def __init__(self,nIn=700, nHidden=500, corruption_level=0.1,id="", initial_w=None, initial_b=None,input=None, input_type="matrix", output=None, output_type="matrix",cost_function=CrossEntroyCostFunction):
self.corruption_level = corruption_level
self.id=id
self.nIn=nIn
self.nHidden=nHidden
ilayer = DenoisingLayer(numNodes=nIn, ltype = Layer.Layer_Type_Input, id=id+"0", corruption_level=corruption_level)
hlayer = Layer(numNodes=nHidden, ltype = Layer.Layer_Type_Hidden, id=id+"1")
olayer = Layer(numNodes=nIn, ltype = Layer.Layer_Type_Output, id=id+"2")
# declare nnnet
iw = None
ib=None
if initial_w != None:
iw = [initial_w,initial_w.T]
if initial_b != None:
ib = [initial_b, None]
NNNet.__init__(self,[ilayer, hlayer, olayer],initial_w=iw, initial_b=ib, cost_function=cost_function, auto_create_connection=True, input=input, output=output, input_type=input_type, output_type=output_type)
# change parameter constraint
conn1 = self.connections[0]
conn2 = self.connections[1]
self.params.remove(conn2.W)
conn2.W = conn1.W.T
self.y=self.x
self.connect(self.x)
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 __init__(self, layers, size_output_layer=100, activate_function=TanhOutputFunction, cost_function=SquaredErrorCostFunction, input=None, extend_input=None, input_type="matrix", output=None, id=""):
NNNet.__init__(self, layers=layers, cost_function=cost_function, input=input, output=output, input_type=input_type, output_type="vector")
for i in range(len(layers)):
self.layers[i].id = id + str(i)
if layers[len(layers)-1].ltype != Layer.Layer_Type_Output:
output_layer = Layer(numNodes=size_output_layer, ltype = Layer.Layer_Type_Output, id=id+str(len(layers)))
self.layers.append(output_layer)
rng = np.random.RandomState(123)
for i in range(len(self.layers)-1):
c = None
if i <len(self.layers)-2:
c = self.createConnection(self.layers[i],self.layers[i+1], of = activate_function)
else:
c = self.createConnection(self.layers[i],self.layers[i+1], of = SoftmaxOutputFunction, otype=Connection.Output_Type_SoftMax)
self.connections.append(c)
self.params=self.params+c.params
if extend_input is None:
if input_type == "matrix":
self.x_extend = T.matrix(name='extend_input')
if input_type == "vector":
self.x = T.vector(name='extend_input')
else:
self.x_extend = extend_input
self.layers[len(layers)-2].extend=self.x_extend
self.connect(self.x)
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')
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
NNNet.__init__(self, [ilayer, hlayer, olayer], cost_function=CrossEntroyCostFunction, auto_create_connection=True)
# change parameter constraint
conn1 = self.connections[0]
conn2 = self.connections[1]
self.params.remove(conn2.W)
conn2.W = conn1.W.T
self.connect(self.x)
