def __init__(self, w, b, stride=1, pad=1, ph=2, pw=2, pstride=2):
self._fwdcache = None
self.stride = stride
self.pad = pad
self.ph = ph
self.pw = pw
self.pstride = pstride
self._dw = None
self._db = None
self.conv_obj = FastConvLayer(w, b, stride, pad)
self.rect_obj = RectifierLinearUnit()
self.pool_obj = PoolingLayer()
class Conv_Rect_Pool(BaseLayer, BaseNeuron):
def __init__(self, w, b, stride=1, pad=1, ph=2, pw=2, pstride=2):
self._fwdcache = None
self.stride = stride
self.pad = pad
self.ph = ph
self.pw = pw
self.pstride = pstride
self._dw = None
self._db = None
self.conv_obj = FastConvLayer(w, b, stride, pad)
self.rect_obj = RectifierLinearUnit()
self.pool_obj = PoolingLayer()
@property
def W(self):
return self.conv_obj.W
@W.setter
def W(self, newValue):
self.conv_obj.W = newValue
@property
def b(self):
return self.conv_obj.b
@b.setter
def b(self, newValue):
self.conv_obj.b = newValue
@property
def dw(self):
# return self._dw
return self.conv_obj.dw # Relay to conv obj
@dw.setter
def dw(self, newValue):
self.conv_obj.dw = newValue
# self._dw = newValue #Relay to conv obj
@property
def db(self):
# return self._db
return self.conv_obj.db # Relay to conv obj
@db.setter
def db(self, newValue):
# self._db = newValue
self.conv_obj.db = newValue # Relay to conv obj
@property
def cache(self):
return self._fwdcache
@cache.setter
def cache(self, newvalue):
self._fwdcache = newvalue
def forward(self, x):
out = self.conv_obj.forward(x)
out = self.rect_obj.forward(out)
out = self.pool_obj.forward(out)
return out
def backward(self, dout):
ds = self.pool_obj.backward(dout)
da = self.rect_obj.backward(ds)
dx = self.conv_obj.backward(da)
return dx
def generateWeightsAndBias(*args, **kwargs):
return ConvLayer.generateWeightsAndBias(*args, **kwargs)
def getDownsampledHeight(self, H):
return (H - self.ph) / self.pstride + 1
def getDownsampledWidth(self, W):
return (W - self.pw) / self.pstride + 1
def printDimensions(self):
print("W:{}\tb:{}".format(self.W.shape, self.b.shape))