Created by: ENGWayne

python版本：python3.7 框架版本：PaddlePaddle 1.7.1 环境：AI studio GPU版本 定义好VGG模型后，运行如下代码：

if __name__ == '__main__':
    # 创建模型
    with fluid.dygraph.guard():
        model = VGG("VGG", num_classes=3)
    train(model, augment='imgaugLib') # 使用imgaug库进行数据増广的训练

报错：

---------------------------------------------------------------------------TypeError                                 Traceback (most recent call last)<ipython-input-9-ccf1dc315009> in <module>
     95     # train(model) # 不使用数据増广的训练
     96     with fluid.dygraph.guard():
---> 97         model = VGG("VGG", num_classes=3)
     98 
     99     train(model, augment='imgaugLib') # 使用imgaug库进行数据増广的训练
<ipython-input-8-3792535017df> in __init__(self, name_scope, conv_arch, num_classes)
     29         for (num_convs, num_channels) in conv_arch:
     30             block = self.add_sublayer('block_' + str(iter_id), 
---> 31                     vgg_block(self.full_name(), num_convs, num_channels))
     32             self.vgg_blocks.append(block)
     33             iter_id += 1
<ipython-input-8-3792535017df> in __init__(self, name_scope, num_convs, num_channels)
     11         for i in range(num_convs):
     12             conv_layer = self.add_sublayer('conv_' + str(i), Conv2D(self.full_name(), 
---> 13                                         num_filters=num_channels, filter_size=3, padding=1, act='relu'))
     14             self.conv_list.append(conv_layer)
     15         self.pool = Pool2D(self.full_name(), pool_stride=2, pool_size = 2, pool_type='max')
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/nn.py in __init__(self, num_channels, num_filters, filter_size, stride, padding, dilation, groups, param_attr, bias_attr, use_cudnn, act, dtype)
    209             shape=filter_shape,
    210             dtype=self._dtype,
--> 211             default_initializer=_get_default_param_initializer())
    212 
    213         self.bias = self.create_parameter(
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/nn.py in _get_default_param_initializer()
    202             filter_elem_num = filter_size[0] * filter_size[
    203                 1] * self._num_channels
--> 204             std = (2.0 / filter_elem_num)**0.5
    205             return Normal(0.0, std, 0)
    206 
TypeError: unsupported operand type(s) for /: 'float' and 'str'

我的VGG网络定义如下：

class vgg_block(fluid.dygraph.Layer):
    def __init__(self, name_scope, num_convs, num_channels):
        """
        num_convs, 卷积层的数目
        num_channels, 卷积层的输出通道数，在同一个Incepition块内，卷积层输出通道数是一样的
        """
        super(vgg_block, self).__init__(name_scope)
        self.conv_list = []
        for i in range(num_convs):
            conv_layer = self.add_sublayer('conv_' + str(i), Conv2D(self.full_name(), 
                                        num_filters=num_channels, filter_size=3, padding=1, act='relu'))
            self.conv_list.append(conv_layer)
        self.pool = Pool2D(self.full_name(), pool_stride=2, pool_size = 2, pool_type='max')
    def forward(self, x):
        for item in self.conv_list:
            x = item(x)
        return self.pool(x)

class VGG(fluid.dygraph.Layer):
    def __init__(self, name_scope, conv_arch=((2, 64), 
                                (2, 128), (3, 256), (3, 512), (3, 512)), num_classes=1):
        super(VGG, self).__init__(name_scope)
        self.vgg_blocks=[]
        iter_id = 0
        # 添加vgg_block
        # 这里一共5个vgg_block，每个block里面的卷积层数目和输出通道数由conv_arch指定
        for (num_convs, num_channels) in conv_arch:
            block = self.add_sublayer('block_' + str(iter_id), 
                    vgg_block(self.full_name(), num_convs, num_channels))
            self.vgg_blocks.append(block)
            iter_id += 1
        self.fc1 = Linear(self.full_name(),
                      size=4096,
                      act='relu')
        self.drop1_ratio = 0.5
        self.fc2= Linear(self.full_name(),
                      size=4096,
                      act='relu')
        self.drop2_ratio = 0.5
        self.fc3 = Linear(self.full_name(),
                      size=num_classes,
                      )
    def forward(self, x):
        for item in self.vgg_blocks:
            x = item(x)
        x = fluid.layers.dropout(self.fc1(x), self.drop1_ratio)
        x = fluid.layers.dropout(self.fc2(x), self.drop2_ratio)
        x = self.fc3(x)
        return x

请教是哪里的问题？