自学教程：Python layers.AveragePooling1D方法代码示例

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import AveragePooling1D [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 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import AveragePooling1D [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 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import AveragePooling1D [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 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import AveragePooling1D [as 别名]def Archi_3CONV2AP_1FC256_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_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=128, 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=192, 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=256, 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 = Flatten()(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_f33_17_9fd')	#----------------------------------------------------------------------- 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import AveragePooling1D [as 别名]def Archi_3CONV2AP_1FC256_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=128, 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=256, 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=384, 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)		#-- Flatten + 	1 FC layers	X = Flatten()(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_f17_9_5fd')	#----------------------------------------------------------------------- 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import AveragePooling1D [as 别名]def Archi_3CONV2AP_1FC256_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_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=nbunits_conv, 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=nbunits_conv*2**1, 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=nbunits_conv*2**2, 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 = Flatten()(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_f9_5_3fd')	#----------------------------------------------------------------------- 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import AveragePooling1D [as 别名]def Archi_3CONV2AP_1FC256_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_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=nbunits_conv, 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=nbunits_conv*2**1, 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=nbunits_conv*2**1, kernel_size=1, 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 = Flatten()(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_f5_3_1fd')		#----------------------------------------------------------------------- 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import AveragePooling1D [as 别名]def Archi_3CONV2AP_1FC256_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_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=nbunits_conv, 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=nbunits_conv, kernel_size=1, kernel_regularizer=l2(l2_rate), padding='same')	X = Dropout(dropout_rate)(X)	X = conv_bn_relu(X, nbunits=nbunits_conv, kernel_size=1, kernel_regularizer=l2(l2_rate), padding='same')	X = Dropout(dropout_rate)(X)		#-- Flatten + 	1 FC layers	X = Flatten()(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_f3_1_1fd')		#----------------------------------------------------------------------- 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import AveragePooling1D [as 别名]def Archi_3CONV2AP_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 = 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)	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)		#-- 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_f17_9_5fd')	#----------------------------------------------------------------------- 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import AveragePooling1D [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')		#----------------------------------------------------------------------- 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import AveragePooling1D [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')		#----------------------------------------------------------------------- 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import AveragePooling1D [as 别名]def get_contextual_temporal_gated_input(X, conf_dict):    # X: input to be gated, (None, steps, x_dim)    # return X' = X * c * softmax(X.Average(f(X))), f is a non-linear function.    assert len(X._keras_shape) == 3, [X._keras_shape]    seq_len, x_dim = X._keras_shape[1], X._keras_shape[2]    gating_hidden_dim = conf_dict['gating_hidden_dim']    gating_hidden_actv = conf_dict['gating_hidden_actv']    scale = conf_dict['scale']    nl_choice = conf_dict['nl_choice']    Xp = ReshapeBatchAdhoc()(X)    Xp = Dense(gating_hidden_dim, activation=gating_hidden_actv)(Xp)    Xp = ReshapeBatchAdhoc(mid_dim=seq_len)(Xp)    Xp = AveragePooling1D(seq_len)(Xp)  # (None, 1, x_dim)    Xp = Reshape((Xp._keras_shape[-1], ))(Xp)    if nl_choice == 'nl':        Xp = Dense(x_dim, activation='relu', bias=True)(Xp)    elif nl_choice == 'bn+nl':        Xp = BatchNormalization()(Xp)        Xp = Dense(x_dim, activation='relu', bias=True)(Xp)    elif nl_choice == 'bn+l':        Xp = BatchNormalization()(Xp)        Xp = Dense(x_dim, activation='linear', bias=True)(Xp)    else:        assert False, 