Question

我希望我的网络在训练时即时计算加权标准偏差（或方差）。

权重必须来自常数向量，例如：

PTRACE_CONT

输入的大小与weights = np.array([0.1,0.4,0.5,0.6,0.1,0.3,0.6,0.9])相同。

我如何在Keras中做到这一点？

我已经得出了均值的公式

weights

但是即使崩溃也会导致错误：

weights = K.variable(weights)
width = dot([in, weights],axes=-1, normalize=False)

编辑：

我弄错了，我想用输入作为权重来计算常数向量的方差。

我遵循@Sharky的建议：

File "/sps/atlas/a/aghosh/miniconda3/envs/cpuApril19/lib/python2.7/site-packages/keras/layers/merge.py", line 668, in dot
return Dot(axes=axes, normalize=normalize, **kwargs)(inputs)
File "~/envs/cpuApril19/lib/python2.7/site-packages/keras/engine/base_layer.py", line 474, in __call__
output_shape = self.compute_output_shape(input_shape)
File "~/envs/cpuApril19/lib/python2.7/site-packages/keras/layers/merge.py", line 512, in compute_output_shape
shape2.pop(0)
IndexError: pop from empty list

它给出此错误： AttributeError：“ NoneType”对象没有属性“ _inbound_nodes”

如果将张量切换为：

constVector = np.array([-0.1,-0.4,-0.5,0.6,0.1,0.3,0.6,0.9])
....
in = Input(shape=(8,), name='Input')
width = Lambda(lambda x: tf.nn.weighted_moments(x,axes=-1,frequency_weights=in)[1])(constVector)
Model = Model(inputs=[in], outputs= width)

它可以编译，但是我需要对tf.nn.weighted_moments(x,axes=-1,frequency_weights=constVector)[1])(in )加权的constVector进行变异

Edit2：只需正确实施Lamda层

in

Answer 1

在@Sharky的评论之后，我将TensorFlow的weighted_moments()函数包装在Keras中：

constVector = np.array([-0.1,-0.4,-0.5,0.6,0.1,0.3,0.6,0.9])
constVector = K.variable(constVector)
def wWidthFunc(x, y=constVector):
    # only works for 1-D vector inputs
    z = tf.nn.weighted_moments(y,axes=[1],frequency_weights=x)[1]
    z = K.expand_dims(z, -1)        # z of shape (batch_size, 1)
    return z
def make_width_model():
    m_input = Input(shape=(8,), name='Input_')
    width = Lambda(wWidthFunc)(m_input) # m_input values must be >0
    M_w = Model(inputs=[m_input], outputs= width)
    return M_w

计算加权标准差？

1 个答案: