LocallyConnceted 局部连接层

LocallyConnnected和Conv差不多，只是Conv每层共享卷积核， 这里不同位置卷积核独立

LocallyConnected1D层

keras.layers.local.LocallyConnected1D(
	filters,
	kernel_size,
	strides=1,
	padding="valid",
	data_format=None,
	activation=None,
	use_bias=True,
	kernel_initializer="glorot_uniform",
	bias_initializer="zeros",
	kernel_regularizer=None,
	bias_regularizer=None,
	activity_regularizer=None,
	kernel_constraint=None,
	bias_constraint=None
)

类似于Conv1D，单卷积核权重不共享

LocallyConnected2D层

keras.layers.local.LocallyConnected2D(
	filters,
	kernel_size,
	strides=(1, 1),
	padding="valid",
	data_format=None,
	activation=None,
	use_bias=True,
	kernel_initializer="glorot_uniform",
	bias_initializer="zeros",
	kernel_regularizer=None,
	bias_regularizer=None,
	activity_regularizer=None,
	kernel_constraint=None,
	bias_constraint=None
)

类似Conv2D，区别是不进行权值共享

• 说明
  • 输出的行列数可能会因为填充方法而改变
• 例

  model = Sequential()
  model.add(LocallyConnected2D(64, (3, 3), input_shape=(32, 32, 3)))
  	# apply a 3x3 unshared weights convolution with 64 output filters on a 32x32 image
  	# with `data_format="channels_last"`:
  	# now model.output_shape == (None, 30, 30, 64)
  	# notice that this layer will consume (30*30)*(3*3*3*64) + (30*30)*64 parameters
  
  model.add(LocallyConnected2D(32, (3, 3)))
  	# now model.output_shape == (None, 28, 28, 32)
  	# add a 3x3 unshared weights convolution on top, with 32 output filters: