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September 7, 2015 14:57
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ipython_test.py
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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) |
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