据我所知，Keras中的Conv2D中有几个kernel_constraint选项：max_norm，non_neg或unit_norm ..
但我需要的是将滤波器内核中的锚点（中心）位置设置为零。 例如，如果我们有一个大小为（width，height）=（5,5）的滤波器内核，并且输入中有3个通道。我需要将每个通道的内核的锚（中心）点约束为0，如w（2,2，:) = 0，假设我们将通道尺寸设为第3维。如果有多个过滤器，则每个过滤器的锚位置应为零。我怎么能实现这个呢？
我假设需要一个自定义内核约束。此链接提供了如何创建一个继承自Constraint的类的建议：https://github.com/keras-team/keras/issues/8196。这显示了如何实现内置约束： https://github.com/keras-team/keras/blob/master/keras/constraints.py
但是，我仍然不知道如何操纵w的尺寸，以及如何将所需位置设置为零。任何帮助表示赞赏。感谢。
更新：
DanielMöller的回答经过了尝试。错误信息如下：
提高ValueError（'一个操作对渐变有None。
ValueError：对于渐变，操作具有None。请确保您的所有操作都定义了渐变（即可区分）。没有渐变的常见操作：K.argmax，K.round，K.eval。
由于丹尼尔可以在没有问题的情况下运行，为了检查我的程序中出了什么问题，我在这里发布我的简化代码。我的数据有8个频道，但不管你有多少频道都没关系。
from keras.layers import Input, Conv2D
from keras.models import Model, optimizers
import numpy as np
import tensorflow as tf
from keras import backend as K
from keras.callbacks import ModelCheckpoint
class ZeroCenterConv2D(Conv2D):
def __init__(self, filters, kernel_size, **kwargs):
super(ZeroCenterConv2D, self).__init__(filters, kernel_size, **kwargs)
def call(self, inputs):
assert self.kernel_size[0] % 2 == 1, "Error: the kernel size is an even number"
assert self.kernel_size[1] % 2 == 1, "Error: the kernel size is an even number"
centerX = (self.kernel_size[0] - 1) // 2
centerY = (self.kernel_size[1] - 1) // 2
kernel_mask = np.ones(self.kernel_size + (1, 1))
kernel_mask[centerX, centerY] = 0
kernel_mask = K.constant(kernel_mask)
customKernel = self.kernel * kernel_mask
outputs = K.conv2d(
inputs,
customKernel,
strides=self.strides,
padding=self.padding,
data_format=self.data_format,
dilation_rate=self.dilation_rate)
if self.activation is not None:
return self.activation(outputs)
return outputs
size1 = 256
size2 = 256
input_img = Input(shape=(size1, size2, 8))
conv1 = ZeroCenterConv2D(8, (5, 5), padding='same', activation='relu')(input_img)
autoencoder = Model(input_img, conv1)
adam = optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-8)
autoencoder.compile(optimizer=adam, loss='mean_squared_error')
import scipy.io
A = scipy.io.loadmat('data_train')
x_train = A['data']
x_train = np.reshape(x_train, (1, 256, 256, 8))
from keras.callbacks import TensorBoard
autoencoder.fit(x_train, x_train,
epochs=5,
batch_size=1,
shuffle=False,
validation_data=(x_train, x_train),
callbacks=[TensorBoard(log_dir='/tmp/autoencoder')])
decoded_imgs = autoencoder.predict(x_train)
当conv1 = ZeroCenterConv2D ...被传统的conv1 = Conv2D取代......时，一切正常。
完整的错误消息：
Connected to pydev debugger (build 181.4668.75)
/home/allen/kerasProject/keras/venv/py2.7/local/lib/python2.7/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
from ._conv import register_converters as _register_converters
Using TensorFlow backend.
Traceback (most recent call last):
File "/snap/pycharm-community/60/helpers/pydev/pydevd.py", line 1664, in <module>
main()
File "/snap/pycharm-community/60/helpers/pydev/pydevd.py", line 1658, in main
globals = debugger.run(setup['file'], None, None, is_module)
File "/snap/pycharm-community/60/helpers/pydev/pydevd.py", line 1068, in run
pydev_imports.execfile(file, globals, locals) # execute the script
File "/home/allen/autotion/temptest", line 62, in <module>
callbacks=[TensorBoard(log_dir='/tmp/autoencoder')])
File "/home/allen/kerasProject/keras/venv/py2.7/local/lib/python2.7/site-packages/keras/engine/training.py", line 1682, in fit
self._make_train_function()
File "/home/allen/kerasProject/keras/venv/py2.7/local/lib/python2.7/site-packages/keras/engine/training.py", line 992, in _make_train_function
loss=self.total_loss)
File "/home/allen/kerasProject/keras/venv/py2.7/local/lib/python2.7/site-packages/keras/legacy/interfaces.py", line 91, in wrapper
return func(*args, **kwargs)
File "/home/allen/kerasProject/keras/venv/py2.7/local/lib/python2.7/site-packages/keras/optimizers.py", line 445, in get_updates
grads = self.get_gradients(loss, params)
File "/home/allen/kerasProject/keras/venv/py2.7/local/lib/python2.7/site-packages/keras/optimizers.py", line 80, in get_gradients
raise ValueError('An operation has `None` for gradient. '
ValueError: An operation has `None` for gradient. Please make sure that all of your ops have a gradient defined (i.e. are differentiable). Common ops without gradient: K.argmax, K.round, K.eval.
Process finished with exit code 1
进一步更新：在Daniel的答案（已经完成）中的代码中添加“偏见”部分，问题解决了！
答案 0 :(得分：0)
您需要一个自定义的Conv2D图层，您可以在其中更改其调用方法以在中心应用零。
class ZeroCenterConv2D(Conv2D):
def __init__(self, filters, kernel_size, **kwargs):
super(ZeroCenterConv2D, self).__init__(filters, kernel_size, **kwargs)
def call(self, inputs):
assert self.kernel_size[0] % 2 == 1, "Error: the kernel size is an even number"
assert self.kernel_size[1] % 2 == 1, "Error: the kernel size is an even number"
centerX = (self.kernel_size[0] - 1) // 2
centerY = (self.kernel_size[1] - 1) // 2
kernel_mask = np.ones(self.kernel_size + (1, 1))
kernel_mask[centerX, centerY] = 0
kernel_mask = K.variable(kernel_mask)
customKernel = self.kernel * kernel_mask
outputs = K.conv2d(
inputs,
customKernel,
strides=self.strides,
padding=self.padding,
data_format=self.data_format,
dilation_rate=self.dilation_rate)
if self.use_bias:
outputs = K.bias_add(
outputs,
self.bias,
data_format=self.data_format)
if self.activation is not None:
return self.activation(outputs)
return outputs
但这并不会取代实际的重量，但中心的重量永远不会被使用。
当您使用layer.get_weights()的{{1}}时，您会看到初始化时的中心权重（不是零）。