ipython_test.py

ipython_test.py

import theano
import sklearn.datasets
# Generate synthetic data
N_CLASSES = 3
X, y = sklearn.datasets.make_classification(n_features=2, n_redundant=0,
                                            n_classes=N_CLASSES, n_clusters_per_class=1)
# Convert to theano floatX
X = X.astype(theano.config.floatX)
# Labels should be ints
y = y.astype('int32')

from lasagne.layers import *
from lasagne.nonlinearities import *
layers = [
    (InputLayer, {'shape': X.shape}),
    (DenseLayer, {'num_units': N_CLASSES, 'nonlinearity': softmax} ),
]

from nolearn.lasagne import NeuralNet
from lasagne.objectives import *
from lasagne.updates import *
net = NeuralNet(
    layers=layers,
    max_epochs = 10,
    
    update=sgd,
    update_learning_rate=1,
    
    verbose=0,
)
_ = net.fit(X, y)

import matplotlib.pyplot as plt
%matplotlib inline
from nolearn.lasagne.visualize import plot_loss
plot_loss(net)