Test.py

__author__ = 'oli'
import cPickle as pickle
import gzip
with gzip.open('mnist_4000.pkl.gz', 'rb') as f:
    (X,y) = pickle.load(f)
PIXELS = len(X[0,0,0,:])
X.shape, y.shape, PIXELS
from lasagne import layers
from lasagne import nonlinearities
from nolearn.lasagne import NeuralNet

net1 = NeuralNet(
    # Geometry of the network
    layers=[
        ('input', layers.InputLayer),
        ('conv1', layers.Conv2DLayer),
        ('pool1', layers.MaxPool2DLayer),
        ('conv2', layers.Conv2DLayer),
        ('pool2', layers.MaxPool2DLayer),
        ('hidden4', layers.DenseLayer),
        ('output', layers.DenseLayer),
        ],
    input_shape=(None, 1, PIXELS, PIXELS), #None in the first axis indicates that the batch size can be set later
    conv1_num_filters=32, conv1_filter_size=(3, 3), pool1_ds=(2, 2),
    conv2_num_filters=64, conv2_filter_size=(2, 2), pool2_ds=(2, 2),
    hidden4_num_units=500,
    output_num_units=10, output_nonlinearity=nonlinearities.softmax,

    # learning rate parameters
    update_learning_rate=0.01,
    update_momentum=0.9,
    regression=False,
    # We only train for 10 epochs
    max_epochs=2,
    verbose=1,

    # Training test-set split
    eval_size = 0.2
    )
net = net1.fit(X[0:1000,:,:,:],y[0:1000])

print("Hallo")