def sgd_optimize_logreg(datasets):
x = T.matrix('x') # the data is presented as rasterized images
y = T.ivector('y') # the labels are presented as 1D vector of [int] labels
classifier = LogisticRegression(input=x, n_in=28 * 28, n_out=10)
trainer = SGDTrainer(classifier, datasets)
trainer.build(x, y)
return trainer.train()
def sgd_optimize_mlp(datasets):
x = T.matrix('x') # the data is presented as rasterized images
y = T.ivector('y') # the labels are presented as 1D vector of [int] labels
classifier = MLP(input=x, n_in=28 * 28, n_out=10, n_hiddens=[1500])
trainer = SGDTrainer(classifier, datasets, learning_rate=0.01, L1_reg=0.0001,
L2_reg=0.001, n_epochs=500, batch_size=20)
trainer.build(x, y)
return trainer.train()
def sgd_optimize_lenet(datasets):
x = T.matrix('x') # the data is presented as rasterized images
y = T.ivector('y') # the labels are presented as 1D vector of [int] labels
classifier = LeNet(input=x, nkerns=[20, 50], filter_shapes=[[5, 5], [5, 5]],
image_shapes=[[28, 28], [12, 12]], batch_size=500,
n_hidden=[500], n_out=10)
trainer = SGDTrainer(classifier, datasets, learning_rate=0.01,
L1_reg=0.0001, L2_reg=0.001, n_epochs=100,
batch_size=classifier.batch_size)
trainer.build(x, y)
return trainer.train()
