class AutoEncoder1:
def __init__(self, nIn=700, nHidden=500):
# declare layers
ilayer = Layer(numNodes=nIn, ltype = Layer.Layer_Type_Input, id="0")
hlayer = Layer(numNodes=nHidden, ltype = Layer.Layer_Type_Hidden, id="1")
olayer = Layer(numNodes=nIn, ltype = Layer.Layer_Type_Output, id="2")
# declare nnnet
self.net = NNNet(ilayer, hlayer, olayer, cost_function=CrossEntroyCostFunction)
# change parameter constraint
conn1 = self.net.connections[0]
conn2 = self.net.connections[1]
self.net.params.remove(conn2.W)
conn2.W = conn1.W.T
def fit(self, train_data, batch_size, training_epochs, learning_rate):
self.net.fit(train_data, train_data, batch_size, training_epochs, learning_rate)
image = Image.fromarray(
tile_raster_images(X=self.net.connections[0].W.get_value(borrow=True).T,
img_shape=(28, 28), tile_shape=(10, 10),
tile_spacing=(1, 1)))
image.save('test_ae.png')
def fit(self, train_data, batch_size, training_epochs, learning_rate):
NNNet.fit(self, train_data, train_data, batch_size, training_epochs, learning_rate)
image = Image.fromarray(
tile_raster_images(X=self.connections[0].W.get_value(borrow=True).T,
img_shape=(28, 28), tile_shape=(10, 10),
tile_spacing=(1, 1)))
image.save('test_ae1.png')
def fit(self, train_data, batch_size, training_epochs, learning_rate, knowledge=None, l=None, llamda=None):
if l is not None:
self.setRegularization(l, llamda)
self.knowledge=knowledge
NNNet.fit(self, train_data, train_data, batch_size, training_epochs, learning_rate)
image = Image.fromarray(
tile_raster_images(X=self.connections[0].W.get_value(borrow=True).T,
img_shape=(np.sqrt(self.nIn), np.sqrt(self.nIn)), tile_shape=(10, 10),
tile_spacing=(1, 1)))
image.save('test'+self.id+ '.png')
