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 generateWeightsAndBias(*args, **kwargs):
return ConvLayer.generateWeightsAndBias(*args, **kwargs)
