Python FixedBatchNormalization.FixedBatchNormalization方法代碼示例

本文整理匯總了Python中keras_frcnn.FixedBatchNormalization.FixedBatchNormalization方法的典型用法代碼示例。如果您正苦於以下問題：Python FixedBatchNormalization.FixedBatchNormalization方法的具體用法？Python FixedBatchNormalization.FixedBatchNormalization怎麽用？Python FixedBatchNormalization.FixedBatchNormalization使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類keras_frcnn.FixedBatchNormalization的用法示例。

在下文中一共展示了FixedBatchNormalization.FixedBatchNormalization方法的13個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: identity_block

# 需要導入模塊: from keras_frcnn import FixedBatchNormalization [as 別名]
# 或者: from keras_frcnn.FixedBatchNormalization import FixedBatchNormalization [as 別名]
def identity_block(input_tensor, kernel_size, filters, stage, block, trainable=True):

    nb_filter1, nb_filter2, nb_filter3 = filters
    
    if K.image_dim_ordering() == 'tf':
        bn_axis = 3
    else:
        bn_axis = 1

    conv_name_base = 'res' + str(stage) + block + '_branch'
    bn_name_base = 'bn' + str(stage) + block + '_branch'

    x = Convolution2D(nb_filter1, (1, 1), name=conv_name_base + '2a', trainable=trainable)(input_tensor)
    x = FixedBatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
    x = Activation('relu')(x)

    x = Convolution2D(nb_filter2, (kernel_size, kernel_size), padding='same', name=conv_name_base + '2b', trainable=trainable)(x)
    x = FixedBatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
    x = Activation('relu')(x)

    x = Convolution2D(nb_filter3, (1, 1), name=conv_name_base + '2c', trainable=trainable)(x)
    x = FixedBatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)

    x = Add()([x, input_tensor])
    x = Activation('relu')(x)
    return x

開發者ID:akshaylamba，項目名稱:FasterRCNN_KERAS，代碼行數:28，代碼來源:resnet.py

示例2: identity_block_td

# 需要導入模塊: from keras_frcnn import FixedBatchNormalization [as 別名]
# 或者: from keras_frcnn.FixedBatchNormalization import FixedBatchNormalization [as 別名]
def identity_block_td(input_tensor, kernel_size, filters, stage, block, trainable=True):

    # identity block time distributed

    nb_filter1, nb_filter2, nb_filter3 = filters
    if K.image_dim_ordering() == 'tf':
        bn_axis = 3
    else:
        bn_axis = 1

    conv_name_base = 'res' + str(stage) + block + '_branch'
    bn_name_base = 'bn' + str(stage) + block + '_branch'

    x = TimeDistributed(Convolution2D(nb_filter1, (1, 1), trainable=trainable, kernel_initializer='normal'), name=conv_name_base + '2a')(input_tensor)
    x = TimeDistributed(FixedBatchNormalization(axis=bn_axis), name=bn_name_base + '2a')(x)
    x = Activation('relu')(x)

    x = TimeDistributed(Convolution2D(nb_filter2, (kernel_size, kernel_size), trainable=trainable, kernel_initializer='normal',padding='same'), name=conv_name_base + '2b')(x)
    x = TimeDistributed(FixedBatchNormalization(axis=bn_axis), name=bn_name_base + '2b')(x)
    x = Activation('relu')(x)

    x = TimeDistributed(Convolution2D(nb_filter3, (1, 1), trainable=trainable, kernel_initializer='normal'), name=conv_name_base + '2c')(x)
    x = TimeDistributed(FixedBatchNormalization(axis=bn_axis), name=bn_name_base + '2c')(x)

    x = Add()([x, input_tensor])
    x = Activation('relu')(x)

    return x

開發者ID:akshaylamba，項目名稱:FasterRCNN_KERAS，代碼行數:30，代碼來源:resnet.py

示例3: conv_block

# 需要導入模塊: from keras_frcnn import FixedBatchNormalization [as 別名]
# 或者: from keras_frcnn.FixedBatchNormalization import FixedBatchNormalization [as 別名]
def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2), trainable=True):

    nb_filter1, nb_filter2, nb_filter3 = filters
    if K.image_dim_ordering() == 'tf':
        bn_axis = 3
    else:
        bn_axis = 1

    conv_name_base = 'res' + str(stage) + block + '_branch'
    bn_name_base = 'bn' + str(stage) + block + '_branch'

    x = Convolution2D(nb_filter1, (1, 1), strides=strides, name=conv_name_base + '2a', trainable=trainable)(input_tensor)
    x = FixedBatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
    x = Activation('relu')(x)

    x = Convolution2D(nb_filter2, (kernel_size, kernel_size), padding='same', name=conv_name_base + '2b', trainable=trainable)(x)
    x = FixedBatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
    x = Activation('relu')(x)

    x = Convolution2D(nb_filter3, (1, 1), name=conv_name_base + '2c', trainable=trainable)(x)
    x = FixedBatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)

    shortcut = Convolution2D(nb_filter3, (1, 1), strides=strides, name=conv_name_base + '1', trainable=trainable)(input_tensor)
    shortcut = FixedBatchNormalization(axis=bn_axis, name=bn_name_base + '1')(shortcut)

    x = Add()([x, shortcut])
    x = Activation('relu')(x)
    return x

開發者ID:akshaylamba，項目名稱:FasterRCNN_KERAS，代碼行數:30，代碼來源:resnet.py

示例4: conv_block_td

# 需要導入模塊: from keras_frcnn import FixedBatchNormalization [as 別名]
# 或者: from keras_frcnn.FixedBatchNormalization import FixedBatchNormalization [as 別名]
def conv_block_td(input_tensor, kernel_size, filters, stage, block, input_shape, strides=(2, 2), trainable=True):

    # conv block time distributed

    nb_filter1, nb_filter2, nb_filter3 = filters
    if K.image_dim_ordering() == 'tf':
        bn_axis = 3
    else:
        bn_axis = 1

    conv_name_base = 'res' + str(stage) + block + '_branch'
    bn_name_base = 'bn' + str(stage) + block + '_branch'

    x = TimeDistributed(Convolution2D(nb_filter1, (1, 1), strides=strides, trainable=trainable, kernel_initializer='normal'), input_shape=input_shape, name=conv_name_base + '2a')(input_tensor)
    x = TimeDistributed(FixedBatchNormalization(axis=bn_axis), name=bn_name_base + '2a')(x)
    x = Activation('relu')(x)

    x = TimeDistributed(Convolution2D(nb_filter2, (kernel_size, kernel_size), padding='same', trainable=trainable, kernel_initializer='normal'), name=conv_name_base + '2b')(x)
    x = TimeDistributed(FixedBatchNormalization(axis=bn_axis), name=bn_name_base + '2b')(x)
    x = Activation('relu')(x)

    x = TimeDistributed(Convolution2D(nb_filter3, (1, 1), kernel_initializer='normal'), name=conv_name_base + '2c', trainable=trainable)(x)
    x = TimeDistributed(FixedBatchNormalization(axis=bn_axis), name=bn_name_base + '2c')(x)

    shortcut = TimeDistributed(Convolution2D(nb_filter3, (1, 1), strides=strides, trainable=trainable, kernel_initializer='normal'), name=conv_name_base + '1')(input_tensor)
    shortcut = TimeDistributed(FixedBatchNormalization(axis=bn_axis), name=bn_name_base + '1')(shortcut)

    x = Add()([x, shortcut])
    x = Activation('relu')(x)
    return x

開發者ID:akshaylamba，項目名稱:FasterRCNN_KERAS，代碼行數:32，代碼來源:resnet.py

示例5: identity_block

# 需要導入模塊: from keras_frcnn import FixedBatchNormalization [as 別名]
# 或者: from keras_frcnn.FixedBatchNormalization import FixedBatchNormalization [as 別名]
def identity_block(input_tensor, kernel_size, filters, stage, block, trainable=True):

    nb_filter1, nb_filter2, nb_filter3 = filters
    
    if K.image_dim_ordering() == 'tf':
        bn_axis = 3
    else:
        bn_axis = 1

    conv_name_base = 'res' + str(stage) + block + '_branch'
    bn_name_base = 'bn' + str(stage) + block + '_branch'

    x = Convolution2D(nb_filter1, (1, 1), name=conv_name_base + '2a', trainable=trainable)(input_tensor)
    x = FixedBatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
    x = Activation('relu')(x) 

