tf.keras.layers.LocallyConnected2D     View source on GitHub    Locally-connected layer for 2D inputs. Inherits From: Layer, Module  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.keras.layers.LocallyConnected2D  
tf.keras.layers.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, implementation=1, **kwargs
)
 The LocallyConnected2D layer works similarly to the Conv2D layer, except that weights are unshared, that is, a different set of filters is applied at each different patch of the input. 
Note: layer attributes cannot be modified after the layer has been called once (except the trainable attribute).
 Examples: # apply a 3x3 unshared weights convolution with 64 output filters on a
32x32 image
# with `data_format="channels_last"`:
model = Sequential()
model.add(LocallyConnected2D(64, (3, 3), input_shape=(32, 32, 3)))
# now model.output_shape == (None, 30, 30, 64)
# notice that this layer will consume (30*30)*(3*3*3*64) + (30*30)*64
parameters

# add a 3x3 unshared weights convolution on top, with 32 output filters:
model.add(LocallyConnected2D(32, (3, 3)))
# now model.output_shape == (None, 28, 28, 32)

 


 Arguments
  filters   Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).  
  kernel_size   An integer or tuple/list of 2 integers, specifying the width and height of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions.  
  strides   An integer or tuple/list of 2 integers, specifying the strides of the convolution along the width and height. Can be a single integer to specify the same value for all spatial dimensions.  
  padding   Currently only support "valid" (case-insensitive). "same" will be supported in future. "valid" means no padding.  
  data_format   A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch, height, width, channels) while channels_first corresponds to inputs with shape (batch, channels, height, width). It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json. If you never set it, then it will be "channels_last".  
  activation   Activation function to use. If you don't specify anything, no activation is applied (ie. "linear" activation: a(x) = x).  
  use_bias   Boolean, whether the layer uses a bias vector.  
  kernel_initializer   Initializer for the kernel weights matrix.  
  bias_initializer   Initializer for the bias vector.  
  kernel_regularizer   Regularizer function applied to the kernel weights matrix.  
  bias_regularizer   Regularizer function applied to the bias vector.  
  activity_regularizer   Regularizer function applied to the output of the layer (its "activation").  
  kernel_constraint   Constraint function applied to the kernel matrix.  
  bias_constraint   Constraint function applied to the bias vector.  
  implementation   implementation mode, either 1, 2, or 3. 1 loops over input spatial locations to perform the forward pass. It is memory-efficient but performs a lot of (small) ops. 2 stores layer weights in a dense but sparsely-populated 2D matrix and implements the forward pass as a single matrix-multiply. It uses a lot of RAM but performs few (large) ops. 3 stores layer weights in a sparse tensor and implements the forward pass as a single sparse matrix-multiply. How to choose: 1: large, dense models, 2: small models, 3: large, sparse models, where "large" stands for large input/output activations (i.e. many filters, input_filters, large np.prod(input_size), np.prod(output_size)), and "sparse" stands for few connections between inputs and outputs, i.e. small ratio `filters * input_filters * np.prod(kernel_size) / (np.prod(input_size)  np.prod(strides)), where inputs to and outputs of the layer are assumed to have shapesinput_size + (input_filters,),output_size + (filters,)` respectively.  It is recommended to benchmark each in the setting of interest to pick the most efficient one (in terms of speed and memory usage). Correct choice of implementation can lead to dramatic speed improvements (e.g. 50X), potentially at the expense of RAM. Also, only padding="valid" is supported by implementation=1. 
   Input shape: 4D tensor with shape: (samples, channels, rows, cols) if data_format='channels_first' or 4D tensor with shape: (samples, rows, cols, channels) if data_format='channels_last'. Output shape: 4D tensor with shape: (samples, filters, new_rows, new_cols) if data_format='channels_first' or 4D tensor with shape: (samples, new_rows, new_cols, filters) if data_format='channels_last'. rows and cols values might have changed due to padding.