'nonononon'    Xp = Reshape((1, x_dim))(Xp)  # (None, 1, x_dim)    Xp = DotSumMergeAdhoc()([X, Xp])  # (None, steps, 1)    if True:  # debug        Xp = Activation('sigmoid')(Xp)  # (None, steps, 1)    else:        # following can be uncomment to replace sigmoid with softmax        Xp = Reshape((Xp._keras_shape[1], ))(Xp)  # (None, steps)        Xp = Activation('softmax')(Xp)  # (None, steps)        Xp = Reshape((Xp._keras_shape[-1], 1))(Xp)  # (None, steps, 1)    X = DotMergeAdhoc(scale=scale)([X, Xp]) # (None, steps, x_dim)    return X 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import AveragePooling1D [as 别名]def ResidualBlock1D_helper(layers, kernel_size, filters, final_stride=1):    def f(_input):        basic = _input        for ln in range(layers):            #basic = BatchNormalization()( basic ) # triggers known keras bug w/ TimeDistributed: https://github.com/fchollet/keras/issues/5221            basic = ELU()(basic)              basic = Conv1D(filters, kernel_size, kernel_initializer='he_normal',                           kernel_regularizer=l2(1.e-4), padding='same')(basic)        # note that this strides without averaging        return AveragePooling1D(pool_size=1, strides=final_stride)(Add()([_input, basic]))    return f 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import AveragePooling1D [as 别名]def __backbone(inp, C=0.001, initial='he_normal'):        """        # 用于信号片段特征学习的卷积层组合        :param inp:  keras tensor, 单个信号切片输入        :param C:   double, 正则化系数， 默认0.001        :param initial:  str, 初始化方式， 默认he_normal        :return: keras tensor, 单个信号切片经过卷积层后的输出        """        net = Conv1D(4, 31, padding='same', kernel_initializer=initial, kernel_regularizer=regularizers.l2(C))(inp)        net = BatchNormalization()(net)        net = Activation('relu')(net)        net = AveragePooling1D(5, 5)(net)        net = Conv1D(8, 11, padding='same', kernel_initializer=initial, kernel_regularizer=regularizers.l2(C))(net)        net = BatchNormalization()(net)        net = Activation('relu')(net)        net = AveragePooling1D(5, 5)(net)        net = Conv1D(8, 7, padding='same', kernel_initializer=initial, kernel_regularizer=regularizers.l2(C))(net)        net = BatchNormalization()(net)        net = Activation('relu')(net)        net = AveragePooling1D(5, 5)(net)        net = Conv1D(16, 5, padding='same', kernel_initializer=initial, kernel_regularizer=regularizers.l2(C))(net)        net = BatchNormalization()(net)        net = Activation('relu')(net)        net = AveragePooling1D(int(net.shape[1]), int(net.shape[1]))(net)        return net 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import AveragePooling1D [as 别名]def Archi_3CONV2AP_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 = 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)	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_f3_1_1fd')				#-----------------------------------------------------------------------		#-----------------------------------------------------------------------#--------------------- Switcher for running the architectures 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import AveragePooling1D [as 别名]def pooling(layer, layer_in, layerId, tensor=True):    poolMap = {        ('1D', 'MAX'): MaxPooling1D,        ('2D', 'MAX'): MaxPooling2D,        ('3D', 'MAX'): MaxPooling3D,        ('1D', 'AVE'): AveragePooling1D,        ('2D', 'AVE'): AveragePooling2D,        ('3D', 'AVE'): AveragePooling3D,    }    out = {}    layer_type = layer['params']['layer_type']    pool_type = layer['params']['pool']    padding = get_padding(layer)    if (layer_type == '1D'):        strides = layer['params']['stride_w']        kernel = layer['params']['kernel_w']        if (padding == 'custom'):            p_w = layer['params']['pad_w']            out[layerId + 'Pad'] = ZeroPadding1D(padding=p_w)(*layer_in)            padding = 'valid'            layer_in = [out[layerId + 'Pad']]    elif (layer_type == '2D'):        strides = (layer['params']['stride_h'], layer['params']['stride_w'])        kernel = (layer['params']['kernel_h'], layer['params']['kernel_w'])        if (padding == 'custom'):            p_h, p_w = layer['params']['pad_h'], layer['params']['pad_w']            out[layerId + 'Pad'] = ZeroPadding2D(padding=(p_h, p_w))(*layer_in)            padding = 'valid'            layer_in = [out[layerId + 'Pad']]    else:        strides = (layer['params']['stride_h'], layer['params']['stride_w'],                   layer['params']['stride_d'])        kernel = (layer['params']['kernel_h'], layer['params']['kernel_w'],                  layer['params']['kernel_d'])        if (padding == 'custom'):            p_h, p_w, p_d = layer['params']['pad_h'], layer['params']['pad_w'],/                layer['params']['pad_d']            out[layerId +                'Pad'] = ZeroPadding3D(padding=(p_h, p_w, p_d))(*layer_in)            padding = 'valid'            layer_in = [out[layerId + 'Pad']]    # Note - figure out a permanent fix for padding calculation of layers    # in case padding is given in layer attributes    # if ('padding' in layer['params']):    #    padding = layer['params']['padding']    out[layerId] = poolMap[(layer_type, pool_type)](        pool_size=kernel, strides=strides, padding=padding)    if tensor:        out[layerId] = out[layerId](*layer_in)    return out# ********** Locally-connected Layers **********