    x = Convolution2D(nb_filter2, (kernel_size, kernel_size), padding='same', name=conv_name_base + '2b', trainable=trainable)(x)
    x = FixedBatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
    x = Activation('relu')(x)

    x = Convolution2D(nb_filter3, (1, 1), name=conv_name_base + '2c', trainable=trainable)(x)
    x = FixedBatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)

    x = Add()([x, input_tensor])
    x = Activation('relu')(x)
    return x

開發者ID:Abhijit-2592，項目名稱:Keras_object_detection，代碼行數:28，代碼來源:nn_arch_resnet50.py

示例6: identity_block

# 需要導入模塊: from keras_frcnn import FixedBatchNormalization [as 別名]
# 或者: from keras_frcnn.FixedBatchNormalization import FixedBatchNormalization [as 別名]
def identity_block(input_tensor, kernel_size, filters, stage, block, trainable=True):
    nb_filter1, nb_filter2, nb_filter3 = filters

    if K.image_dim_ordering() == 'tf':
        bn_axis = 3
    else:
        bn_axis = 1

    conv_name_base = 'res' + str(stage) + block + '_branch'
    bn_name_base = 'bn' + str(stage) + block + '_branch'

    x = Convolution2D(nb_filter1, (1, 1), name=conv_name_base + '2a', trainable=trainable)(input_tensor)
    x = FixedBatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
    x = Activation('relu')(x)

    x = Convolution2D(nb_filter2, (kernel_size, kernel_size), padding='same', name=conv_name_base + '2b',
                      trainable=trainable)(x)
    x = FixedBatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
    x = Activation('relu')(x)

    x = Convolution2D(nb_filter3, (1, 1), name=conv_name_base + '2c', trainable=trainable)(x)
    x = FixedBatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)

    x = Add()([x, input_tensor])
    x = Activation('relu')(x)
    return x

開發者ID:moyiliyi，項目名稱:keras-faster-rcnn，代碼行數:28，代碼來源:resnet101.py

示例7: identity_block_td

# 需要導入模塊: from keras_frcnn import FixedBatchNormalization [as 別名]
# 或者: from keras_frcnn.FixedBatchNormalization import FixedBatchNormalization [as 別名]
def identity_block_td(input_tensor, kernel_size, filters, stage, block, trainable=True):
    # identity block time distributed

    nb_filter1, nb_filter2, nb_filter3 = filters
    if K.image_dim_ordering() == 'tf':
        bn_axis = 3
    else:
        bn_axis = 1

    conv_name_base = 'res' + str(stage) + block + '_branch'
    bn_name_base = 'bn' + str(stage) + block + '_branch'

    x = TimeDistributed(Convolution2D(nb_filter1, (1, 1), trainable=trainable, kernel_initializer='normal'),
                        name=conv_name_base + '2a')(input_tensor)
    x = TimeDistributed(FixedBatchNormalization(axis=bn_axis), name=bn_name_base + '2a')(x)
    x = Activation('relu')(x)

    x = TimeDistributed(
        Convolution2D(nb_filter2, (kernel_size, kernel_size), trainable=trainable, kernel_initializer='normal',
                      padding='same'), name=conv_name_base + '2b')(x)
    x = TimeDistributed(FixedBatchNormalization(axis=bn_axis), name=bn_name_base + '2b')(x)
    x = Activation('relu')(x)

    x = TimeDistributed(Convolution2D(nb_filter3, (1, 1), trainable=trainable, kernel_initializer='normal'),
                        name=conv_name_base + '2c')(x)
    x = TimeDistributed(FixedBatchNormalization(axis=bn_axis), name=bn_name_base + '2c')(x)

    x = Add()([x, input_tensor])
    x = Activation('relu')(x)

    return x

開發者ID:moyiliyi，項目名稱:keras-faster-rcnn，代碼行數:33，代碼來源:resnet101.py

示例8: conv_block

# 需要導入模塊: from keras_frcnn import FixedBatchNormalization [as 別名]
# 或者: from keras_frcnn.FixedBatchNormalization import FixedBatchNormalization [as 別名]
def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2), trainable=True):
    nb_filter1, nb_filter2, nb_filter3 = filters
    if K.image_dim_ordering() == 'tf':
        bn_axis = 3
    else:
        bn_axis = 1

