def build_simple_block(incoming_layer, names,
num_filters, filter_size, stride, pad,
use_bias=False, nonlin=rectify):
"""Creates stacked Lasagne layers ConvLayer -> BN -> (ReLu)
Parameters:
----------
incoming_layer : instance of Lasagne layer
Parent layer
names : list of string
Names of the layers in block
num_filters : int
Number of filters in convolution layer
filter_size : int
Size of filters in convolution layer
stride : int
Stride of convolution layer
pad : int
Padding of convolution layer
use_bias : bool
Whether to use bias in conlovution layer
nonlin : function
Nonlinearity type of Nonlinearity layer
Returns
-------
tuple: (net, last_layer_name)
net : dict
Dictionary with stacked layers
last_layer_name : string
Last layer name
"""
net = []
net.append((
names[0],
ConvLayer(incoming_layer, num_filters, filter_size, stride, pad,
flip_filters=False, nonlinearity=None) if use_bias
else ConvLayer(incoming_layer, num_filters, filter_size, stride, pad, b=None,
flip_filters=False, nonlinearity=None)
))
net.append((
names[1],
BatchNormLayer(net[-1][1])
))
if nonlin is not None:
net.append((
names[2],
NonlinearityLayer(net[-1][1], nonlinearity=nonlin)
))
return dict(net), net[-1][0]
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