plt.imshow(feature.reshape(k, shp[0], shp[1])[0], interpolation='None', cmap='binary')
plt.show()
def dump2file(cnn, filename):
global datasets_for_abstract
x = datasets_for_abstract[0][0]
y = datasets_for_abstract[0][1].eval()
features = cnn.get_feature(x)
print features.shape
print y.shape
cPickle.dump((features, y), open(filename, 'wb'))
cnn = CNN(dim_in = 1, size_in = (28, 28), nkerns = [(4, (2, 2), (1, 1)), (2, (3, 3), (2, 2))])
#fit(self, lossf, datasets, batch_size = 500, n_epochs = 200, learning_rate = 0.01):
# examinate(cnn)
# dump2file(cnn, './features/1_random.feature')
examinate(cnn)
cnn.set_lossf(core.loss_functions.TEST_LOSS_F)
cnn.fit(datasets, batch_size = 200, n_epochs = 100, learning_rate = 0.0001)
examinate(cnn)
# dump2file(cnn, './features/1_trained.feature')
features = features0 - features1
cPickle.dump((features, y), open(filename, 'wb'))
cnn1 = CNN(dim_in = 1, size_in = (32, 32), nkerns = [(8, (2, 2), (1, 1)), (6, (3, 3), (2, 2))])
cnn2 = CNN(dim_in = 1, size_in = (32, 32), nkerns = [(8, (2, 2), (1, 1)), (6, (3, 3), (2, 2))])
#fit(self, lossf, datasets, batch_size = 500, n_epochs = 200, learning_rate = 0.01):
# examinate(cnn)
examinate(cnn1, cnn2)
dump2file(cnn1, cnn2, './features/1_random.feature')
for i in range(4):
print 'The %03dth updating' % i
loss = core.loss_functions.lossf3(cnn1, cnn2.get_feature(datasets1[0][0]))
cnn1.set_lossf(loss)
cnn1.fit(datasets0, batch_size = 50, n_epochs = 5, learning_rate = 0.00001, test_model_on = 0)
loss = core.loss_functions.lossf3(cnn2, cnn1.get_feature(datasets1[0][0]))
cnn2.set_lossf(loss)
cnn2.fit(datasets1, batch_size = 50, n_epochs = 5, learning_rate = 0.00001, test_model_on = 0)
examinate(cnn1, cnn2)
dump2file(cnn1, cnn2, './features/1_trained.feature')