    conv_name_base = 'res' + str(stage) + block + '_branch'
    bn_name_base = 'bn' + str(stage) + block + '_branch'

    x = Convolution2D(nb_filter1, (1, 1), strides=strides, name=conv_name_base + '2a', trainable=trainable)(
        input_tensor)
    x = FixedBatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
    x = Activation('relu')(x)

    x = Convolution2D(nb_filter2, (kernel_size, kernel_size), padding='same', name=conv_name_base + '2b',
                      trainable=trainable)(x)
    x = FixedBatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
    x = Activation('relu')(x)

    x = Convolution2D(nb_filter3, (1, 1), name=conv_name_base + '2c', trainable=trainable)(x)
    x = FixedBatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)

    shortcut = Convolution2D(nb_filter3, (1, 1), strides=strides, name=conv_name_base + '1', trainable=trainable)(
        input_tensor)
    shortcut = FixedBatchNormalization(axis=bn_axis, name=bn_name_base + '1')(shortcut)

    x = Add()([x, shortcut])
    x = Activation('relu')(x)
    return x

開發者ID:moyiliyi，項目名稱:keras-faster-rcnn，代碼行數:32，代碼來源:resnet101.py

示例9: conv_block_td

# 需要導入模塊: from keras_frcnn import FixedBatchNormalization [as 別名]
# 或者: from keras_frcnn.FixedBatchNormalization import FixedBatchNormalization [as 別名]
def conv_block_td(input_tensor, kernel_size, filters, stage, block, input_shape, strides=(2, 2), trainable=True):
    # conv block time distributed

    nb_filter1, nb_filter2, nb_filter3 = filters
    if K.image_dim_ordering() == 'tf':
        bn_axis = 3
    else:
        bn_axis = 1

    conv_name_base = 'res' + str(stage) + block + '_branch'
    bn_name_base = 'bn' + str(stage) + block + '_branch'

    x = TimeDistributed(
        Convolution2D(nb_filter1, (1, 1), strides=strides, trainable=trainable, kernel_initializer='normal'),
        input_shape=input_shape, name=conv_name_base + '2a')(input_tensor)
    x = TimeDistributed(FixedBatchNormalization(axis=bn_axis), name=bn_name_base + '2a')(x)
    x = Activation('relu')(x)

    x = TimeDistributed(Convolution2D(nb_filter2, (kernel_size, kernel_size), padding='same', trainable=trainable,
                                      kernel_initializer='normal'), name=conv_name_base + '2b')(x)
    x = TimeDistributed(FixedBatchNormalization(axis=bn_axis), name=bn_name_base + '2b')(x)
    x = Activation('relu')(x)

    x = TimeDistributed(Convolution2D(nb_filter3, (1, 1), kernel_initializer='normal'), name=conv_name_base + '2c',
                        trainable=trainable)(x)
    x = TimeDistributed(FixedBatchNormalization(axis=bn_axis), name=bn_name_base + '2c')(x)

    shortcut = TimeDistributed(
        Convolution2D(nb_filter3, (1, 1), strides=strides, trainable=trainable, kernel_initializer='normal'),
        name=conv_name_base + '1')(input_tensor)
    shortcut = TimeDistributed(FixedBatchNormalization(axis=bn_axis), name=bn_name_base + '1')(shortcut)

    x = Add()([x, shortcut])
    x = Activation('relu')(x)
    return x

開發者ID:moyiliyi，項目名稱:keras-faster-rcnn，代碼行數:37，代碼來源:resnet101.py

示例10: identity_block

# 需要導入模塊: from keras_frcnn import FixedBatchNormalization [as 別名]
# 或者: from keras_frcnn.FixedBatchNormalization import FixedBatchNormalization [as 別名]
def identity_block(input_tensor, kernel_size, filters, stage, block, trainable=True):

