Question

我正在使用Keras构建卷积神经网络，并希望在最后一个完全连接的层之前添加一个具有数据标准差的节点。

以下是重现错误的最低代码：

from keras.layers import merge, Input, Dense
from keras.layers import Convolution1D, Flatten
from keras import backend as K

input_img = Input(shape=(64, 4))

x = Convolution1D(48, 3, activation='relu', init='he_normal')(input_img)
x = Flatten()(x)

std = K.std(input_img, axis=1)
x = merge([x, std], mode='concat', concat_axis=1)

output =  Dense(100, activation='softmax', init='he_normal')(x)

这导致以下TypeError：

-----------------------------------------------------------------
TypeError                       Traceback (most recent call last)
<ipython-input-117-c1289ebe610e> in <module>()
      6 x = merge([x, std], mode='concat', concat_axis=1)
      7 
----> 8 output =  Dense(100, activation='softmax', init='he_normal')(x)

/home/ubuntu/anaconda2/envs/tensorflow/lib/python2.7/site-packages/keras/engine/topology.pyc in __call__(self, x, mask)
    486                                     '`layer.build(batch_input_shape)`')
    487             if len(input_shapes) == 1:
--> 488                 self.build(input_shapes[0])
    489             else:
    490                 self.build(input_shapes)

/home/ubuntu/anaconda2/envs/tensorflow/lib/python2.7/site-packages/keras/layers/core.pyc in build(self, input_shape)
    701 
    702         self.W = self.init((input_dim, self.output_dim),
--> 703                            name='{}_W'.format(self.name))
    704         if self.bias:
    705             self.b = K.zeros((self.output_dim,),

/home/ubuntu/anaconda2/envs/tensorflow/lib/python2.7/site-packages/keras/initializations.pyc in he_normal(shape, name, dim_ordering)
     64     '''
     65     fan_in, fan_out = get_fans(shape, dim_ordering=dim_ordering)
---> 66     s = np.sqrt(2. / fan_in)
     67     return normal(shape, s, name=name)
     68 

TypeError: unsupported operand type(s) for /: 'float' and 'NoneType'

知道为什么吗？

Answer 1

std不是Keras图层，因此它不满足图层输入/输出形状界面。解决方案是使用Lambda包裹K.std：

的图层

from keras.layers import merge, Input, Dense, Lambda
from keras.layers import Convolution1D, Flatten
from keras import backend as K

input_img = Input(shape=(64, 4))

x = Convolution1D(48, 3, activation='relu', init='he_normal')(input_img)
x = Flatten()(x)

std = Lambda(lambda x: K.std(x, axis=1))(input_img)
x = merge([x, std], mode='concat', concat_axis=1)

output =  Dense(100, activation='softmax', init='he_normal')(x)

在Keras中合并变量

1 个答案: