Tensorflow为什么使用类方式建模改成函数方式建模精度就下不去了
这是类方法建模 Resnet50的
class ResNet(Model):
def get_config(self):
config = {
"block_layers": self.block_layers,
"base_filters": self.base_filters,
"block_type": self.block_type
}
base_config = super(ResNet, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def __init__(self, block_layers, name="resnet", base_filters=64, block_type='deep'):
"""
pa
:param block_layers:
:param base_filters:
:param block_type: deep or simple
"""
super(ResNet, self).__init__(name=name)
self.block_layers = block_layers
self.base_filters = base_filters
self.block_type = block_type
self.feature = Sequential([
layers.Conv2D(filters=base_filters, kernel_size=7, strides=2),
layers.BatchNormalization(),
layers.ReLU(),
layers.MaxPool2D(pool_size=3, strides=2),
])
nlb_dims = base_filters * 4
if block_type == "deep":
nlb_dims = base_filters * 16
self.block1 = build_block(base_filters, block_layers[0], init_stride=1, block_type=block_type,
is_downsample=True, name="res_block_1")
self.block2 = build_block(base_filters * 2, block_layers[1], block_type=block_type, name='res_block_2')
self.block3 = build_block(base_filters * 4, block_layers[2], block_type=block_type, name='res_block_3')
# 非局部网络
self.nlb = NonLocalBlock(input_dims=nlb_dims)
self.block4 = build_block(base_filters * 8, block_layers[3], block_type=block_type, name='res_block_4')
def call(self, inputs, training=None, mask=None):
x = inputs
x = self.feature(x)
x = self.block1(x)
x = self.block2(x)
x = self.block3(x)
x = self.nlb(x)
x = self.block4(x)
return x
这是方法建模
def resnet(base_filters=64, block_layers=(3, 4, 6, 3), block_type="deep", se=True, name='resnet', inputs=None):
if inputs is None:
inputs = layers.Input(shape=(128, 128, 2), dtype=tf.float32)
nlb_dims = base_filters * 4
if block_type == "deep":
nlb_dims = base_filters * 16
x = layers.Conv2D(filters=base_filters, kernel_size=7, strides=2)(inputs)
x = layers.BatchNormalization()(x)
x = layers.ReLU()(x)
x = layers.MaxPool2D(pool_size=3, strides=2)(x)
x = build_block(base_filters, block_layers[0], init_stride=1, block_type=block_type, se=se, is_downsample=True,
name="res_block_1")(x)
x = build_block(base_filters * 2, block_layers[1], block_type=block_type, se=se, name='res_block_2')(x)
x = build_block(base_filters * 4, block_layers[2], block_type=block_type, se=se, name='res_block_3')(x)
x = NonLocalBlock(input_dims=nlb_dims)(x)
x = build_block(base_filters * 8, block_layers[3], block_type=block_type, se=se, name='res_block_4')(x)
return Model(inputs, x, name=name)
上一个下降的很快，但是这个就很慢
因为这一行：
base_config = super(ResNet, self).get_config()