- class lasagne.layers.LocallyConnected2DLayer(incoming, num_filters, filter_size, stride=(1, 1), pad='same', untie_biases=False, W=lasagne.init.GlorotUniform(), b=lasagne.init.Constant(0.), nonlinearity=lasagne.nonlinearities.rectify, flip_filters=True, channelwise=False, **kwargs)¶
2D locally connected layer
Performs an operation similar to a 2D convolution but without the weight sharing, then optionally adds a bias and applies an elementwise nonlinearity.
incoming : a Layer instance or a tuple
The layer feeding into this layer, or the expected input shape. The output of this layer should be a 4D tensor, with shape (batch_size, num_input_channels, input_rows, input_columns).
num_filters : int
The number of learnable convolutional filters this layer has.
filter_size : int or iterable of int
An integer or a 2-element tuple specifying the size of the filters.
stride : int or iterable of int
An integer or a 2-element tuple specifying the stride of the convolution operation. This implementation only supports unit stride, the argument is provided for compatibility to convolutional layers only.
pad : int, iterable of int, or ‘valid’ (default: ‘same’)
The amount of implicit zero padding of the input. This implementation only supports ‘same’ padding, the argument is provided for compatibility to other convolutional layers only.
untie_biases : bool (default: False)
If False, the layer will have a bias parameter for each channel, which is shared across all positions in this channel. As a result, the b attribute will be a vector (1D).
If True, the layer will have separate bias parameters for each position in each channel. As a result, the b attribute will be a 3D tensor.
W : Theano shared variable, expression, numpy array or callable
Initial value, expression or initializer for the weights. If channelwise is set to False, the weights should be a 6D tensor with shape (num_filters, num_input_channels, filter_rows, filter_columns, output_rows, output_columns). If channelwise is set to True, the weights should be a 5D tensor with shape (num_filters, filter_rows, filter_columns, output_rows, output_columns). See lasagne.utils.create_param() for more information.
b : Theano shared variable, expression, numpy array, callable or None
Initial value, expression or initializer for the biases. If set to None, the layer will have no biases. Otherwise, biases should be a 1D array with shape (num_filters,) if untied_biases is set to False. If it is set to True, its shape should be (num_filters, output_rows, output_columns) instead. See lasagne.utils.create_param() for more information.
nonlinearity : callable or None
The nonlinearity that is applied to the layer activations. If None is provided, the layer will be linear.
flip_filters : bool (default: True)
Whether to flip the filters before multiplying them over the input, similar to a convolution (this is the default), or not to flip them, similar to a correlation.
channelwise : bool (default: False)
If False, each filter interacts will all of the input channels as in a convolution. If True, each filter only interacts with the corresponding input channel. That is, each output channel only depends on its filter and on the input channel at the same channel index. In this case, the number of output channels (i.e. number of filters) should be equal to the number of input channels.
Any additional keyword arguments are passed to the Layer superclass.
When channelwise is set to True and the number of filters differs from the number of input channels, a ValueError is raised.
This implementation computes the output tensor by iterating over the filter weights and multiplying them with shifted versions of the input tensor. This implementation assumes no stride, ‘same’ padding and no dilation.
W (Theano shared variable or expression) Variable or expression representing the filter weights. b (Theano shared variable or expression) Variable or expression representing the biases.