    '''The identity_block is the block that has no conv layer at shortcut
    # Arguments
            input_tensor: input tensor
            kernel_size: defualt 3, the kernel size of middle conv layer at main path
            filters: list of integers, the nb_filters of 3 conv layer at main path
            stage: integer, current stage label, used for generating layer names
            block: 'a','b'..., current block label, used for generating layer names
    '''
    nb_filter1, nb_filter2, nb_filter3 = filters
    if K.image_dim_ordering() == 'tf':
        bn_axis = 3
    else:
        bn_axis = 1
    conv_name_base = 'res' + str(stage) + block + '_branch'
    bn_name_base = 'bn' + str(stage) + block + '_branch'

    x = Convolution2D(nb_filter1, 1, 1, name=conv_name_base + '2a', trainable=trainable)(input_tensor)
    x = FixedBatchNormalization(trainable=False,axis=bn_axis, name=bn_name_base + '2a')(x)
    x = Activation('relu')(x)

    x = Convolution2D(nb_filter2, kernel_size, kernel_size, border_mode='same', name=conv_name_base + '2b', trainable=trainable)(x)
    x = FixedBatchNormalization(trainable=False,axis=bn_axis, name=bn_name_base + '2b')(x)
    x = Activation('relu')(x)

    x = Convolution2D(nb_filter3, 1, 1, name=conv_name_base + '2c', trainable=trainable)(x)
    x = FixedBatchNormalization(trainable=False,axis=bn_axis, name=bn_name_base + '2c')(x)

    x = merge([x, input_tensor], mode='sum')
    x = Activation('relu')(x)
    return x

開發者ID:small-yellow-duck，項目名稱:keras-frcnn，代碼行數:34，代碼來源:resnet.py

示例11: identity_block_td

# 需要導入模塊: from keras_frcnn import FixedBatchNormalization [as 別名]
# 或者: from keras_frcnn.FixedBatchNormalization import FixedBatchNormalization [as 別名]
def identity_block_td(input_tensor, kernel_size, filters, stage, block, trainable=True):
    '''The identity_block is the block that has no conv layer at shortcut
    # Arguments
            input_tensor: input tensor
            kernel_size: defualt 3, the kernel size of middle conv layer at main path
            filters: list of integers, the nb_filters of 3 conv layer at main path
            stage: integer, current stage label, used for generating layer names
            block: 'a','b'..., current block label, used for generating layer names
    '''
    nb_filter1, nb_filter2, nb_filter3 = filters
    if K.image_dim_ordering() == 'tf':
        bn_axis = 3
    else:
        bn_axis = 1

    conv_name_base = 'res' + str(stage) + block + '_branch'
    bn_name_base = 'bn' + str(stage) + block + '_branch'


    x = TimeDistributed(Convolution2D(nb_filter1, 1, 1, trainable=trainable, init='normal'), name=conv_name_base + '2a')(input_tensor)
    x = TimeDistributed(FixedBatchNormalization(trainable=False,axis=bn_axis), name=bn_name_base + '2a')(x)

    x = Activation('relu')(x)

    x = TimeDistributed(Convolution2D(nb_filter2, kernel_size, kernel_size, trainable=trainable, init='normal',border_mode='same'), name=conv_name_base + '2b')(x)
    x = TimeDistributed(FixedBatchNormalization(trainable=False,axis=bn_axis), name=bn_name_base + '2b')(x)

    x = Activation('relu')(x)

    x = TimeDistributed(Convolution2D(nb_filter3, 1, 1, trainable=trainable, init='normal'), name=conv_name_base + '2c')(x)
    x = TimeDistributed(FixedBatchNormalization(trainable=False,axis=bn_axis), name=bn_name_base + '2c')(x)


    x = merge([x, input_tensor], mode='sum')
    x = Activation('relu')(x)

    return x

開發者ID:small-yellow-duck，項目名稱:keras-frcnn，代碼行數:39，代碼來源:resnet.py

示例12: conv_block

# 需要導入模塊: from keras_frcnn import FixedBatchNormalization [as 別名]
# 或者: from keras_frcnn.FixedBatchNormalization import FixedBatchNormalization [as 別名]
def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2), trainable=True):
    '''conv_block is the block that has a conv layer at shortcut
    # Arguments
            input_tensor: input tensor
            kernel_size: defualt 3, the kernel size of middle conv layer at main path
            filters: list of integers, the nb_filters of 3 conv layer at main path
            stage: integer, current stage label, used for generating layer names
            block: 'a','b'..., current block label, used for generating layer names
    Note that from stage 3, the first conv layer at main path is with subsample=(2,2)
    And the shortcut should have subsample=(2,2) as well
    '''
    nb_filter1, nb_filter2, nb_filter3 = filters
    if K.image_dim_ordering() == 'tf':
        bn_axis = 3
    else:
        bn_axis = 1
    conv_name_base = 'res' + str(stage) + block + '_branch'
    bn_name_base = 'bn' + str(stage) + block + '_branch'

    x = Convolution2D(nb_filter1, 1, 1, subsample=strides, name=conv_name_base + '2a', trainable=trainable)(input_tensor)
    x = FixedBatchNormalization(trainable=False,axis=bn_axis, name=bn_name_base + '2a')(x)
    x = Activation('relu')(x)

    x = Convolution2D(nb_filter2, kernel_size, kernel_size, border_mode='same', name=conv_name_base + '2b', trainable=trainable)(x)
    x = FixedBatchNormalization(trainable=False,axis=bn_axis, name=bn_name_base + '2b')(x)
    x = Activation('relu')(x)

    x = Convolution2D(nb_filter3, 1, 1, name=conv_name_base + '2c', trainable=trainable)(x)
    x = FixedBatchNormalization(trainable=False,axis=bn_axis, name=bn_name_base + '2c')(x)

    shortcut = Convolution2D(nb_filter3, 1, 1, subsample=strides, name=conv_name_base + '1', trainable=trainable)(input_tensor)
    shortcut = FixedBatchNormalization(trainable=False,axis=bn_axis, name=bn_name_base + '1')(shortcut)

    x = merge([x, shortcut], mode='sum')
    x = Activation('relu')(x)
    return x

開發者ID:small-yellow-duck，項目名稱:keras-frcnn，代碼行數:38，代碼來源:resnet.py

示例13: conv_block_td

# 需要導入模塊: from keras_frcnn import FixedBatchNormalization [as 別名]
# 或者: from keras_frcnn.FixedBatchNormalization import FixedBatchNormalization [as 別名]
def conv_block_td(input_tensor, kernel_size, filters, stage, block, strides=(2, 2), trainable=True):
    '''conv_block is the block that has a conv layer at shortcut
    # Arguments
            input_tensor: input tensor
            kernel_size: defualt 3, the kernel size of middle conv layer at main path
            filters: list of integers, the nb_filters of 3 conv layer at main path
            stage: integer, current stage label, used for generating layer names
            block: 'a','b'..., current block label, used for generating layer names
    Note that from stage 3, the first conv layer at main path is with subsample=(2,2)
    And the shortcut should have subsample=(2,2) as well
    '''
    nb_filter1, nb_filter2, nb_filter3 = filters
    if K.image_dim_ordering() == 'tf':
        bn_axis = 3
    else:
        bn_axis = 1

    conv_name_base = 'res' + str(stage) + block + '_branch'
    bn_name_base = 'bn' + str(stage) + block + '_branch'

    x = TimeDistributed(Convolution2D(nb_filter1, 1, 1, subsample=strides, trainable=trainable, init='normal'), name=conv_name_base + '2a')(input_tensor)
    x = TimeDistributed(FixedBatchNormalization(trainable=False,axis=bn_axis), name=bn_name_base + '2a')(x)

    x = Activation('relu')(x)

    x = TimeDistributed(Convolution2D(nb_filter2, kernel_size, kernel_size, border_mode='same', trainable=trainable, init='normal'), name=conv_name_base + '2b')(x)
    x = TimeDistributed(FixedBatchNormalization(trainable=False,axis=bn_axis), name=bn_name_base + '2b')(x)

    x = Activation('relu')(x)

    x = TimeDistributed(Convolution2D(nb_filter3, 1, 1, init='normal'), name=conv_name_base + '2c', trainable=trainable)(x)
    x = TimeDistributed(FixedBatchNormalization(trainable=False,axis=bn_axis), name=bn_name_base + '2c')(x)

    shortcut = TimeDistributed(Convolution2D(nb_filter3, 1, 1, subsample=strides, trainable=trainable, init='normal'), name=conv_name_base + '1')(input_tensor)
    shortcut = TimeDistributed(FixedBatchNormalization(trainable=False,axis=bn_axis), name=bn_name_base + '1')(shortcut)


    x = merge([x, shortcut], mode='sum')
    x = Activation('relu')(x)
    return x

開發者ID:small-yellow-duck，項目名稱:keras-frcnn，代碼行數:42，代碼來源:resnet.py

注：本文中的keras_frcnn.FixedBatchNormalization.FixedBatchNormalization方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。