def train(self):
# self.trainBuntton["state"] = 'disabled'
#sizes, epochs, eta, update_queue, validation_threshold, error_threshold
trainingPercent = int(self.trainingPercent.get())
epochs = int(self.epochs.get())
et = float(self.error_threshold.get())
vt = int(self.validation_threshold.get())
poolinRule = self.poolingRule.get()
mini_batch_size = int(self.mini_batch_size.get())
try:
import pickle
weights = pickle.load(open(self.address.get(), "rb"))
except:
weights = None
if not self.plotter.is_alive():
self.plotter.start()
if self.cnn is not None:
if self.cnn.exitcode != 0:
print 'Another learner is working.'
return
self.cnn.terminate()
# # self.cnn = CNN.Network(sizes, epochs, eta, mu, self.updatesQueue, vt, et, widrow, bs, activationFunc, weights)
# self.cnn = CNN.Network([CNN.ConvPoolLayer(image_shape=(mini_batch_size, 1, 28, 28), filter_shape=(20, 1, 5, 5), poolsize=(2, 2)),
# CNN.FullyConnectedLayer(n_in=20*12*12, n_out=100),
# CNN.SoftmaxLayer(n_in=100, n_out=10)], mini_batch_size, self.updatesQueue)
self.cnn = CNN.Network([
CNN.ConvPoolLayer(image_shape=(mini_batch_size, 1, 28, 28),
filter_shape=(20, 1, 5, 5),
poolsize=(2, 2)),
CNN.ConvPoolLayer(image_shape=(mini_batch_size, 20, 12, 12),
filter_shape=(30, 20, 5, 5),
poolsize=(2, 2)),
CNN.ConvPoolLayer(image_shape=(mini_batch_size, 30, 4, 4),
filter_shape=(40, 30, 2, 2),
poolsize=(3, 3)),
CNN.FullyConnectedLayer(n_in=40 * 1 * 1, n_out=100),
CNN.SoftmaxLayer(n_in=100, n_out=10)
], mini_batch_size, self.updatesQueue)
self.cnn.start()
