Crappy Version

network = NeuralNet(
            layers=[('input', layers.InputLayer),
                    ('conv2d1', layers.Conv2DLayer),
                    ('maxpool1', layers.MaxPool2DLayer),
                    ('conv2d2', layers.Conv2DLayer),
                    ('maxpool2', layers.MaxPool2DLayer),
                    ('dropout1', layers.DropoutLayer),
                    ('dense', layers.DenseLayer),
                    ('dropout2', layers.DropoutLayer),
                    ('output', layers.DenseLayer),
                    ],
            # input layer
            input_shape=(1, self.width, self.height),
            # layer conv2d1
            conv2d1_num_filters=32,
            conv2d1_filter_size=(5, 5),
            conv2d1_nonlinearity=lasagne.nonlinearities.rectify,
            conv2d1_W=lasagne.init.GlorotUniform(),
            # layer maxpool1
            maxpool1_pool_size=(2, 2),
            # layer conv2d2
            conv2d2_num_filters=32,
            conv2d2_filter_size=(5, 5),
            conv2d2_nonlinearity=lasagne.nonlinearities.rectify,
            # layer maxpool2
            maxpool2_pool_size=(2, 2),
            # dropout1
            dropout1_p=0.5,
            # dense
            dense_num_units=256,
            dense_nonlinearity=lasagne.nonlinearities.rectify,
            # dropout2
            dropout2_p=0.5,
            # output
            output_nonlinearity=lasagne.nonlinearities.softmax,
            output_num_units=7,
            # optimization method params
            regression=False,
            update=nesterov_momentum,
            update_learning_rate=0.01,
            update_momentum=0.9,
            max_epochs=10,
            verbose=1,
        )