run2

This is an example nolearn NeuralNet to do supervised learning. This network is being used to do supervised learning on black and white images, which are 135 * 240 in size. The same network can be modified to bgr images just by modifying the input_shape to None,3,..,.. and output_num_unit to 3*135 * 240

net_unsupervised_bw = NeuralNet(
    layers=[
        ('input', layers.InputLayer),
        ('noise1', GaussianNoiseLayer),
        ('conv1', Conv2DLayer),
        ('pool1', MaxPool2DLayer),
        ('dropout1', layers.DropoutLayer),  # !
        ('conv2', Conv2DLayer),

run2

    def load_weights_from_by_layers(self, source):
        self._output_layer = self.initialize_layers()

        if isinstance(source, str):
            source = np.load(source)

        sourcelayers = {'Conv2DLayer':[],'DenseLayer':[]}
        for l in source.get_all_layers():
            if 'Conv2DLayer' in str(type(l)):
                sourcelayers['Conv2DLayer'].append(l)