未能在keras中添加可训练的_重量

Question

我试图通过编写一个新的层，在一个标准的完全连接的神经网络中实现层规范化。我复制了Dense层的几乎所有代码，并添加了layer normalization函数和相应的参数。我的代码如下：

class DenseLN(Layer):
    def __init__(self, output_dim, init='glorot_uniform', activation='linear', weights=None,
             W_regularizer=None, b_regularizer=None, activity_regularizer=None,
             W_constraint=None, b_constraint=None, bias=True, input_dim=None, gamma_init=1., **kwargs):
    self.init = initializations.get(init)
    self.activation = activations.get(activation)
    self.output_dim = output_dim
    self.input_dim = input_dim

    def gamma_init_func(shape, c=gamma_init):
        if c == 1.:
            return initializations.get('one')(shape)
        return K.variable(np.ones(shape) * c, **kwargs)
    self.gamma_init = gamma_init_func
    self.beta_init = initializations.get('zero')
    self.epsilon = 1e-5        

    self.W_regularizer = regularizers.get(W_regularizer)
    self.b_regularizer = regularizers.get(b_regularizer)
    self.activity_regularizer = regularizers.get(activity_regularizer)

    self.W_constraint = constraints.get(W_constraint)
    self.b_constraint = constraints.get(b_constraint)

    self.bias = bias
    self.initial_weights = weights
    self.input_spec = [InputSpec(ndim=2)]

    if self.input_dim:
        kwargs['input_shape'] = (self.input_dim,)
    super(DenseLN, self).__init__(**kwargs)

def ln(self, x):
    m = K.mean(x, axis=-1, keepdims=True)
    std = K.sqrt(K.var(x, axis=-1, keepdims=True) + self.epsilon)
    x_normed = (x - m) / (std + self.epsilon)
    x_normed = self.gamma * x_normed + self.beta
    return x_normed

def build(self, input_shape):
    assert len(input_shape) == 2
    input_dim = input_shape[1]
    self.input_spec = [InputSpec(dtype=K.floatx(),
                                 shape=(None, input_dim))]

    self.gamma = self.gamma_init(input_dim)
    self.beta = self.beta_init(input_dim)

    self.W = self.init((input_dim, self.output_dim),
                       name='{}_W'.format(self.name))
    if self.bias:
        self.b = K.zeros((self.output_dim,),
                         name='{}_b'.format(self.name))
        self.trainable_weights = [self.W, self.gamma, self.beta, self.b]
    else:
        self.trainable_weights = [self.W, self.gamma, self.beta]
    self.regularizers = []
    if self.W_regularizer:
        self.W_regularizer.set_param(self.W)
        self.regularizers.append(self.W_regularizer)

    if self.bias and self.b_regularizer:
        self.b_regularizer.set_param(self.b)
        self.regularizers.append(self.b_regularizer)

    if self.activity_regularizer:
        self.activity_regularizer.set_layer(self)
        self.regularizers.append(self.activity_regularizer)

    self.constraints = {}
    if self.W_constraint:
        self.constraints[self.W] = self.W_constraint
    if self.bias and self.b_constraint:
        self.constraints[self.b] = self.b_constraint

    if self.initial_weights is not None:
        self.set_weights(self.initial_weights)
        del self.initial_weights

def call(self, x, mask=None):
    output = K.dot(x, self.W)
    #output = self.ln(output)
    if self.bias:
        output += self.b
    return self.activation(output)

def get_output_shape_for(self, input_shape):
    assert input_shape and len(input_shape) == 2
    return (input_shape[0], self.output_dim)

但在fit期间，它得到了一个TypeError:unorderable types: NoneType() < NoneType()。根据日志消息，原因似乎是trainable_weights：

^{pr2}$

你能告诉我我做错什么了吗？我该怎么解决它？提前谢谢你！在

编辑：

以下是构建模型并进行拟合的代码：

model = Sequential()
model.add(DenseLN(12, input_dim=12))
model.add(Activation('relu'))
#model.add(Dropout(0.5))
model.add(DenseLN(108))
model.add(Activation('relu'))
model.add(DenseLN(108))
model.add(Activation('relu'))
model.add(Dense(1))
model.add(Activation('relu'))
adadelta = Adadelta(lr=0.1, rho=0.95, epsilon=1e-08)
adagrad = Adagrad(lr=0.003, epsilon=1e-08)

model.compile(loss='poisson',
             optimizer=adagrad,
             metrics=['accuracy'])

model.fit(X_train_scale,
          Y_train,
          batch_size=3000,
          nb_epoch=300)

编辑2：

我将name属性添加到beta和gamma中，似乎源代码错误已经修复，但是又发生了另一个错误。编辑后的代码和新的日志消息如下：

self.gamma = K.ones((output_dim,), name='{}_gamma'.format(self.name))
self.beta = K.zeros((output_dim,), name='{}_beta'.format(self.name))

DisconnectedInputError:  
Backtrace when that variable is created:

  File "/home/lcc/anaconda3/envs/sensequant/lib/python3.5/site-packages/IPython/core/interactiveshell.py", line 2809, in run_ast_nodes
    if self.run_code(code, result):
  File "/home/lcc/anaconda3/envs/sensequant/lib/python3.5/site-packages/IPython/core/interactiveshell.py", line 2869, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-464-a90d5bdc38d3>", line 15, in <module>
    model.add(DenseLN(108))
  File "/home/lcc/anaconda3/envs/sensequant/lib/python3.5/site-packages/keras/models.py", line 146, in add
    output_tensor = layer(self.outputs[0])
  File "/home/lcc/anaconda3/envs/sensequant/lib/python3.5/site-packages/keras/engine/topology.py", line 458, in __call__
    self.build(input_shapes[0])
  File "<ipython-input-463-901584f9945c>", line 46, in build
    self.beta = K.zeros((output_dim,), name='{}_beta'.format(self.name))
  File "/home/lcc/anaconda3/envs/sensequant/lib/python3.5/site-packages/keras/backend/theano_backend.py", line 77, in zeros
    return variable(np.zeros(shape), dtype, name)
  File "/home/lcc/anaconda3/envs/sensequant/lib/python3.5/site-packages/keras/backend/theano_backend.py", line 31, in variable
    return theano.shared(value=value, name=name, strict=False)