout_rate=dropout_rate) #-- SOFTMAX layer out = softmax(X, nbclasses, kernel_regularizer=l2(l2_rate)) # Create model. return Model(inputs = X_input, outputs = out, name='Archi_3CONV2MP_1FC256_GAP_f17_9_5fd') #-----------------------------------------------------------------------
示例19: Archi_3CONV2MP_1FC256_GAP_f9_5_3fd# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import GlobalAveragePooling1D [as 别名]def Archi_3CONV2MP_1FC256_GAP_f9_5_3fd(X, nbclasses): #-- get the input sizes m, L, depth = X.shape input_shape = (L,depth) #-- parameters of the architecture l2_rate = 1.e-6 dropout_rate = 0.5 nb_conv = 3 nb_fc= 1 nbunits_fc = 256 #-- will be double # Define the input placeholder. X_input = Input(input_shape) #-- nb_conv CONV layers X = conv_bn_relu(X_input, nbunits=512, kernel_size=9, kernel_regularizer=l2(l2_rate), padding='same') X = MaxPooling1D(pool_size=2, strides=2, padding='valid')(X) X = Dropout(dropout_rate)(X) X = conv_bn_relu(X, nbunits=512, kernel_size=5, kernel_regularizer=l2(l2_rate), padding='same') X = MaxPooling1D(pool_size=2, strides=2, padding='valid')(X) X = Dropout(dropout_rate)(X) X = conv_bn_relu(X, nbunits=512, kernel_size=3, kernel_regularizer=l2(l2_rate), padding='same') X = MaxPooling1D(pool_size=2, strides=2, padding='valid')(X) X = Dropout(dropout_rate)(X) #-- Flatten + 1 FC layers X = GlobalAveragePooling1D()(X) for add in range(nb_fc): X = fc_bn_relu_drop(X, nbunits=nbunits_fc, kernel_regularizer=l2(l2_rate), dropout_rate=dropout_rate) #-- SOFTMAX layer out = softmax(X, nbclasses, kernel_regularizer=l2(l2_rate)) # Create model. return Model(inputs = X_input, outputs = out, name='Archi_3CONV2MP_1FC256_GAP_f9_5_3fd') #-----------------------------------------------------------------------
示例20: Archi_3CONV2MP_1FC256_GAP_f5_3_1fd# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import GlobalAveragePooling1D [as 别名]def Archi_3CONV2MP_1FC256_GAP_f5_3_1fd(X, nbclasses): #-- get the input sizes m, L, depth = X.shape input_shape = (L,depth) #-- parameters of the architecture l2_rate = 1.e-6 dropout_rate = 0.5 nb_conv = 3 nb_fc= 1 nbunits_fc = 256 #-- will be double # Define the input placeholder. X_input = Input(input_shape) #-- nb_conv CONV layers X = conv_bn_relu(X_input, nbunits=512, kernel_size=5, kernel_regularizer=l2(l2_rate), padding='same') X = MaxPooling1D(pool_size=2, strides=2, padding='valid')(X) X = Dropout(dropout_rate)(X) X = conv_bn_relu(X, nbunits=768, kernel_size=3, kernel_regularizer=l2(l2_rate), padding='same') X = MaxPooling1D(pool_size=2, strides=2, padding='valid')(X) X = Dropout(dropout_rate)(X) X = conv_bn_relu(X, nbunits=1024, kernel_size=1, kernel_regularizer=l2(l2_rate), padding='same') #~ X = MaxPooling1D(pool_size=2, strides=2, padding='valid')(X) X = Dropout(dropout_rate)(X) #-- Flatten + 1 FC layers X = GlobalAveragePooling1D()(X) for add in range(nb_fc): X = fc_bn_relu_drop(X, nbunits=nbunits_fc, kernel_regularizer=l2(l2_rate), dropout_rate=dropout_rate) #-- SOFTMAX layer out = softmax(X, nbclasses, kernel_regularizer=l2(l2_rate)) # Create model. return Model(inputs = X_input, outputs = out, name='Archi_3CONV2MP_1FC256_GAP_f5_3_1fd') #-----------------------------------------------------------------------
