class Model(object):
def __init__(self, param):
self.c1 = ConvLayer((1, 28, 28), (16, 5, 5), (1, 0), Relu(), param)
# 4x24x24
self.p1 = PoolingLayer(self.c1.output_shape, (
2,
2,
), 2, PoolingTypes.MAX)
# 4x12x12
self.c2 = ConvLayer(self.p1.output_shape, (8, 3, 3), (1, 0), Relu(),
param)
# 4x10x10
self.p2 = PoolingLayer(self.c2.output_shape, (
2,
2,
), 2, PoolingTypes.MAX)
# 4x5x5
self.f1 = FcLayer(self.p2.output_size, 32, Relu(), param)
self.f2 = FcLayer(self.f1.output_size, 10, Softmax(), param)
def forward(self, x):
net = self.c1.forward(x)
net = self.p1.forward(net)
net = self.c2.forward(net)
net = self.p2.forward(net)
net = self.f1.forward(net)
net = self.f2.forward(net)
self.output = net
return self.output
def backward(self, y):
delta = self.output - y
delta = self.f2.backward(delta, LayerIndexFlags.LastLayer)
delta = self.f1.backward(delta, LayerIndexFlags.MiddleLayer)
delta = self.p2.backward(delta, LayerIndexFlags.MiddleLayer)
delta = self.c2.backward(delta, LayerIndexFlags.MiddleLayer)
delta = self.p1.backward(delta, LayerIndexFlags.MiddleLayer)
delta = self.c1.backward(delta, LayerIndexFlags.FirstLayer)
def update(self, learning_rate):
self.c1.update()
self.c2.update()
self.f1.update()
self.f2.update()
def save(self):
self.c1.save_parameters("c1")
self.c2.save_parameters("c2")
self.p1.save_parameters("p1")
self.p2.save_parameters("p2")
self.f1.save_parameters("f1")
self.f2.save_parameters("f2")
def load(self):
self.c1.load_parameters("c1")
self.c2.load_parameters("c2")
self.p1.load_parameters("p1")
self.p2.load_parameters("p2")
self.f1.load_parameters("f1")
self.f2.load_parameters("f2")
class Model(object):
def __init__(self, param):
self.c1 = ConvLayer((1, 28, 28), (4, 5, 5), (1, 0), Relu(), param)
# 4x24x24
self.p1 = PoolingLayer(self.c1.output_shape, (
2,
2,
), 2, PoolingTypes.MAX)
# 4x12x12
self.f1 = FcLayer(self.p1.output_size, 32, Sigmoid(), param)
self.f2 = FcLayer(self.f1.output_size, 10, Softmax(), param)
def forward(self, x):
a_c1 = self.c1.forward(x)
a_p1 = self.p1.forward(a_c1)
a_f1 = self.f1.forward(a_p1)
a_f2 = self.f2.forward(a_f1)
self.output = a_f2
return self.output
def backward(self, y):
delta_in = self.output - y
d_f2 = self.f2.backward(delta_in, LayerIndexFlags.LastLayer)
d_f1 = self.f1.backward(d_f2, LayerIndexFlags.MiddleLayer)
d_p1 = self.p1.backward(d_f1, LayerIndexFlags.MiddleLayer)
d_c1 = self.c1.backward(d_p1, LayerIndexFlags.FirstLayer)
def update(self, learning_rate):
self.c1.update()
self.f1.update()
self.f2.update()
def save(self):
self.c1.save_parameters("c1")
self.p1.save_parameters("p1")
self.f1.save_parameters("f1")
self.f2.save_parameters("f2")
def load(self):
self.c1.load_parameters("c1")
self.p1.load_parameters("p1")
self.f1.load_parameters("f1")
self.f2.load_parameters("f2")