MNIST CNN model with Lasagne

The Lasagne library has packaged layers and tools to handle neural nets easily. Let's first install the latest version of Lasagne:

pip install --upgrade https://github.com/Lasagne/Lasagne/archive/master.zip

Let us reprogram the MNIST model from Chapter 2, Classifying Handwritten Digits with a Feedforward Network with Lasagne:

def model(l_input, input_dim=28, num_units=256, num_classes=10, p=.5):


    network = lasagne.layers.Conv2DLayer(
            l_input, num_filters=32, filter_size=(5, 5),
            nonlinearity=lasagne.nonlinearities.rectify,
            W=lasagne.init.GlorotUniform())

    network = lasagne.layers.MaxPool2DLayer(network, pool_size=(2, 2))

    network = lasagne.layers.Conv2DLayer(
            network, num_filters=32, filter_size=(5, 5),
            nonlinearity=lasagne.nonlinearities.rectify)

    network = lasagne.layers.MaxPool2DLayer(network, pool_size=(2, 2))

    if num_units > 0:
        network = lasagne.layers.DenseLayer(
...