自学教程：Python layers.GlobalAveragePooling3D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 GlobalAveragePooling3D [as 别名]def squeeze_excitation_block_3D(inputSE, ratio=16):    '''    Creates a squeeze and excitation block    :param input: input tensor    :param ratio: reduction ratio r for bottleneck given by the two FC layers    :return: keras tensor    '''    if backend.image_data_format() == 'channels_first':        channels = 1    else:        channels = -1    # number of input filters/channels    inputSE_shape = backend.int_shape(inputSE)    numChannels = inputSE_shape[channels]    #squeeze operation    output = GlobalAveragePooling3D(data_format=backend.image_data_format())(inputSE)    #excitation operation    output = Dense(numChannels//ratio, activation='relu', use_bias=True, kernel_initializer='he_normal')(output)    output = Dense(numChannels, activation='sigmoid', use_bias=True, kernel_initializer='he_normal')(output)    #scale operation    output = multiply([inputSE, output])    return output 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import GlobalAveragePooling3D [as 别名]def preds3d_baseline(width):        learning_rate = 5e-5    #optimizer = SGD(lr=learning_rate, momentum = 0.9, decay = 1e-3, nesterov = True)    optimizer = Adam(lr=learning_rate)        inputs = Input(shape=(1, 136, 168, 168))    conv1 = Convolution3D(width, 3, 3, 3, activation = 'relu', border_mode='same')(inputs)    conv1 = BatchNormalization(axis = 1)(conv1)    conv1 = Convolution3D(width*2, 3, 3, 3, activation = 'relu', border_mode='same')(conv1)    conv1 = BatchNormalization(axis = 1)(conv1)    pool1 = MaxPooling3D(pool_size=(2, 2, 2), border_mode='same')(conv1)        conv2 = Convolution3D(width*2, 3, 3, 3, activation = 'relu', border_mode='same')(pool1)    conv2 = BatchNormalization(axis = 1)(conv2)    conv2 = Convolution3D(width*4, 3, 3, 3, activation = 'relu', border_mode='same')(conv2)    conv2 = BatchNormalization(axis = 1)(conv2)    pool2 = MaxPooling3D(pool_size=(2, 2, 2), border_mode='same')(conv2)    conv3 = Convolution3D(width*4, 3, 3, 3, activation = 'relu', border_mode='same')(pool2)    conv3 = BatchNormalization(axis = 1)(conv3)    conv3 = Convolution3D(width*8, 3, 3, 3, activation = 'relu', border_mode='same')(conv3)    conv3 = BatchNormalization(axis = 1)(conv3)    pool3 = MaxPooling3D(pool_size=(2, 2, 2), border_mode='same')(conv3)        output = GlobalAveragePooling3D()(pool3)    output = Dense(2, activation='softmax', name = 'predictions')(output)    model3d = Model(inputs, output)    model3d.compile(loss='categorical_crossentropy', optimizer = optimizer, metrics = ['accuracy'])    return model3d 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import GlobalAveragePooling3D [as 别名]def preds3d_baseline(width):        learning_rate = 5e-5    optimizer = SGD(lr=learning_rate, momentum = 0.9, decay = 1e-3, nesterov = True)    #optimizer = Adam(lr=learning_rate)        inputs = Input(shape=(1, 136, 168, 168))    conv1 = Convolution3D(width, 3, 3, 3, activation = 'relu', border_mode='same')(inputs)    conv1 = BatchNormalization(axis = 1)(conv1)    conv1 = Convolution3D(width*2, 3, 3, 3, activation = 'relu', border_mode='same')(conv1)    conv1 = BatchNormalization(axis = 1)(conv1)    pool1 = MaxPooling3D(pool_size=(2, 2, 2), border_mode='same')(conv1)        conv2 = Convolution3D(width*2, 3, 3, 3, activation = 'relu', border_mode='same')(pool1)    conv2 = BatchNormalization(axis = 1)(conv2)    conv2 = Convolution3D(width*4, 3, 3, 3, activation = 'relu', border_mode='same')(conv2)    conv2 = BatchNormalization(axis = 1)(conv2)    pool2 = MaxPooling3D(pool_size=(2, 2, 2), border_mode='same')(conv2)    conv3 = Convolution3D(width*4, 3, 3, 3, activation = 'relu', border_mode='same')(pool2)    conv3 = BatchNormalization(axis = 1)(conv3)    conv3 = Convolution3D(width*8, 3, 3, 3, activation = 'relu', border_mode='same')(conv3)    conv3 = BatchNormalization(axis = 1)(conv3)    pool3 = MaxPooling3D(pool_size=(2, 2, 2), border_mode='same')(conv3)        output = GlobalAveragePooling3D()(pool3)    output = Dense(2, activation='softmax', name = 'predictions')(output)    model3d = Model(inputs, output)    