def __init__(
self,
layer_weight_dims,
layer_output_dims,
filter_size=7,
hidden_dim=100,
num_classes=10,
weight_scale=1e-3,
reg=0.0,
layer_config=[1, 0, 0, 1, 1],
):
"""
Layer Id
0 - Conv_Rect_Pool
1 - Conv_Rect
2 - Conv
3 - Linear_Rect
4 - Linear
Input list = [2,0,0,1,1] - 2 Conv_Rect_Pool layers, 1 Linear_Rect and 1 Linear
"""
self.layer_objs = []
self.layer_output_dims = layer_output_dims
self.layer_weight_dims = layer_weight_dims
assert len(layer_output_dims) == len(layer_weight_dims)
self.num_conv_layers = len(layer_output_dims)
# Construct the Conv layers
for layer_weight_dim in self.layer_weight_dims:
w, b = ConvLayer.generateWeightsAndBias(layer_weight_dim, weight_scale)
conv_obj = Conv_Rect_Pool(w, b, pad=(filter_size - 1) // 2)
self.layer_objs.append(conv_obj)
# Construct the FC layer
w2, b2 = LinearNet.generateWeightsAndBias(np.prod(self.layer_output_dims[-1]), hidden_dim, weight_scale)
lin_obj = Linear_Rect(w2, b2)
# Construct the output layer
w3, b3 = LinearNet.generateWeightsAndBias(hidden_dim, num_classes, weight_scale)
lin_obj_2 = LinearNet(w3, b3)
self.layer_objs.append(lin_obj)
self.layer_objs.append(lin_obj_2)
self.reg = reg
def __init__(self, w, b):
self._fwdcache = None
self.lin_obj = LinearNet(w, b)
class Linear_Rect(BaseLayer, BaseNeuron):
def __init__(self, w, b):
self._fwdcache = None
self.lin_obj = LinearNet(w, b)
@property
def cache(self):
return self._fwdcache
@cache.setter
def cache(self, newvalue):
self._fwdcache = newvalue
@property
def W(self):
return self.lin_obj.W
@W.setter
def W(self, newValue):
self.lin_obj.W = newValue
@property
def b(self):
return self.lin_obj.b
@b.setter
def b(self, newValue):
self.lin_obj.b = newValue
@property
def dw(self):
return self.lin_obj.dw
@dw.setter
def dw(self, newValue):
self.lin_obj.dw = newValue
@property
def db(self):
return self.lin_obj.db
@db.setter
def db(self, newValue):
self.lin_obj.db = newValue
def forward(self, x):
out = self.lin_obj.forward(x) # x is cached inside this object. w,b - Refer to w and b.
self.rect_obj = RectifierLinearUnit() # out is cached inside this object
return self.rect_obj.forward(out)
def backward(self, dout):
dx = self.rect_obj.backward(dout)
out = self.lin_obj.backward(dx)
return out
def generateWeightsAndBias(*args, **kwargs):
return LinearNet.generateWeightsAndBias(*args, **kwargs)
def printW(self):
print(self.W)
def printDimensions(self):
print("W:{}\tb:{}".format(self.lin_obj.W.shape, self.lin_obj.b.shape))
def generateWeightsAndBias(*args, **kwargs):
return LinearNet.generateWeightsAndBias(*args, **kwargs)
def __init__(self, w, b):
self._fwdcache = None
self.lin_obj = LinearNet(w, b)
class Linear_Rect(BaseLayer, BaseNeuron):
def __init__(self, w, b):
self._fwdcache = None
self.lin_obj = LinearNet(w, b)
@property
def cache(self):
return self._fwdcache
@cache.setter
def cache(self, newvalue):
self._fwdcache = newvalue
@property
def W(self):
return self.lin_obj.W
@W.setter
def W(self, newValue):
self.lin_obj.W = newValue
@property
def b(self):
return self.lin_obj.b
@b.setter
def b(self, newValue):
self.lin_obj.b = newValue
@property
def dw(self):
return self.lin_obj.dw
@dw.setter
def dw(self, newValue):
self.lin_obj.dw = newValue
@property
def db(self):
return self.lin_obj.db
@db.setter
def db(self, newValue):
self.lin_obj.db = newValue
def forward(self, x):
out = self.lin_obj.forward(
x) # x is cached inside this object. w,b - Refer to w and b.
self.rect_obj = RectifierLinearUnit(
) # out is cached inside this object
return self.rect_obj.forward(out)
def backward(self, dout):
dx = self.rect_obj.backward(dout)
out = self.lin_obj.backward(dx)
return out
def generateWeightsAndBias(*args, **kwargs):
return LinearNet.generateWeightsAndBias(*args, **kwargs)
def printW(self):
print(self.W)
def printDimensions(self):
print("W:{}\tb:{}".format(self.lin_obj.W.shape, self.lin_obj.b.shape))
def generateWeightsAndBias(*args, **kwargs):
return LinearNet.generateWeightsAndBias(*args, **kwargs)