示例21: Archi_3CONV2MP_1FC256_GAP_f3_1_1fd# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import GlobalAveragePooling1D [as 别名]def Archi_3CONV2MP_1FC256_GAP_f3_1_1fd(X, nbclasses): #-- get the input sizes m, L, depth = X.shape input_shape = (L,depth) #-- parameters of the architecture l2_rate = 1.e-6 dropout_rate = 0.5 nb_conv = 3 nb_fc= 1 nbunits_fc = 256 #-- will be double # Define the input placeholder. X_input = Input(input_shape) #-- nb_conv CONV layers X = conv_bn_relu(X_input, nbunits=768, kernel_size=3, kernel_regularizer=l2(l2_rate), padding='same') X = MaxPooling1D(pool_size=2, strides=2, padding='valid')(X) X = Dropout(dropout_rate)(X) X = conv_bn_relu(X, nbunits=1024, kernel_size=1, kernel_regularizer=l2(l2_rate), padding='same') X = Dropout(dropout_rate)(X) X = conv_bn_relu(X, nbunits=1024, kernel_size=1, kernel_regularizer=l2(l2_rate), padding='same') X = Dropout(dropout_rate)(X) #-- Flatten + 1 FC layers X = GlobalAveragePooling1D()(X) for add in range(nb_fc): X = fc_bn_relu_drop(X, nbunits=nbunits_fc, kernel_regularizer=l2(l2_rate), dropout_rate=dropout_rate) #-- SOFTMAX layer out = softmax(X, nbclasses, kernel_regularizer=l2(l2_rate)) # Create model. return Model(inputs = X_input, outputs = out, name='Archi_3CONV2MP_1FC256_GAP_f3_1_1fd') #-----------------------------------------------------------------------
示例22: Archi_3CONV2AP_1FC256_GAP_f33_17_9fd# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import GlobalAveragePooling1D [as 别名]def Archi_3CONV2AP_1FC256_GAP_f33_17_9fd(X, nbclasses): #-- get the input sizes m, L, depth = X.shape input_shape = (L,depth) #-- parameters of the architecture l2_rate = 1.e-6 dropout_rate = 0.5 nb_conv = 3 nb_fc= 1 nbunits_conv = 128 #-- will be double nbunits_fc = 256 #-- will be double # Define the input placeholder. X_input = Input(input_shape) #-- nb_conv CONV layers X = conv_bn_relu(X_input, nbunits=256, kernel_size=33, kernel_regularizer=l2(l2_rate), padding='same') X = AveragePooling1D(pool_size=2, strides=2, padding='valid')(X) X = Dropout(dropout_rate)(X) X = conv_bn_relu(X, nbunits=256, kernel_size=17, kernel_regularizer=l2(l2_rate), padding='same') X = AveragePooling1D(pool_size=2, strides=2, padding='valid')(X) X = Dropout(dropout_rate)(X) X = conv_bn_relu(X, nbunits=512, kernel_size=9, kernel_regularizer=l2(l2_rate), padding='same') X = AveragePooling1D(pool_size=2, strides=2, padding='valid')(X) X = Dropout(dropout_rate)(X) #-- Flatten + 1 FC layers X = GlobalAveragePooling1D()(X) for add in range(nb_fc): X = fc_bn_relu_drop(X, nbunits=nbunits_fc, kernel_regularizer=l2(l2_rate), dropout_rate=dropout_rate) #-- SOFTMAX layer out = softmax(X, nbclasses, kernel_regularizer=l2(l2_rate)) # Create model. return Model(inputs = X_input, outputs = out, name='Archi_3CONV2AP_1FC256_GAP_f33_17_9fd') #-----------------------------------------------------------------------
示例23: Archi_3CONV2AP_1FC256_GAP_f9_5_3fd# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import GlobalAveragePooling1D [as 别名]def Archi_3CONV2AP_1FC256_GAP_f9_5_3fd(X, nbclasses): #-- get the input sizes m, L, depth = X.shape input_shape = (L,depth) #-- parameters of the architecture l2_rate = 1.e-6 dropout_rate = 0.5 nb_conv = 3 nb_fc= 1 nbunits_fc = 256 #-- will be double # Define the input placeholder. X_input = Input(input_shape) #-- nb_conv CONV layers X = conv_bn_relu(X_input, nbunits=512, kernel_size=9, kernel_regularizer=l2(l2_rate), padding='same') X = AveragePooling1D(pool_size=2, strides=2, padding='valid')(X) X = Dropout(dropout_rate)(X) X = conv_bn_relu(X, nbunits=512, kernel_size=5, kernel_regularizer=l2(l2_rate), padding='same') X = AveragePooling1D(pool_size=2, strides=2, padding='valid')(X) X = Dropout(dropout_rate)(X) X = conv_bn_relu(X, nbunits=512, kernel_size=3, kernel_regularizer=l2(l2_rate), padding='same') X = AveragePooling1D(pool_size=2, strides=2, padding='valid')(X) X = Dropout(dropout_rate)(X) #-- Flatten + 1 FC layers X = GlobalAveragePooling1D()(X) for add in range(nb_fc): X = fc_bn_relu_drop(X, nbunits=nbunits_fc, kernel_regularizer=l2(l2_rate), dropout_rate=dropout_rate) #-- SOFTMAX layer out = softmax(X, nbclasses, kernel_regularizer=l2(l2_rate)) # Create model. return Model(inputs = X_input, outputs = out, name='Archi_3CONV2AP_1FC256_GAP_f9_5_3fd') #-----------------------------------------------------------------------
