Created by: rationme

def CSRNet(input,label):
    def conv_block(ipt, filter_list,pool_size = 2):
        return fluid.nets.img_conv_group(
        input=ipt,
        pool_size=pool_size,
        pool_stride=pool_size,
        conv_num_filter=filter_list,
        conv_filter_size=3,
        conv_act='relu',
        #conv_with_batchnorm=True,
        #conv_batchnorm_drop_rate=dropouts,
        pool_type='max')


    frontend_conv1 = conv_block(input,[64,64])

启动时 会报错

ValueErrorTraceback (most recent call last)
<ipython-input-27-3d678422d9b0> in <module>()
----> 1 train(r'/home/aaa/net/save')

<ipython-input-26-c9bf5f97e705> in train(save_dirname)
     10     y = fluid.layers.data(name='y', shape=[None, None, None, 1], dtype='float32')
     11 
---> 12     y_predict, l2_cost = CSRNet(x, y)
     13     sgd_optimizer = fluid.optimizer.SGD(learning_rate=1e-5)
     14 

<ipython-input-25-ef2b957dd1f2> in CSRNet(input, label)
     16 
     17 
---> 18     frontend_conv1 = conv_block(input,[64,64])
     19     frontend_conv2 = conv_block(frontend_conv1,[128,128])
     20     frontend_conv3 = conv_block(frontend_conv2 ,[256, 256,256])

<ipython-input-25-ef2b957dd1f2> in conv_block(ipt, filter_list, pool_size)
     13         #conv_with_batchnorm=True,
     14         #conv_batchnorm_drop_rate=dropouts,
---> 15         pool_type='max')
     16 
     17 

/home/aaa/anaconda2/lib/python2.7/site-packages/paddle/fluid/nets.pyc in img_conv_group(input, conv_num_filter, pool_size, conv_padding, conv_filter_size, conv_act, param_attr, conv_with_batchnorm, conv_batchnorm_drop_rate, pool_stride, pool_type, use_cudnn, use_mkldnn)
     96             act=local_conv_act,
     97             use_cudnn=use_cudnn,
---> 98             use_mkldnn=use_mkldnn)
     99 
    100         if conv_with_batchnorm[i]:

/home/aaa/anaconda2/lib/python2.7/site-packages/paddle/fluid/layers/nn.pyc in conv2d(input, num_filters, filter_size, stride, padding, dilation, groups, param_attr, bias_attr, use_cudnn, use_mkldnn, act, name)
   1273         shape=filter_shape,
   1274         dtype=dtype,
-> 1275         default_initializer=_get_default_param_initializer())
   1276 
   1277     pre_bias = helper.create_tmp_variable(dtype)

/home/aaa/anaconda2/lib/python2.7/site-packages/paddle/fluid/layers/nn.pyc in _get_default_param_initializer()
   1266 
   1267     def _get_default_param_initializer():
-> 1268         std = (2.0 / (filter_size[0]**2 * num_channels))**0.5
   1269         return Normal(0.0, std, 0)
   1270 

ValueError: negative number cannot be raised to a fractional power