model3d.compile(loss='categorical_crossentropy', optimizer = optimizer, metrics = ['accuracy'])    return model3d# 1398 stage1 original examples 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import GlobalAveragePooling3D [as 别名]def createModel(patchSize, numClasses):    if K.image_data_format() == 'channels_last':        bn_axis = -1    else:        bn_axis = 1    growthRate_k = 12    compressionFactor = 1.0    input_tensor = Input(shape=(patchSize[0], patchSize[1], patchSize[2], 1))    # first conv layer    x = Conv3D(16, (3, 3, 3), strides=(1, 1, 1), padding='same', kernel_initializer='he_normal')(input_tensor)    # 1. Dense Block    x, numFilters = dense_block_3D(x, numInputFilters=16, numLayers=10, growthRate_k=growthRate_k,                                   bottleneck_enabled=True)    # Transition Layer    x, numFilters = transition_SE_layer_3D(x, numFilters, compressionFactor=compressionFactor, se_ratio=16)    # 2. Dense Block    x, numFilters = dense_block_3D(x, numInputFilters=numFilters, numLayers=10, growthRate_k=growthRate_k,                                   bottleneck_enabled=True)    # Transition Layer    x, numFilters = transition_SE_layer_3D(x, numFilters, compressionFactor=compressionFactor, se_ratio=16)    # 3. Dense Block    x, numFilters = dense_block_3D(x, numInputFilters=numFilters, numLayers=10, growthRate_k=growthRate_k,                                   bottleneck_enabled=True)    # SE Block    x = squeeze_excitation_block_3D(x, ratio=16)    x = BatchNormalization(axis=bn_axis)(x)    x = Activation('relu')(x)    # global average pooling    x = GlobalAveragePooling3D(data_format='channels_last')(x)    # fully-connected layer    output = Dense(units=numClasses,                   activation='softmax',                   kernel_initializer='he_normal',                   name='fully-connected')(x)    # create model    cnn = Model(input_tensor, output, name='3D-DenseNet-34')    sModelName = '3D-DenseNet-34'    return cnn, sModelName 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import GlobalAveragePooling3D [as 别名]def createModel(patchSize, numClasses):    if K.image_data_format() == 'channels_last':        bn_axis = -1    else:        bn_axis = 1    growthRate_k = 12    compressionFactor = 0.5    input_tensor = Input(shape=(patchSize[0], patchSize[1], patchSize[2], 1))    # first conv layer    x = Conv3D(16, (3,3,3), strides=(1,1,1), padding='same', kernel_initializer='he_normal')(input_tensor)    # 1. Dense Block    x, numFilters = dense_block_3D(x, numInputFilters=16, numLayers=7, growthRate_k=growthRate_k, bottleneck_enabled=True)    # Transition Layer    x, numFilters = transition_SE_layer_3D(x, numFilters, compressionFactor=compressionFactor, se_ratio=8)    # 2. Dense Block    x, numFilters = dense_block_3D(x, numInputFilters=numFilters, numLayers=7, growthRate_k=growthRate_k, bottleneck_enabled=True)    #Transition Layer    x, numFilters = transition_SE_layer_3D(x, numFilters, compressionFactor=compressionFactor, se_ratio=8)    #3. Dense Block    x, numFilters = dense_block_3D(x, numInputFilters=numFilters, numLayers=7, growthRate_k=growthRate_k, bottleneck_enabled=True)    # SE Block    x = squeeze_excitation_block_3D(x, ratio=16)    x = BatchNormalization(axis=bn_axis)(x)    x = Activation('relu')(x)    # global average pooling    x = GlobalAveragePooling3D(data_format='channels_last')(x)    # fully-connected layer    output = Dense(units=numClasses,                   activation='softmax',                   kernel_initializer='he_normal',                   name='fully-connected')(x)    # create model    cnn = Model(input_tensor, output, name='3D-DenseNet-34')    sModelName = '3D-DenseNet-34'    return cnn, sModelName 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import GlobalAveragePooling3D [as 别名]def fCreateModel_FCN_MultiFM(patchSize, dr_rate=0.0, iPReLU=0,l1_reg=0, l2_reg=1e-6):    # Total params: 1,420,549    # The dense layer is repleced by a convolutional layer with filters=2 for the two classes    # The FM from the third down scaled convolutional layer is upsempled by deconvolution and    # added with the FM from the second down scaled convolutional layer.    # The combined FM goes through a convolutional layer with filters=2 for the two classes    # The two predictions are averages as the final result.    Strides = fgetStrides()    kernelnumber = fgetKernelNumber()    inp = Input(shape=(1, int(patchSize[0]), int(patchSize[1]), int(patchSize[2])))    after_Conv_1 = fCreateVNet_Block(inp, kernelnumber[0], type=fgetLayerNumConv(), l2_reg=l2_reg)    after_DownConv_1 = fCreateVNet_DownConv_Block(after_Conv_1, after_Conv_1._keras_shape[1], Strides[0],                                                     iPReLU=iPReLU, dr_rate=dr_rate, l2_reg=l2_reg)    after_Conv_2 = fCreateVNet_Block(after_DownConv_1, kernelnumber[1], type=fgetLayerNumConv(), l2_reg=l2_reg)    after_DownConv_2 = fCreateVNet_DownConv_Block(after_Conv_2, after_Conv_2._keras_shape[1], Strides[1],                                                   iPReLU=iPReLU, dr_rate=dr_rate, l2_reg=l2_reg)    after_Conv_3 = fCreateVNet_Block(after_DownConv_2, kernelnumber[2], type=fgetLayerNumConv(), l2_reg=l2_reg)    after_DownConv_3 = fCreateVNet_DownConv_Block(after_Conv_3, after_Conv_3._keras_shape[1], Strides[2],                                                   iPReLU=iPReLU, dr_rate=dr_rate, l2_reg=l2_reg)    # fully convolution over the FM from the deepest level    dropout_out1 = Dropout(dr_rate)(after_DownConv_3)    fclayer1 = Conv3D(2,                       kernel_size=(1,1,1),                       kernel_initializer='he_normal',                       weights=None,                       padding='valid',                       strides=(1, 1, 1),                       kernel_regularizer=l1_l2(l1_reg, l2_reg),                       )(dropout_out1)    fclayer1 = GlobalAveragePooling3D()(fclayer1)        # Upsample FM from the deepest level, add with FM from level 2,     UpedFM_Level3 = Conv3DTranspose(filters=97, kernel_size=(3,3,1), strides=(2,2,1), padding='same')(after_DownConv_3)    conbined_FM_Level23 = add([UpedFM_Level3, after_DownConv_2])        fclayer2 = Conv3D(2,                       kernel_size=(1,1,1),                       kernel_initializer='he_normal',                       weights=None,                       padding='valid',                       strides=(1, 1, 1),                       kernel_regularizer=l1_l2(l1_reg, l2_reg),                       )(conbined_FM_Level23)    fclayer2 = GlobalAveragePooling3D()(fclayer2)    # combine the two predictions using average    fcl_aver = average([fclayer1, fclayer2])    predict = Activation('softmax')(fcl_aver)    cnn_fcl_msfm = Model(inputs=inp, outputs=predict)    return cnn_fcl_msfm 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import GlobalAveragePooling3D [as 别名]def densenet_3d(nb_classes, input_shape, weight_decay=0.005, dropout_rate=0.2):    model_input = Input(shape=input_shape)    # 112x112x8    # stage 1 Initial convolution    x = conv_factory(model_input, 64)    x = MaxPool3D((2, 2, 1), strides=(2, 2, 1), padding='same')(x)    # 56x56x8    # stage 2    x = dense_block(x, 32, internal_layers=4,                             dropout_rate=dropout_rate)    x = MaxPool3D((2, 2, 2), strides=(2, 2, 2), padding='same')(x)    x = conv_factory(x, 128, (1, 1, 1), dropout_rate=dropout_rate)    # 28x28x4    # stage 3    x= dense_block(x, 32, internal_layers=4,                   dropout_rate=dropout_rate)    x = MaxPool3D((2, 2, 2), strides=(2, 2, 2), padding='same')(x)    x = conv_factory(x, 128, (1, 1, 1), dropout_rate=dropout_rate)    # 14x14x2    # stage 4    x = dense_block(x, 64, internal_layers=4,                   dropout_rate=dropout_rate)    x = MaxPool3D((2, 2, 2), strides=(2, 2, 2), padding='same')(x)    x = conv_factory(x, 256, (1, 1, 1), dropout_rate=dropout_rate)    # 7x7x1    # stage 5    x = dense_block(x, 64, internal_layers=4,                   dropout_rate=dropout_rate)    x = conv_factory(x, 256, (1, 1, 1), dropout_rate=dropout_rate)    x = GlobalAveragePooling3D()(x)    x = Dense(nb_classes,              activation='softmax',              kernel_regularizer=l2(weight_decay),              bias_regularizer=l2(weight_decay))(x)    model = Model(inputs=model_input, outputs=x, name="densenet_3d")    return model