示例24: Archi_3CONV2AP_1FC256_GAP_f5_3_1fd# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import GlobalAveragePooling1D [as 别名]def Archi_3CONV2AP_1FC256_GAP_f5_3_1fd(X, nbclasses): #-- get the input sizes m, L, depth = X.shape input_shape = (L,depth) #-- parameters of the architecture l2_rate = 1.e-6 dropout_rate = 0.5 nb_conv = 3 nb_fc= 1 nbunits_fc = 256 #-- will be double # Define the input placeholder. X_input = Input(input_shape) #-- nb_conv CONV layers X = conv_bn_relu(X_input, nbunits=512, kernel_size=5, kernel_regularizer=l2(l2_rate), padding='same') X = AveragePooling1D(pool_size=2, strides=2, padding='valid')(X) X = Dropout(dropout_rate)(X) X = conv_bn_relu(X, nbunits=768, kernel_size=3, kernel_regularizer=l2(l2_rate), padding='same') X = AveragePooling1D(pool_size=2, strides=2, padding='valid')(X) X = Dropout(dropout_rate)(X) X = conv_bn_relu(X, nbunits=1024, kernel_size=1, kernel_regularizer=l2(l2_rate), padding='same') X = Dropout(dropout_rate)(X) #-- Flatten + 1 FC layers X = GlobalAveragePooling1D()(X) for add in range(nb_fc): X = fc_bn_relu_drop(X, nbunits=nbunits_fc, kernel_regularizer=l2(l2_rate), dropout_rate=dropout_rate) #-- SOFTMAX layer out = softmax(X, nbclasses, kernel_regularizer=l2(l2_rate)) # Create model. return Model(inputs = X_input, outputs = out, name='Archi_3CONV2AP_1FC256_GAP_f5_3_1fd') #-----------------------------------------------------------------------
示例25: __call__# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import GlobalAveragePooling1D [as 别名]def __call__(self, inputs): x = inputs[0] kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay) x = kl.Conv1D(128, 11, name='conv1', kernel_initializer=self.init, kernel_regularizer=kernel_regularizer)(x) x = kl.BatchNormalization(name='bn1')(x) x = kl.Activation('relu', name='act1')(x) x = kl.MaxPooling1D(2, name='pool1')(x) # 124 x = self._res_unit(x, [32, 32, 128], stage=1, block=1, stride=2) x = self._res_unit(x, [32, 32, 128], stage=1, block=2) # 64 x = self._res_unit(x, [64, 64, 256], stage=2, block=1, stride=2) x = self._res_unit(x, [64, 64, 256], stage=2, block=2) # 32 x = self._res_unit(x, [128, 128, 512], stage=3, block=1, stride=2) x = self._res_unit(x, [128, 128, 512], stage=3, block=2) # 16 x = self._res_unit(x, [256, 256, 1024], stage=4, block=1, stride=2) x = kl.GlobalAveragePooling1D()(x) x = kl.Dropout(self.dropout)(x) return self._build(inputs, x)
开发者ID:cangermueller，项目名称:deepcpg，代码行数:33，代码来源:dna.py
示例26: __call__# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import GlobalAveragePooling1D [as 别名]def __call__(self, inputs): x = self._merge_inputs(inputs) shape = getattr(x, '_keras_shape') replicate_model = self._replicate_model(kl.Input(shape=shape[2:])) x = kl.TimeDistributed(replicate_model)(x) x = kl.GlobalAveragePooling1D()(x) x = kl.Dropout(self.dropout)(x) return self._build(inputs, x)
开发者ID:cangermueller，项目名称:deepcpg，代码行数:12，代码来源:cpg.py
示例27: cnn_spatial_multi# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import GlobalAveragePooling1D [as 别名]def cnn_spatial_multi(self): # shared cnn_spatial model cnn_spatial = self.cnn_spatial() cnn_spatial.load_weights(self.saved_weights) for layer in cnn_spatial.layers: layer.trainable = False # building inputs and output model = Sequential() model.add(TimeDistributed((cnn_spatial), input_shape=self.input_shape_multi)) model.add(GlobalAveragePooling1D()) return model # CNN model for the spatial stream
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